Health Monitoring Systems-An Enabling Technology For Patient Care (R Gupta and D Biswas, CRC Press, 2020)
Health Monitoring Systems-An Enabling Technology For Patient Care (R Gupta and D Biswas, CRC Press, 2020)
Health Monitoring Systems-An Enabling Technology For Patient Care (R Gupta and D Biswas, CRC Press, 2020)
Edited by
Rajarshi Gupta
and
Dwaipayan Biswas
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Foreword........................................................................................................................................ vii
Preface...............................................................................................................................................ix
Acknowledgments..........................................................................................................................xi
Editors............................................................................................................................................ xiii
Contributors....................................................................................................................................xv
v
vi Contents
Index.............................................................................................................................................. 317
Foreword
The field of remote healthcare technologies is not only booming, but it is also vast and
extremely diverse. What could a new book on remote healthcare technology cover which
has not been covered already? Should it concern new diagnostic or therapeutic technolo-
gies? Should it focus on acute or chronic disease applications? Should it be disease specific
or disease agnostic? Should it focus on technologies for the patient or for the doctor? Should
it cover early innovations or stick to validated technologies with regulatory approval? The
good news is, it does not matter what the exact answers are to the above questions; there is
value in sharing knowledge and sharing challenges with a broader community in all the
above permutations.
This book looks at the remote healthcare technologies space from an engineering per-
spective and particularly focuses on aspects related to the gathering and processing of
data: data compression, signal analysis, and security. These are essential ingredients to the
application of digital healthcare services in the real world. By covering a few use cases in
the cardiovascular and metabolic domain, this book will also satisfy the reader interested
in applications of new technology.
vii
Preface
Healthcare practices have undergone a sea change over the last few decades. Primarily
this is driven by a general awareness among people for enjoying a good quality of life.
In the United States, healthcare has been identified as one of the top areas of priority.
According to World health Organization (WHO) statistics, the per capita total expenditure
on healthcare in the USA has increased from US$6000 in 2005 to nearly US$9900 in 2016.
The growing interest on healthcare and its peripheral services has given a high impetus
to the research and contribution in multidisciplinary areas of science and technology.
Undoubtedly, healthcare is one of the fastest growing areas of research and business in
the current century. However, in spite of the tremendous growth of information and com-
munication technology (ICT), embedded technology, computer engineering, sensing tech-
nology, biomedicine, and biotechnology in last century, the level of mortality rates due to
illness has been a serious issue of concern for policy makers. For example, cardiovascular
diseases (CVDs) contribute nearly 20% of the total annual mortality rates each year in the
USA. For a developing nation like India, the figure is slightly higher at 22%. Healthcare
scientists and policy makers attribute two prime factors for this. The first is inadequate
doctor to patient ratio. The second is aggravation of minor health problems due to a lack of
early detection and treatment. ICT has played a pivotal role in healthcare in the integration
of healthcare components, their interoperability, and maintaining healthcare information
records. The practice of health monitoring of remote patients was initiated in early the
1960s. Since then, development of embedded technology, communication and network-
ing protocols, and computing has revolutionized the practice of health monitoring over
the decades. From point-to-point communication based on a fixed station, we are now
in the era of e-healthcare, a general term commonly used to collectively define a host of
services. Innovations in healthcare research have been driven by some societal demands,
viz., mobility of the caregiver (physician); wearable technologies for monitoring; use of
handheld devices (mobile phone, PDAs); and technological developments in microelec-
tronics, embedded systems, networking, and communications.
Health monitoring is contributed to by various specialized applications like telehealth,
telecare, telemedicine, etc. Telehealth refers to the use of technology for connecting with
a patient who is situated at a remote location. Telecare uses information and communi-
cation technology to monitor the vital signs of a patient and detects pathological condi-
tions from the acquired signals for therapeutic actions. Telemedicine uses audiovisual
consultation between two physicians for the treatment of a remote patient. Many modern
health monitoring systems are enabled with Internet-of-Things (IoT) technology, which
has paved the path for pervasive and ubiquitous monitoring and computing, resulting in
strong cooperation between the various components of the complex healthcare setting. As
a result, present-day health monitoring technology enables multi-patient monitoring using
wearable sensors integrated with a wireless communication framework. On one hand,
this facilitates ubiquitous monitoring of patients but, on the other, has demanded system-
level requirements like managing huge medical data, preserving the security of data, and
privacy of personal data.
The objective of this book is to provide a comprehensive discussion on different aspects
of health monitoring systems. While discussing the basic technological areas related to the
domain, it also focuses on the research carried out in pertinent areas and future research
ix
x Preface
directions and challenges. Common medical signals used for cardiovascular and respira-
tion measurement, such as electrocardiogram (ECG), photoplethysmogram (PPG or SpO2),
electroencephalogram (EEG), respiration, and blood pressure, have been considered for
illustrations of circuits, algorithms, and systems in the various chapters of this book.
Although the chapters are standalone in nature, detailed discussion on basic aspects of
biomedical instrumentation, circuit analysis, signal processing concepts, soft computing
tools, and communication technology are beyond the scope of this book. Adequate end-of-
chapter references are provided for enthusiastic readers for further reading. We hope that
this book will be useful for undergraduate and postgraduate students of engineering and
medical physics, technicians, practicing engineers, and researchers.
Book Organization
This book discusses the core areas related to remote health monitoring systems, describ-
ing signal acquisition, compression, transmission, and analysis using different c omputing
methods. Privacy and security of data collected/transmitted/processed using wireless
sensor networks (WSN) have been discussed in detail. Examples of health monitoring
applications have been presented to augment the theoretical details.
The book comprises 13 chapters, with focus primarily on cardiovascular monitoring.
Chapter 1 introduces new paradigms of remote health monitoring systems. Chapter 2–9
focus on sensing, processing, pervasive computing, and security associated with remote
health monitoring. Sensing and data acquisition have been discussed in Chapter 2, and
details on compressing the acquired data is mentioned in Chapter 3. Research on IoT-based
health monitoring has been presented in Chapter 4. Chapter 5 lists out state-of-the-art sys-
tems using telemedicine technology. Methods to analyze biomedical signals have been the
focus of Chapter 6, and techniques for pervasive computation in a resource-constrained
ambulant environment are mentioned in Chapter 7. Systems using big data and cloud
computing have been discussed in Chapter 8, and issues related to cybersecurity in WSN
have been detailed in Chapter 9. Chapters 10–13 present four examples of state-of-the-art
health monitoring applications, namely, activity monitoring using wearable sensors; blood
pressure monitoring using PPG and ECG sensors; wireless telecardiology; and diabetes
monitoring.
We sincerely acknowledge the chapter authors for sharing their wide experience and
expertise to enrich the book, and sparing their valuable time to contribute to this book. We
are grateful to the CRC Press/Taylor & Francis Group team for their patience and tireless
effort toward copyediting, typesetting, and publication of this book. We also thank the
readers for selecting this book for advancement of their knowledge and technical skills.
xi
Editors
xiii
Contributors
xv
1
Remote Healthcare Technology: A New Paradigm
Dwaipayan Biswas
IMEC
CONTENTS
1.1 Introduction.............................................................................................................................1
1.2 IoT Applications......................................................................................................................4
1.2.1 Ambient Assisted Living...........................................................................................4
1.2.2 Disease Monitoring....................................................................................................7
1.2.3 Energy Efficient Operation........................................................................................8
1.3 Book Organization..................................................................................................................9
1.4 Future Research Directions................................................................................................. 10
1.4.1 Challenges.................................................................................................................. 10
1.4.2 Wearable Biosensors................................................................................................. 10
References........................................................................................................................................ 11
1.1 Introduction
Increased life expectancy due to better medical facilities in developed nations has also
paradoxically augmented the prevalence of health impairments among ageing popula-
tion. Cardiovascular disease (CVD), neurodegenerative disease, and motor impairments
are some of the most common syndromes affecting majority of the world’s population
[1–5]. The effect of these diseases is not limited only to the elderly, but have also affected
large sections of the society in their middle and young ages, mainly due to the stress
prevalent in current living conditions (unrestricted working hours, eating disorders, etc.).
CVD is one of the major reasons for mortality around the world, representing 31% of
all global deaths in 2015, as reported by the World Health Organization (WHO) [2]. It is
caused by disorders of the heart and blood vessels which result in coronary artery disease
(CAD), heart failure, cardiac arrest, and sudden cardiac death [1]. Dementia, Alzheimer’s
disease, cerebrovascular accident, popularly termed as stroke, are some of the common
neurodegenerative diseases plaguing the elderly population at large [6–9]. Stroke sur-
vivors often suffer from impairments of their various body parts, leading to a reduced
quality of life [10]. Motor impairments are also common among patients suffering from
Parkinson’s disease having a Freezing of Gait (FoG) syndrome and survivors of epileptic
seizures [11]. Treatment, rehabilitation, and care expenses associated with these diseases
have an adverse impact on the financial aspect, in addition to the loss of quality of life
causing societal disruption.
1
2 Health Monitoring Systems
EEG
ECG
Ambulant situation
Blood
pressure
IMU Cloud
(acceleration,
Personal Digital Internet (Wifi, GSM)
gyroscope)
Assistant
Zigbee
PPG Bluetooth
EMG Server
Clinician
FIGURE 1.1
Overview of a typical remote health monitoring system. (Adapted from [30].)
be worth USD 163.2 billion by the year 2020, having a high economic impact across the
world [18]. State-of-the-art IoHT systems exist for ECG [19,20], electroencephalogram (EEG)
[21], diabetes [22], and vital signs including PPG [23], blood oxygenation (SPO2), respira-
tion rate, body temperature, glucometer, galvanic skin response, blood pressure, position
(accelerometer, gyroscope, etc.), and electromyography (EMG) [24–28].
A holistic overview of a typical wireless body area network (BAN) system for remote
healthcare monitoring framework is shown in Figure 1.1. In this figure, the central hub
acts as a gateway and can be used for displaying the vital information on user interface
or transmitting the clinically relevant information to a remote medical center or a clini-
cian [29]. The sensor nodes comprise ECG, EMG, SpO2, galvanic skin response (GSR), etc.
among many other sensors.
The factors significantly contributing toward home-based/remote rehabilitation can be
summarized as follows:
In the following sections, we highlight a few significant state-of-the-art systems that have
transformed the concept of remote health monitoring into reality.
4 Health Monitoring Systems
1.2 IoT Applications
Technological developments such as 5G wireless networks, mobile edge computing (MEC),
cloud computing, have enabled the development of ‘smart healthcare’, gaining attention
from academia, governments, and industry. 5G wireless networks facilitate efficient com-
munication between resource-constrained device, faster data generation and processing as
well as high-quality data transmission to stakeholders [32]. With the growing emergence of
IoT devices, privacy and security of data are also important issues as have been discussed
in [33]. Example systems benefitting from such developments are EcoHealth, a middleware
platform developed for IoT that connects patients, healthcare providers, and devices [34].
It allows data management and aims to simplify and standardize IoT application develop-
ment, addressing issues like interoperability between different devices. Similarly, an IoT-
based network reported in [35] was designed to monitor patients in rural areas and areas
with low population density. Another solution, named U-Healthcare, is based on a mobile
gateway for ubiquitous healthcare systems that collects data, processes it, and stores it in
the cloud for remote access [36].
IoHT applications are targeted toward providing a holistic care for patients and can
mainly be divided into two segments – (a) ambient assisted living (AAL), supporting
elderly or incapacitated patients, in their home environment, providing a safer environ-
ment and increased autonomy toward an active life [37] and (b) systems for monitor-
ing specific diseases. CVD monitoring, movement/location tracking, fall detection, food
intake are some of the key monitoring components which ensure that subjects receive
the proper assistance in ambient environment (i.e. away from clinic/hospital). State-of-
the-art AAL systems, discussed in Section 1.2.1, incorporate variants of these monitoring
components, in association with sensing, processing, storage, and transmission modali-
ties. In addition, there exist IoT systems for tracking specific disease which have also
been discussed in Section 1.2.2. Processing of sensor data for IoHT applications deserves
special attention given the importance of energy efficient operation of battery-operated
sensor nodes, ensuring long-term continuous monitoring, as discussed in Section 1.2.3.
A few state-of-the-art remote health monitoring systems have been listed in Table 1.1 for
further reference.
TABLE 1.1
State-of-the-Art Remote Health Monitoring Systems
Refs. IoHT Service Product Description
[38] AAL system BeClose Remote monitoring system from
Assisted Living Technologies Inc., for
both caregiver and subject.
[39] AAL mobile application Fade Android application for detecting ‘fall’
and generating an alarm.
[40] Mobile application Medisafe Medication reminder
[41] Mobile application OnTrack Managing diabetes
[42] Monitoring system EarlySense Continuously monitors heart rate,
respiratory rate, fall prevention, and
pressure ulcer prevention.
[43] Monitoring system AccuSom Monitors sleep
[44] Monitoring system Proteus Discover System comprises ingestible sensors,
portable sensor patch, mobile
application, manufactured by Proteus
Digital Health Inc. The pill dissolved in
the stomach produces a signal, detected
by a body-worn sensor, which transmits
the data to a mobile application.
[45] Wearable sensor Apple watch Monitors cardiac parameters and
kinematic information
[46] Wearable sensor Enterprise A product from Bittium, providing
customized solution for industrial,
healthcare, and sports device
manufacturers
[47] Wearable sensor QardioCore Monitors cardiac parameters and
transmits information to clinician
[48] Wearable sensor Monica AN24 Clinical and home monitoring system
the elderly population. In this context, a system developed for vital signs monitoring on
elderly people in care homes is described in [51]. Sensors located on the patient’s clothes
collect data used to monitor their physiological parameters. Smart sensory furniture used
for elderly people living alone was proposed in [52]. A new method toward achieving
assistive smart homes based on an intention recognition mechanism incorporated into an
intelligent agent architecture was proposed in [53]. The method provided a web interface
and provided assistance during physical activities. Robotics and home automation was
combined in [54] for AAL. RFID intelligent system was used to identify user–object inter-
actions employing machine learning techniques to enable an AAL system in a retail store.
A system developed in [55] allows AAL systems to identify and predict situations that may
endanger users in their living environment, proposing a complementary and alternative
way to acquire comprehension of a person’s behavior providing this knowledge when cog-
nitive impairments occur. A survey reported in [56] covers signal analysis and processing
techniques employed with different types of sensors, such as pyro-electric infrared, and
vibration sensors, accelerometers, cameras, depth sensors, and microphones, analyzing
the increase of diseases and healthcare costs, shortage of caregivers, and a rise in the num-
ber of individuals unable to live independently.
The prevalence of smartphones and the use of fitness applications have gained immense
popularity, as investigated in [57], presenting information from more than 14,000 cellular
6 Health Monitoring Systems
towers and 4,000 users. This work correlates key factors, such as temporal, location,
mobility, and personal incomes, to the usage of such mobile applications. These findings
are important for developing public policy and city planning. Healthcare solutions using
smartphones has become increasingly common these days. These solutions range from a
simple application to remind the patient to take a medicine or an oximeter. According to
Saúde Business [58], it is estimated that there are more than a 100 million m-health Apps
around the world. A review of using medical apps on smartphones, discussing their limi-
tations and future trends, is presented in [59]. A review of all randomized clinical trials
reporting the effects of psychological interventions delivered via smartphone on symp-
toms of anxiety, observing a reduction in total anxiety scores, is reported in [60]. M-hub,
a system addressing issues of mobility and security in medical environments, uses a
middleware in mobile devices that automatically connects smart objects [61]. Similarly,
H3IoT is a five-layer architectural framework for monitoring health of elderly people [62].
There are some devices specifically built for the AAL market. BeClose monitoring sys-
tem is an example of a solution that combines several sensors with artificial intelligence
approaches providing more freedom to elderly people [63]. MC10 offers a state-of-the-art
body-worn sensor capable of gathering complex physiological data [64]. Bittium’s smart-
watch takes the leading position capable of measuring vital signs, monitoring patient
location, detecting body positioning and medicine dosage, and timing management [46].
As security is necessary in all IoHT solutions, Bittium also offers a solution that ensures
secure data transfer between sensor devices and cloud services, being a company special-
ized in the development of reliable, secure communications and connectivity solutions.
It is also important to mention the Apple Watch as a wearable development platform [45],
allowing a large number of applications for healthcare. It is possible to obtain the body
mass index (BMI), body surface area, estimated glomerular filtration rate (eGFR), and sev-
eral scores used in CVDs. The popularity can be judged from the fact that nearly 6 million
devices were sold in 2016.
Fall is one of the main causes of fatal injury in the elderly population, turning their lives
more dependent on care [65]. The Fade App aims to solve the problem with fall and is
easy to use [39]. A fall detection system for elderly patients is shown [66–68], monitoring
and detecting activities such as walking, running, sitting or standing up, lying down, and
falling. A system based on cloud computing for monitoring Parkinson patients was devel-
oped in [69]. An m-health wearable wireless sensing system for monitoring human motion
disorders was proposed in [70], capable of generating timely rhythmic auditory stimula-
tions to release the gait block during freezing of gait (FoG) episodes in Parkinson’s dis-
ease. Tracking movements of visually impaired people, with an indoor anti-collision alarm
system based on wearable sensors, is proposed in [71]. It identifies the distance between
the RFID tag and antenna, keeping track of the degree at which the user is straying off
the normal path. Information from tri-axial accelerometers and a heart-rate monitor were
used to distinguish different physical activity using supervised machine learning algo-
rithms [72]. A smartphone application was used to acquire, process, and store inertial sen-
sor data and rotation matrices about device position for gait parameters estimation during
daily living [73]. HealtheBrain is an application, proposed in [74], focussing on allowing
the square stepping exercise done by elderly people without cognitive loss. A correlation-
based sleep scheduling mechanism (LCSSM) was proposed in [75] to implement energy
efficient wireless sensor networks in ambient-assisted homes (AAH).
A prediction system, Wanda [76], was developed to detect CVD risk factors, where sub-
jects receive technical support and reinforcement. A patient health monitoring system
integrated with cloud computing and IoT [77] was applied for real-time monitoring of
Remote Healthcare Technology 7
congestive heart failure using ECG. Similarly, a wearable ECG monitoring system ana-
lyzing heart and respiratory rates was proposed in [78]. A smartphone-based real-time
CVD monitoring app was developed in [79] for patients with heart disease, diabetes,
and hypertension and other chronic diseases. Also, a ubiquitous system for monitoring
elderly patients with Alzheimer’s disease is described in [80], enabling the patient to notify
the clinician on vital parameters such as oxygen saturation, blood pressure, and heart
rate. More specific wearable devices are also found in literature, such as the system pro-
posed in [81], enabling real-time data access in the cloud and monitoring blood pressure,
temperature, ECG, and oxygen saturation. Ubiquitous cardiac care (UCC) is a ubiquitous
health monitoring system for cardiac arrhythmias detection [82], where data is captured
using wearable ECG sensors and sent to cardiologists for analysis. An ECG remote moni-
toring system dedicated to long-term residential health monitoring integrated with IoT
was proposed in [83]. Majority of the systems presented here are capable of measuring
vital parameters and ensuring a general well-being (also termed as ‘wellness monitoring’)
of patients as well as proactively notifying respective clinicians in times of emergency.
1.2.2 Disease Monitoring
In this section, health monitoring systems, not necessarily restricted to the home and
living environment, but targeting specific diseases, which are key toward societal well-
being, have been discussed. A 5G-Smart Diabetes monitoring system has been proposed
in [84], providing communication infrastructure for continuous physiological monitoring
of diabetic patients. It targets achieving a cost-effective solution toward personalized
monitoring. The number of people with diabetes has risen from 108 m illion (in 1980)
to 422 million in 2014. According to the World Health Organization, these n umbers are
likely to increase considerably [4]. One good solution is OnTrack Diabetes [85], an appli-
cation for people with type-2 diabetes. It helps users manage their conditions by per-
forming blood glucose, blood pressure, exercise, food, medication, pulse, and weight
checks. An approach described in [86] enables real-time location monitoring system for
elderly people, ensuring patient safety, with respect to drug compliance in diabetes ther-
apy management.
Assessing voice disorders using deep learned features of voice signals and their cor-
responding treatments is proposed in [87]. The processing, assessment, and treatment are
managed by an application management system in the cloud, an edge computing (EC)
application platform management system. An embedded system measuring blood flow
with low-cost technology is described in [88]. A remote body pressure monitoring system
is proposed in [89]. Self-monitoring of blood pressure for pregnant women was demon-
strated in [90].
An integrated system design for monitoring of dementia patients has been proposed in
[91]. It allows data collection from different sensors placed in a house and transmission
over the internet. A smartphone-based accelerometer is used to quantify spatiotemporal
gait parameters when attached to the body or in a bag, belt, hand, and pocket, a llowing
detection of behavior changes among patients diagnosed with bipolar disorder [92].
A smartphone-based AAL solution has been demonstrated in [93], supporting diagnosis,
clinical communication, providing drug references or medical education.
An automatic detection of chronic wounds (using color and size as features) was
proposed in [94], using image analysis and machine learning, developed as a mobile appli-
cation. A StripTest reader was proposed in [95], a smartphone interpreter of biochemical
tests based on paper-based strip color using the phone camera for acquisition, processing
8 Health Monitoring Systems
the images within the phone and producing comparable results with respect to gold stan-
dard. Skin monitoring and automatic detection of melanoma, using smartphones, showed
that an early intervention at home could successfully treat this disease [96]. Girl Talk, a free
smartphone app, provides comprehensive sexual health information, particularly target-
ing teenage girls [97]. A method to detect and monitor the outbreak of chikungunya virus
based on IoT and fog computing was proposed in [98], diagnosing infected users and gen-
erating alerts during emergency.
The characteristics of wearable applications for IoT scenarios was analyzed in [99],
describing the interaction between wearable or mobile devices and smart objects, and a
Web of Things application for evaluating interaction patterns in a smart environment was
presented. In USA, 15% of the healthcare consumers use wearable devices, such as smart-
watches and fitness devices. It is predicted that 110 million of wearable devices will be sold
by 2018–2019 [100]. The goal is always to make the user as comfortable as possible in order
to provide quality healthcare. Therefore, the main aspects taken into consideration to ana-
lyze the previously presented wearable solutions were patient’s comfort, data security, and
usability. IoT is bringing innovations to healthcare, resulting in new companies taking
interest, thereby helping to create an impact on product development and marketing.
1.3 Book Organization
In this book, we have primarily focussed on wearable sensing platforms for cardiovas-
cular monitoring. We have presented an in-depth discussion on telemedicine technolo-
gies, sensing methodology, IoT infrastructure for health monitoring, signal processing
techniques, pervasive computing, cybersecurity solutions, and lastly various state-of-the-
art health monitoring example applications. Apart from the Introduction chapter, the book
comprises 12 chapters, of which Chapters 2–9 focus on the theoretical aspects of sensing,
processing, pervasive computing, and security associated with remote health monitor-
ing. Chapters 10–13 present four key health applications which further compliment the
methodological details presented in the previous eight chapters.
Chapter 2 presents an overview on medical sensing and associated data acquisition,
focussing on sensors for cardiovascular (ECG, BP, phonocardiogram (PCG), PPG), respira-
tory (respiratory rate), nerve (EEG), and motor activity (EMG, bioimpedance) monitoring.
Chapter 3 presents details on medical data compression, a key component in terms of
the huge amount of data generated by smart sensing platforms and impending storage
constraints. The compression modalities have been explained with respect to ECG and
PPG signals.
The technological standards, challenges, and open research areas in IoT-based health
monitoring have been discussed in Chapter 4. In particular, low-energy implementation,
signal processing and machine learning challenges, communication constraints, and secu-
rity concerns have been reviewed. State-of-the-art systems using telemedicine technology
have been listed down in Chapter 5. Signal processing is a key aspect in biomedical appli-
cations, and hence the most popular time- or frequency-domain processing methodolo-
gies and machine learning techniques have been discussed in Chapter 6. The discussion
is further substantiated by two illustrative use-case applications on prosthetic controller
using EMG signals and a brain–computer interface speller application controlled by ECG.
Chapter 7 presents a detailed discussion on pervasive computation for CVD monitoring,
in particular focussing on an important research area of lead reconstruction for ambulant
ECG monitoring. The chapter demonstrates solutions for reconstructing a 12-lead ECG
system which is suitable for clinical diagnosis, from a two- to three-lead pervasive ECG
recording setup.
Chapter 8 revisits major technological advances behind novel e-health and m-health ser-
vices (both consumer-grade and medical-grade), with emphasis on big data and cloud
computing. The role of IoT in healthcare is analyzed, particularly exploring the role of
blockchain in health information sharing. The best practices for building modern, user-
friendly health-related mobile applications are also summarized herein. In Chapter 9, the
major driving force behind healthcare digitization is discussed, namely, the role of trust
and cybersecurity. Key enabling technologies and approaches for secure and trusted appli-
cations such as homomorphic encryption, blockchain-based cybersecurity solutions, and
sandboxing are presented in detail. Four key applications – human activity monitoring
using wearable sensors, blood pressure monitoring using PPG and ECG measurements,
wireless telecardiology, and lastly, diabetes monitoring – have been presented in Chapters
10, 11, 12, and 13, respectively.
We hope this book will act as a launching platform for readers interested in biomedical
instrumentation and signal processing, presenting a holistic overview on sensing, pro-
cessing, security, and state-of-the-art knowledge on key health monitoring applications.
10 Health Monitoring Systems
1.4.1 Challenges
With majority gadgets connected to the internet, demands on a finite bandwidth are
rapidly straining the system. By the end of 2014, global mobile-data traffic reached 2.5
exabytes (2.5 billion gigabytes) per month according to the networking-technology com-
pany Cisco Systems. Approximately, the 100 million odd wearable devices were generat-
ing 15 million gigabytes of monthly traffic on what is a physically finite portion of the
electromagnetic spectrum, with their number expected to increase fivefold by 2019 [108].
Moreover, there will be occasions for gridlock, with an increased use of headsets which
deliver data-hungry virtual and augmented reality experiences. All these devices clog up
the airwaves, impairing performance and threatening essential internet traffic. Probable
solutions include making efficient use of the spectrum; optimizing antenna designs to
reduce interference and power consumption; and transforming wireless communications
into the visible-light realm using light-emitting diodes (LEDs), acting as photoreceptors,
to communicate either between wearables or directly to the internet. Cognitive radios
is one such solution, which enables smart use of communication channels, identifying
unused bandwidth regions, and opportunistically using them to speed up communication
[109]. Optimum potential can be reached, when devices use a licensed frequency to com-
municate, and then it drops off the spectrum when someone with higher priority enters.
Although techniques based on this principle have been used for decades, cognitive radio
will make sharing available spectrum among devices more efficient.
Wearable sensors generate a wealth of personal data, causing data privacy concerns
among potential users. Encryption is an effective way to deal with it; however, it is not
always used in low-cost wearable devices. SensCrypt, an encryption protocol designed
specifically for low-energy fitness trackers that reduces communications costs, is in prac-
tice [110]. There have been efforts to improve security of mobile and wearable devices by
equipping them with biometrics such as fingerprint readers and iris scanners [111,112].
However, high-resolution cameras have been shown to be capable of capturing iris infor-
mation from a distance and capture fingerprint using a phone’s camera. Robust encryption
mechanisms need to be used, which include heartbeat patterns, or other physiological
signals such as DNA, paired with sensor platforms, to enhance security measures.
1.4.2 Wearable Biosensors
The face of wearable devices has changed rapidly in recent years, with researchers branch-
ing out from tracking physical activities for healthcare applications to the development
of biosensors. These devices incorporate a biological recognition element into sensing
Remote Healthcare Technology 11
FIGURE 1.2
Generic overview of biosensing components. (Adapted from [113].)
mechanism (for example, enzyme, antibody, cell receptor). The potential utility of wearable
biosensors is evident from the large number of reported studies. A typical biosensor com-
prises (a) bioreceptor (for example, enzyme, antibody) responsible for selective recognition
of the target analyte and (b) physico-chemical transducer (for example, electrochemical,
optical, or mechanical) which translates the biorecognition event into a signal of interest,
as shown in Figure 1.2 [113]. These devices have been used in point-of-care clinical/home
settings (for example, test strips for blood glucose measurement). These wearable sensors
allow noninvasive chemical analysis of biofluids, such as sweat, saliva, or interstitial fluid
(ISF), which fall under the category of epidermal wearable biosensors, or tears which rep-
resent ocular wearable biosensors. They hold promise due to their high specificity, speed,
portability, and low-cost and low-power requirements. Epidermal devices rely on sweat or
ISF sampling at the skin surface, along with transport of these biofluids over the biosensor
surface. Biomarker molecules in tears diffuse directly from the blood and exhibit close cor-
relation between tear and blood concentrations and present an opportunity for diagnosis
of ocular disease.
The wide acceptance of such wearable biosensing technology requires a deep under-
standing of the biochemical composition of bodily fluids, such as sweat or tears, and its
relation to blood chemistry. Wearable monitoring platforms can lend insights into dynamic
biochemical processes in these biofluids by enabling continuous, real-time monitoring of
biomarkers that can be related to a wearer’s health and performance. Such real-time moni-
toring can provide information on wellness and enhance the management of chronic dis-
eases by acting as a proactive mechanism for disease detection. They obviate painful and
risky blood sampling procedures and can be readily blended with a wearer’s daily rou-
tine. Multidisciplinary development of new biosensing technologies has led to numerous
proof-of concept demonstrations and has driven growing efforts toward the commercial-
ization of wearable sensors. However, these products still require further large-scale vali-
dation studies. Minimally invasive glucose monitoring devices is one such success story
which has attracted a large-scale commercial interest. Some of the popular commercial
prototypes are (a) smart contact lens from Google and Novartis, measuring glucose in
tears [114,115], and (b) GlucoWatch, Freestyle Libre (in the form of a patch) [116], Eversense
(subcutaneous implant), all measuring glucose in ISF [117].
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2
Biomedical Sensors and Data Acquisition
Rajarshi Gupta
University of Calcutta
CONTENTS
2.1 General Principles of Biomedical Measurements............................................................ 19
2.2 Bioelectric Phenomena and Biopotential Electrodes....................................................... 20
2.3 Origin and Measurements of Cardiovascular and Respiration Signals....................... 24
2.3.1 ECG, PPG, Respiration, Blood Pressure, and PCG............................................... 24
2.3.2 Photoplethysmography (PPG) and Pulse Oximetry............................................ 31
2.3.3 Respiration Signal Sensing...................................................................................... 35
2.3.4 Phonocardiogram (PCG) Signal............................................................................. 37
2.3.4.1 PCG Signal Recording............................................................................... 39
2.3.5 BP Measurement....................................................................................................... 40
2.4 Muscle and Nervous System Activity...............................................................................44
2.4.1 Electroencephalogram (EEG)..................................................................................44
2.4.1.1 EEG Signal Conditioning.......................................................................... 45
2.4.2 EMG Signal................................................................................................................ 47
2.5 Galvanic Skin Response....................................................................................................... 49
2.5.1 Measurement of Bioimpedance.............................................................................. 49
2.6 Smart Biomedical Sensors and Data Acquisition Technology....................................... 51
2.7 Conclusion............................................................................................................................. 53
References........................................................................................................................................54
19
20 Health Monitoring Systems
Computer for
Cables measurement,
interpretation and
storage
FIGURE 2.1
Basic schematic of a biomedical measurement system.
developed to collect the endogenous and exogenous signals for diagnostic actions. The gen-
eral structure and measurement principle of an advanced biomedical measurement system
can be shown in Figure 2.1.
The biomedical signals are picked up through the metal electrodes, and cables connect
them to the biomedical equipment (BME). The first stage of the BME is an electronic cir-
cuit which is primarily responsible for refinement of the weak biopotential. This function
includes noise reduction, amplification, scaling, and patient isolation, collectively known
as signal conditioning. This emphasizes (boosts up) the clinical information from the
weak biopotential, discarding the non-relevant information. The next stage is discretiza-
tion of the continuous domain signal into discrete samples and their representation using
finite bit-lengths using analog to digital converter (ADC). A microcontroller transfers the
quantized samples to a computer which is the final data storage, interpretation, and mea-
surement device. Often, for standalone biomedical instruments, a low- to medium-end
processor is provided for elementary signal analysis and direct digital readout of the phys-
iological parameter. As such, these principles are very similar to any typical process mea-
surement system with the additional constraints of maintaining the safety of the human
subject (patient) from electrical, radiation power levels, and other toxic hazards.
Over the last three decades or more, the development of various signal processing algo-
rithms has enabled computerized measurement and interpretation of human physiological
processes, equally correct and consistent with that of a medical doctor. As a result, in recent
years, the tendency to use computers in hospital setting for automated measurement and
analysis of medical signals has been increased for rapid processing of high volume biomedi-
cal data collected from a number of patients, with the additional facility of data archival and
sharing with authorized users. The scope of this chapter, however, is to provide a brief over-
view of origin, sensing, and interfacing principles of some prime biomedical signals which
are related to cardiovascular, respiratory, and nervous system of human physiologic system.
generation of small electric potentials is due to the fact these cells are polarized in normal
condition due to distribution of Na+, K+, Ca++, Cl− ions in the intra- and extracellular fluids
and semi-permeable nature of the cell membrane. This potential, named resting mem-
brane potential (RMP) of most human living cells, is around −20 to −70 mV while measured
inside to outside of the cell membrane. The steady-state RMP is expressed by Goldman–
Hodgkin–Katz equation [3]:
RT Na i PNa+ + K i PK + + Cl i PCl−
+ + −
Vmo = − , (2.1)
F Na+ PNa+ + K + PK + + Cl − PCl−
e e e
where T is the Kelvin temperature, R is the gas constant [8.314 J/(mol K)], F is the Faraday
number, 96,500 Cb/mol, and PX is the permeability for ion species X.
This ‘polarized’ behavior cell of the cell undergoes a reversible process due to the appli-
cation of short duration external stimulus. The semi-permeable nature of the cell mem-
brane is governed by a Na–K pump theory, which describes the transfer of Na+ and K+
ions between intra- and extracellular fluids through the membrane. This makes the cell
potential positive for sometime, called ‘depolarization’ of the cell. After the stimulus is
withdrawn, it slowly recovers to the initial polarized state (negative polarity) through a
slow ‘repolarization’ phase. The recording of the membrane potential during these revers-
ible changes is called AP of the cell. Each type of excitable living cells have their specific
signatures (amplitude, morphology, and frequency character). Figure 2.2 shows the typical
waveform morphology of typical AP of a human cell. As soon as the external stimulus is
applied, the cell membrane potential rapidly changes to slightly positive state, indicating
depolarization (time duration D). When the stimulus is withdrawn, the membrane poten-
tial slowly recovers to the repolarized state (time duration R), after which some afterpoten-
tials (state A) may be observed.
The recording of any physiological signal is the collective representation of these tiny
APs resulted from a group of tissues over space and time at predefined body location(s).
Sensing of small bioelectric potentials involve interaction with the ions and convert
them suitably into an electric current using electrodes and instrumentation electron-
ics. Metal electrodes of different configurations are used for measuring medical sig-
nals. Very often, an electrode gel is used for better contact and chemical stability of the
D R
A
Legends:
External D: depolarization
stimuli R: Repolarization
A: Afterpotentials
Amplitude (in mV)
+
0
Time
RMP
FIGURE 2.2
Typical AP waveform of a human excitable living cell.
22 Health Monitoring Systems
Conductive
adhesive polymer
(a)
(b)
(d)
(c)
Lead
(f)
(e)
Gel
Electrolytic Double sided Ag/AgCl
gel in recess adhesive tape
FIGURE 2.3
Common biopotential electrodes used in clinical setting: (a–c) metal electrodes; (d and e) disposable electrode;
(f) reusable Ag/AgCl electrode.
Conductive
rubber
(a) Pin
Connector
(b)
Lead wire
Hub
Insulation (c)
Sharp metallic Central Electrode
point
FIGURE 2.4
Some specialized biomedical electrodes: (a) basic recessed electrode; (b) flexible electrode; and (c) needle
electrode.
separated from the skin by just a thin layer of electrolyte. Figure 2.3f shows another reus-
able silver–silver chloride electrode attached to the skin by non-allergic adhesive tape and
provides high-quality measurement.
Some special electrode configurations used in patient monitoring are shown in
Figure 2.4. A recessed electrode basic configuration is shown in Figure 2.4a. The electro-
lyte layer now consists of a sponge saturated with a thickened electrolytic solution. The
sponge serves the same function as the recess in the cup electrodes and is coupled directly
to a silver–silver chloride electrode. For neonatal monitoring, flexible electrodes, as shown
in Figure 2.4b, are used. They are basically similar to the metal plate electrodes. A carbon-
filled silicon rubber compound is used as the active material. The other one is a Mylar
electrode, consisting of a 13 µm thick Mylar film, on which Ag and AgCl is deposited. The
lead wire is attached to the Mylar substrate through a conductive adhesive. A thin layer of
Ag is deposited on the adhesive layer and the Mylar. The additional advantages of flexible
electrodes are being penetrable by X-rays and relief from repeated use and detachment
for clinical tests. A second group of special electrodes are used to collect the biopotential
from within the body tissues. These do not require electrolytic gel and can be broadly dis-
cussed under the two heads, viz., needle electrodes and microelectrodes. The basic needle
electrode, as shown in Figure 2.4c, consists of a sharp stainless steel pointed needle, with
the shank insulated with a coating. When this structure is placed in tissue such as skeletal
muscle, electrical signals can be picked up by the exposed tip. One can also make needle
electrodes by running one or more insulated wires down the lumen of a standard hypo-
dermic needle.
For EEG measurements, special types of electrodes are required since these are to be placed
on the cortex. The standard electrodes consist of a metal plate covering an area of a few square
millimeters. The electrolyte, normally a saline solution, is either contained in liquid form in
a pad wrapping the electrode or is bound in a gel or paste filling the gap between electrode
and skin. Different methods of fixing the electrodes on the cortex are available: a grid of rub-
ber strips, special caps with the electrodes being integrated into the cap textile, gluing using
24 Health Monitoring Systems
(a) (b)
FIGURE 2.5
Electrodes used for EEG measurements: (a) metal electrodes; (b) electrodes fitted in a cap for automated
positioning.
a collodium–acetone solution, paste (i.e., the paste providing the electrolyte also serves as a
fixation aide). Ag/AgCl electrodes (silver electrodes coated with a thin silver chloride layer)
are the current standard for routine applications. The chloride layer prevents polarization. A
special variant is made by sintering a mixed powder of Ag/AgCl onto the electrode rather
than applying it as a discrete metal coating. This electrode requires less maintenance because,
with conventional Ag/AgCl electrodes, the AgCl layer is easily eroded with regular use.
Figure 2.5a shows metal electrodes used for EEG measurements. For specialized appli-
cations like ICU monitoring, needle electrodes made of refined steel are pierced into the
skin. For regular use, modern electrode arrangements using caps (called ‘electrocaps’) are
available (lower) where all the electrodes are automatically placed at their right position
once the cap is applied.
SA node
AV node P-wave
Bundle of His
FIGURE 2.6
(a) A typical ECG waveform; (b) associated events with heart chambers
called bipolar leads of Einthoven, and the other three are unipolar leads. The remaining six
are collected from the transverse plane and are called precordial or chest leads. The standard
leads provide bipolar measurements, designated as I, II, and III, indicating differential mea-
surements between a pair of limb positions. The remaining three unipolar leads from fron-
tal plane measurements are unipolar measurements, designated as aVR, aVL, and aVF, also
named augmented limb leads. These potentials are measured with reference to a common
point, Wilson Central Terminal (WCT), which also provides reference for six chest leads, V1,
V2, V3, V4, V5, and V6. The lead positions are shown in Figure 2.7 and detailed in Table 2.1.
Most of the clinical information in ECG is available in the frequency band of
0.05–100 Hz. However, there are various artifacts which corrupt the ECG, few of which
have overlapping spectra with the ECG. The major artifacts in ECG are electromyography
(EMG) noises, power line interference (PLI), electrode pop or contact noise, baseline wan-
der, motion artifact, electrosurgical noise, and amplifier noise [5,6]. The noise and artifacts
of ECG are briefly described below:
1. EMG noise: Picked up due to muscular activity of the subject during the ECG
collection. Their amplitude is in the range 0.1–1 mV, and frequency 5 Hz–1 kHz is
partly overlapping with that of ECG signal. EMG noise, if not properly taken care
of, may completely destroy the signal in morphology-based analysis. For short
duration clinical testing, the patient is advised to lie on resting condition so as
to minimize the noise. However, for long duration Holter monitoring, this EMG
noise is unavoidable.
FIGURE 2.7
ECG lead conventions and body positions.
26 Health Monitoring Systems
TABLE 2.1
ECG Lead Positions
Lead Name Exploring/Measuring Electrode Reference
I Left arm Right arm
II Left leg Right arm
III Left leg Right arm
aVL Left arm Wilson central terminal
aVR Right arm Wilson central terminal
aVF Left leg Wilson central terminal
V1 Right fourth intercostal space Wilson central terminal
V2 Left fourth intercostal space Wilson central terminal
V3 Halfway between V2 and V4 Wilson central terminal
V4 Left fifth intercostal space, midclavicular line Wilson central terminal
V5 Horizontal to V4, anterior axillary line Wilson central terminal
V6 Horizontal to V5, midaxillary line Wilson central terminal
2. Power line interference (PLI): These are picked up on the lead wires of neigh-
boring power cables, due to capacitive coupling with ECG lead wires [7]. So, a
50/60 Hz ± 0.2 Hz current flows through the lead wires to the ground through
the patient’s body. Sometimes, the equivalent voltage drop, which appears as a
common-mode signal to the input of the ECG amplifier can be as high as 20 mV,
which is itself many times greater than the maximum ECG amplitude. Driven
right leg (DRL) circuit, as shown later in Figure 2.10, can minimize the PLI effect.
3. Electrode pop or contact noise: During the collection of ECG, loss of contact
between the patient’s skin and electrode may cause a temporary saturation of the
amplifier output for certain period of time.
4. Baseline wander: The lung volume change due to the respiration of the patient
changes the impedance between the heart muscle and electrode. This causes base-
line (isoelectric segments) of the ECG to oscillate at a very low frequency drifting
between 0.15 and 0.3 Hz.
5. Motion artifacts: Due to improper “preparation” of the skin for electrode place-
ment, or patient movement, a slow movement of the electrodes may occur in long-
term recording using wearable sensors. Motion artifact has a significant overlap
with ECG signal spectrum in the range 1–10 Hz. This results in an abrupt baseline
jump or complete saturation of amplifier output for 0.5 s.
6. Electrosurgical noise: This is the noise generated by neighboring medical equip-
ment in the clinical set-up at frequencies between 100 kHz and 1 MHz.
7. Amplifier noise: Noise and drift are two unwanted signals that are generated
within the amplifier that contaminate a biopotential signal under measurement.
‘Noise’ generally refers to undesirable signals with spectral components above
0.1 Hz, while ‘drift’ generally refers to slow changes in the baseline at frequencies
below 0.1 Hz. The noise and drift are measured either in microvolts peak to peak
(μVp–p) or microvolts root mean square (RMS) (μVRMS) and applies as if they were
applied as differential input voltage.
These artifacts can be minimized by suitable designs and clinical set-up; however, it is
impossible to completely eliminate those altogether using hardware designs. An ECG
Biomedical Sensors and Data Acquisition 27
amplifier must be able to minimize these external interferences and faithfully amplify the
potentials due to cardiac systole and diastole only. Additionally, the design should also
ensure patient safety arising out of accidental direct electric current flow through the car-
diac muscle. The basic specifications of an ECG amplifier is given below:
A
CMRR (dB) = 20log 10 d , (2.3)
Acm
where Acm stands for gain for common-mode input and Ad stands for gain for
differential input. Most biopotential amplifiers offer a typical CMRR of 120–140.
A novel, balanced AC coupled differential amplifier with AC coupled input stage
and a third stage providing ground path using a third (common) electrode is
described in [8]. The CMRR of ECG amplifier greatly depends on matching of the
resistance pairs in the instrumentation amplifier (INA) block, which, in practical
circuits, is difficult to achieve. This results in an amplification of the DC offset
voltage, mainly caused due to electrode–skin impedance and PLI. In [9], a design
approach is proposed which does not rely on matching of resistors. It includes a
fully differential DC suppression circuit in addition to AC coupling for full res-
toration of DC biopotential signals. The power line frequency (and its harmon-
ics) induces stray capacitive coupling with the patient’s body and lead wires. The
order of these capacitances is 60 pfd, which corresponds to an impedance of 64
kΩ at 50 Hz. The leakage current produces a voltage drop w.r.t. ground, which
appears as common-mode signal to the first stage of the ECG amplifier. To mini-
mize this interference effect, the CMR plays a vital role in rejecting this noise.
4. Recovery: Sudden movement of the patient during ECG procedure can saturate
the amplifier output due to high amplitude input transient pulses. After a small
interval, called ‘recovery time’, the amplifier slowly comes back to the normal
condition.
5. Input impedance: This should be sufficiently high so as to ensure that input signal
is not attenuated. Most common ECG amplifiers offer an input impedance of 10 MΩ.
The schematic of a typical ECG signal conditioning circuit to generate a uni-
polar voltage is shown in Figure 2.8. The lead selector circuit can be manually
operated or controlled by a microcontroller in simultaneous or sequential mode.
The objective of the isolation circuit is to provide galvanic isolation (1,000 MΩ or
more) between the patient’s body and the signal conditioning to prevent any acci-
dental current flowing through the patient’s body. The total gain of the amplifier,
28 Health Monitoring Systems
Electrode 1 Lead
Isolation Final analog
selector Filter INA
circuit output
Circuit
Electrode 2 Pre-
(Reference) Amplifier Ref Level
Control Voltage shifter
FIGURE 2.8
Block schematic shows components of a typical ECG amplifier. (Printed with permissions from “Biomedical
sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer
Nature, 2017.)
FIGURE 2.9
An ECG amplifier circuit using INA.
distributed over a preamplifier and an INA, is around 2,000. In the first stage, a
preamplifier provides a low gain, typically 10–20. The filter circuit mainly dis-
cards high-frequency noise and passes the clinical bandwidth of 0.05–100 Hz to
the INA. The INA provides a gain of 100–200. After INA, the final level shifter is
used to get a unipolar voltage of 0–5 V.
The main component of the ECG signal component is the INA, which should
provide a high CMR and amplify the weak ECG signal. The first stage (two op-
amps A1 and A2) of the circuit shown in Figure 2.9 represents a standard INA,
which provides high differential gain and unity common-mode gain without the
requirement of close resistor matching. The differential output from A1 an A2
represents a signal with substantial relative reduction of the common-mode signal
and is used to drive a standard differential amplifier (op-amp A3) which further
reduces the common-mode signal. CMRR of the output op-amp as well as resis-
tor matching in its circuit are less critical than in the another variant of INA, the
follower type. Offset trimming for the whole circuit is done at one of the input
op-amps. Complete INA integrated circuits based on this standard INA configu-
ration are available from several manufacturers [10]. The third stage (op-amp A4)
represents a band-pass filter with some gain. The total gain of the circuit as shown
in Figure 2.9 is given as
2R2 R4 R
G = 1+ 1+ 7 . (2.4)
R1 R3 R6
Biomedical Sensors and Data Acquisition 29
1 1 1
f= − . (2.5)
2π R1C1 R2C2
A good example of commercial ECG amplifier is AD620 from Analog Devices, a modifica-
tion of the classic three OPAMP INA approach. The gain of the amplifier is determined
by a single external resistance, and potentiometer for offset trimming are externally con-
nected. In addition to the certain desirable characteristics, specific design enhancements of
the ECG amplifier are required:
1. PLI reduction: Stray capacitances are formed between the ECG lead wires and
neighbouring power lines. The displacement currents flow to ground through the
patient’s body and right leg, which is used as reference. Considering a small resis-
tance r0 of common lead wire (at right leg) and displacement current id, the small
drop Vc = r0·id appears as common to the first stage of INA. A high-CMRR INA
can minimize this effect. Additionally, a driven leg circuit (DRL) is used [3,11], as
shown in Figure 2.10a. In the DRL circuit, two sensing registers (R1) are used at the
output of the first stage of the INA. They invert, amplify, and feedback the voltage
Vc to the right leg of the patient. The modified common-mode voltage is given by
id r0
Vc = . (2.6)
1 + 2 R2 R1
Hence, the interference signal is minimized at the amplifier input. A low-power
VLSI implementation of DRL circuit is described in [12]. A second option to minimize
the PLI is to implement a 50/60 Hz notch filter in the INA, as shown in Figure 2.10b.
2. Patient isolation: The objective of patient safety in a biosignal measurement
application is to ensure that current from the acquisition circuit or its applied
part can flow through the patient’s body to ground, termed ‘leakage current’, and
remain within safe levels in case of a fault. Thus, all biomedical amplifiers acquir-
ing physiological signals from human body must satisfy some safety criteria for
the worst-case voltage breakdown and maximum leakage currents through the
electrodes attached to the human body. The accepted international standard is
IEC-601 for Medical Electrical Equipment adopted by Europe as EN-60601. This is
R1
-
+ R2
-
r0 +
Vc = idr0 / (1+2R2/R1)
Vc = idr0 (a) (b)
FIGURE 2.10
PLI reduction techniques in ECG measurements: (a) driven right leg; (b) notch filter. (Printed with permissions
from “Biomedical sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al.
(Eds.), Springer Nature, 2017.)
30 Health Monitoring Systems
combined with UL (Underwriters Laboratories Inc.) Standard 2601-1 for the United
States and endorsed by the Health Industries Manufacturers’ Association (HIMA),
National Electrical Manufacturers Association (NEMA), and US Food and Drug
Administration (FDA) [3]. IEC-60601-1 standard allows a patient auxiliary current
(current that can flow between two separate leads connected to the patient’s body)
up to 100 µA at not less than 0.1 Hz. The objective is to achieve complete galvanic
isolation between the patient and acquisition circuit, and using surge protection
arising from defibrillator or electrosurgical equipment.
Circuit (a)
Earth Ground
common
Input
Stage
R
D -
P
Output Stage
Circuit
common Earth Ground (b)
FIGURE 2.11
Schematics of patient isolation using isolation amplifier: (a) optical isolation; (b) magnetic isolation. (Printed
with permissions from “Biomedical sensors and their interfacing” in Advanced Interfacing Techniques for Sensors,
B. George et al. (Eds.), Springer Nature, 2017).
Biomedical Sensors and Data Acquisition 31
FIGURE 2.12
A practical ECG amplifier circuit. (Printed with permissions from “Telecardiology” in Telemedicine and Electronic
Medicine, J.G. Webster, H. Eren (Eds.), CRC Press, 2017.)
a matched pair of capacitors (1–3 pfd range). At the output stage, the modulated signal is
converted back to analog voltage by an averaging technique. The performance of an iso-
lation amplifier is described by isolation-mode rejection ratio (IMRR), which refers to its
ability to suppress the feed-through isolation-mode voltage that arises across the barrier
and output stage, detailed in [28].
Accidental voltage surge protection from the biomedical equipment can be achieved by
connecting a voltage limiting device (e.g., zener diode) between the connecting electrode
and ground. The device absorbs the extra current inrush when the voltage across it raises
a certain value, typically 300 mV or higher.
A practical single-channel ECG amplifier is shown in Figure 2.12. The first stage uses
AD620 with a gain of 10–20 to avoid saturation of its output by electrode offset potential,
which is around 300 mV. The gain setting resistance can be chosen as per the equation
49.4 k
RG = . (2.7)
G−1
The output of AD620 is used to implement a DRL circuit, as well as AC coupled to block
the DC and low frequency (up to 0.05 Hz) using a low-pass filter (1 µF and 3.3 MΩ combi-
nation). The second stage uses a 70 Hz low-pass filter (CA3140) with a small gain. The last
two stages (OP07s) provide a gain of 20 and a variable DC offset to obtain unipolar output.
Nowadays, many soft computational techniques are available which are used for denoiz-
ing of digitized ECG.
SP
h1 h2 DP
DN
FIGURE 2.13
A typical transmission PPG waveform and clinical features. (Printed with permissions from “Biomedical
sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer
Nature, 2017.)
of absorbance of light radiation between blood-full and bloodless skin at near infrared (IR)
wavelengths. The transmission mode, LED (operating in 0.8–1 µm wavelength), and PD are
attached to opposite surface of skin, and the PD captures the light after passing through
the bones, tissues, and blood capillaries. Under this wavelength, light is least absorbed by
water content of the t issue. The PPG waveform shows a steady (DC) and a pulsatile compo-
nent. The steady part represents thermoregulation and autonomic functions. The pulsatile
component, as shown in Figure 2.13, is related to blood volume changes and consists of
two phases, anacrotic (relates to ventricular systole) and catacrotic (relates to ventricular
diastole), represented by four major fiducial points, viz., foot (F), systolic peak (SP), dicrotic
notch (DN), and diastolic peak (DP). During ventricular systole, there is a momentary rise in
blood volume in the peripheral capillaries, and in the mentioned wavelength, the received
light intensity at the PD is less. The absorption of light by blood is quantitatively expressed
by Beer–Lambert’s law:
I = I 0 e − dα c , (2.8)
where I is the light intensity at a distance d, I0 is the intensity at source, α is the absorp-
tion coefficient of blood, and c is the concentration of the medium. However, other factors
like scattering, reflection, etc. from bones and skin tissues are not considered. By conven-
tion, the PPG waveform is inverted so that it correlates positively with blood volume. In
catacrotic phase, more light is absorbed by the PD due to less blood flow. The anacrotic
phase is characterized by SP. In the catacrotic phase, there is momentary rise of blood
flow in peripheral capillary due to reflection from pulse wave pressure from the closed
aortic valve. This results in the DN and DP. Like ECG, there are certain morphological fea-
tures which can provide significant clinical information on cardiovascular parameters, as
shown in Figure 2.13. Transmission-mode PPG sensing is feasible only for tissue thickness
of up to a few centimeters. For thicker body sites, the detectable light intensity is too faint
to produce a satisfactory signal to noise ratio (SNR). Transmission-mode measurements
are typically performed on digits and the earlobes. In reflection-mode PPG, the LED and
PD are attached to same surface of skin, and green wavelength in the range 0.5–0.6 µm
has become popular in recent times. Unlike transmission mode, this configuration can
Biomedical Sensors and Data Acquisition 33
LED Photodiode
(Source) (Detector)
Epidermis
Dermis Capillaries
(a) (b)
FIGURE 2.14
Reflective PPG: (a) tissue penetration and back scattering; (b) source and detector circuit. (Printed with permis-
sions from “Biomedical sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George
et al. (Eds.), Springer Nature, 2017.)
be applied to any site of the body. In this mode, the emitted photons that arrive at the PD
follow a banana-shaped primary light path through the tissue, barely penetrating deeper
than the skin, as shown in Figure 2.14a. This is due to the reason that with the increase in
blood flow in a capillary, non-hemolyzed erythrocytes act as little mirrors which reflect the
incident radiation to the PD [15].
The PPG sensor electronics shown in Figure 2.14b consists of a forward biases LED with
a current limiting resistance Rlim, with the light intensity produced being directly propor-
tional to the diode current (few mA ILED) to produce a voltage drop of nearly 2 V across the
diode. In the detection circuit (a phototransistor), a load resistor (RL) converts the output
current into a proportional voltage Vout:
Vout = I CE RL . (2.9)
A typical PPG signal conditioning circuit is shown in Figure 2.15. It consists of two identi-
cal cascaded stages of passive high-pass filter (cutoff at 0.5 Hz) followed by active low-pass
filter (cutoff at 3.4 Hz). The potentiometer P provides adjustable gain to the final output.
The reference voltage is kept fixed at 2 V.
+
Vin from + + Analog output
4.7µF -
sensing 4.7µF -
-
circuit
P
470K
470K Buffer
68K
10K 100nF VREF 68K 10K
100nF
Passive Active Passive Active
HPF VREF LPF HPF LPF
VREF
FIGURE 2.15
PPG signal conditioning circuit. (Printed with permissions from “Biomedical sensors and their interfacing” in
Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
34 Health Monitoring Systems
In recent times, there has been resurgence over the use of PPG for cardiovascular func-
tion monitoring, and many improvised integrated sensors have been proposed which
facilitate wearable and wireless monitoring [16–18].
A useful application of transmission-type PPG is found in pulse oximetry, which mea-
sures oxygen content in blood. Here, two LEDs are used as light sources, one at 660 nm
(red) and the other at 940 nm (IR) with a single photodetector (normally, a silicon PD) as the
sensor. In these two wavelengths, the absorption coefficients of oxygen-rich (HbO2) and
oxygen-less blood exhibit maximum separation, expressed by extinction coefficient [19].
In each cycle, the red LED is turned on, both LEDs are powered off, IR LED is powered
on, and both LEDs are powered off in sequence through a high-speed switching circuit.
The photodetector is mounted on the same plane but opposite side of the finger to receive
radiation pulses from both the LEDs. The output of the photodetector is very small (order
or few mV), and hence protection against electromagnetic interference (EMI) and radiofre-
quency interference (RFI) is essential. The most common practice is to use a high-CMRR
INA to minimize the common-mode interference signal. The schematic block diagram of
a standalone pulse oximeter is shown in Figure 2.16. The LEDs and PD are mounted in a
flexible, light-resistant enclosure. A single screened cable powers the LEDs as well as car-
ries the PD output to the electronics module. The switching of the LEDs is controlled by a
microcontroller unit, which also calculates the SpO2 after separating the signal components
corresponding to oxygen-rich and oxygen-less blood from the PD output. The PD output
is the representation of the absorption components due to tissue, venous blood, arterial
blood, and variation due to pulsation in arterial blood (plethysmogram information). The
amplified signal is filtered to get the representation of red, IR, and ‘dark’ light components,
from which the DC or steady parts are extracted and scaled to equal values by the micro-
controller unit. Then the pulsating components [AC] Red and [AC] IR corresponding to red
and IR are extracted, and their amplitude ratio provides a measure of SpO2 as
[AC]Red
SpO 2 = . (2.10)
[AC]IR
Diode
control
IR LED (940 nm) and
Red LED (660 nm) switching
Light resistant
enclosure
Microcontroller Display SpO2
Unit value
Analog to digital
converter
Photodetector
Differential
amplifier
FIGURE 2.16
Schematic of pulse oximetry standalone instrument using PPG.
Biomedical Sensors and Data Acquisition 35
Current drawn to
Heater temperature maintain Wheatstone
Measurand (air flow) maintained by current bridge balance (a)
change
Current output
Wheatstone bridge
Measurand (air flow) Cooling of heater
unbalance voltage (b)
(temperature change)
Voltage output
Tapered Tube
Air flow
(c)
Heated wire
Vi
Vcc
R1 R
+ 3 T
7
- 2 5 6
+ 1
R2 Rs Vs 4
(d)
-Vcc
FIGURE 2.17
Basic scheme of thermal convection flowmeters for respiration measurement: (a) constant temperature mode;
(b) constant current mode; (c) hot wire anemometer sensor configuration; (d) signal conditioning circuit of
bridge output. (Printed with permissions from “Biomedical sensors and their interfacing” in Advanced Interfacing
Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
36 Health Monitoring Systems
maintained at constant temperature by the adjustment of current from the source, which
is used as a measure of airflow rate. In constant current mode, the heater is supplied with
a constant current, and bridge unbalance provides a measure of airflow rate. A typical hot
wire anemometer configuration used in clinical setting is shown in Figure 2.17c, where a
heated wire is mounted perpendicular to the flow line (nasal cavity or mouth) in a tapered
tube. The bridge unbalance voltage is fed to a high-gain DC amplifier and an emitter fol-
lower stage, as shown in Figure 2.17d [21].
Microwave Doppler radar (MDR) [22,23] is another popular form of respiratory airflow
sensing technique that is fully non-contact type measurement. A typical MDR system
employs a pair of transmitting (Tx) and receiving (Rx) antennas mounted close to the body
of the subject. Here, a radio wave, typically in the GHz range, is transmitted, and the
modulated version of the signal due to quasi-periodic motion of the chest wall/abdomen
due to inhalation, exhalation and the pause in between is received. The signal processing
circuit captures the frequency shift of the reflected wave from the target (chest or abdo-
men). The theoretical analysis [24] shows that the inhalation, exhalation, and the pause
in between them can be represented as a phase-modulated signal, with the phase shift
directly proportional to the subject’s chest movement. A typical test set-up of MDR respi-
ration measurement system with interfacing schematic is shown in Figure 2.18. A pair of
Tx–Rx antennas transmits and receives the frequency-modulated continuous wave (CW)
microwave radiation in the GHz range, controlled by the MDR unit. The received signal
is passed through a direct quadrature demodulator with RF and baseband automatic gain
control, facilitating quadrature demodulation into direct baseband frequencies. The final
output is passed through a low-pass filter and amplified to extract the respiration signal
[25,26]. Additional advantages of MDR technique are robustness against environmental
factors, interference from other sensor signals, etc.
Ultrasonic respiration sensors can use either transit time principle or the Doppler effect, i.e.,
change in frequency or phase shift of the reflected signal based on quasi-periodic motion of the
chest due to respiration [27]. Direct time-of-flight measurements suffer from the inaccuracies
that occur in measurement of the flight time (~µs or less) and inertial delay problem in piezo
sensor. A piezo crystal operating at 40 kHz frequency transmits short bursts of ultrasonic
wave to the subject’s chest. The receiver calculates the time of travel from the reflected pulses,
considering the speed of sound wave in air. The resolution in measurement can be improved
with higher frequency (~240 kHz) activation of the sensor. Since the echo signal of an ultra-
sonic wave contains baseline wandering, high-frequency signals, and bursts; envelope detec-
tion principle from the received pulse waveform provides an indirect measurement of the
time of flight of the ultrasonic pulses [28,29]. As shown in Figure 2.19, the transceiver is placed
near the body, typically within 100 cm to avoid heavy attenuation of reflected ultrasonic wave
Tx I/Q
demodulator Filtering and
MDR with V
amplification
unit automatic
gain control
Rx
FIGURE 2.18
Schematic set-up of MDR for respiration rate measurement. (Printed with permissions from “Biomedical s ensors
and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
Biomedical Sensors and Data Acquisition 37
40 KHz crystal Tx
Gain
oscillator controller
FIGURE 2.19
Ultrasonic respiration measurement schematic. (Printed with permissions from “Biomedical sensors and their
interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
Sensor belt
LP Filter
HP Filter (35 Hz)
(0.05-0.01 Hz)
Amplifier ∑
LP Filter Vout
HP Filter (35 Hz)
Amplifier (0.05-0.01 HZ)
FIGURE 2.20
Basic RIP sensor configuration. (Printed with permissions from “Biomedical sensors and their interfacing” in
Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
energy. The reflected signal is passed through an amplifier, an envelope detection unit, and a
band-pass filter to extract the low-frequency respiration information. An envelope detection
circuit detects the peaks from the amplitude-modulated high-frequency signals employing
a combination of moving average filter and adaptive threshold at the output of amplifier.
Finally, the analog output is digitized and collected in a personal computer.
Respiratory inductive plethysmography (RIP) is a general technique used for measuring
respiration or breathing effort by change in abdominal or chest sectional area. The con-
figuration can utilize either elastic belt tension (piezo or strain gauge as sensing element)
or electrical impedance (change in current between two electrode positions) or inductive-
type plethysmography (frequency change in an RF signal looped in a chest belt) [30–32].
Among these, RIP sensors of the chest belt type are popular and often taken as standard to
calibrate other respiration sensors. The common RIP sensor consists of an elastic chest and
abdomen belt (around 1 in. wide) with a zigzagging wire sewn into it and worn around
the rib cage under the armpits and abdomen. A small current (~20 mA) is passed through
the coil from an oscillator. During inspiration, the abdomen (rib cage) area decreases
(increases) with the lung volume. A typical RIP configuration is shown in Figure 2.20. The
outputs of the sensing units are separately amplified and filtered to extract the respiration
information before being added. Since the amplitudes of abdomen and chest sensors are
unequal, the outputs of filters are normalized before addition.
character and are of longer duration. The audible and most prominent heart sounds, gen-
erally known as ‘lub-dub’, are related to the closing of heart valves. There are two other
heart sounds that are weaker in amplitude and originate due to blood flow through the
heart chambers.
Phonocardiography is the recording of heart sound and murmurs resulting from the
motion of blood through the different cardiac chambers and opening and closing of dif-
ferent valves to facilitate this blood flow. It was first recorded by Einthoven in 1907 using
carbon microphone and string galvanometer. Phonocardiography is one of the primary
auscultation techniques that can be used for cardiac function assessment and often syn-
chronously recorded with standardized cardiovascular signals like ECG. A typical PCG
recording consists of heart sounds and heart murmurs. While heart sounds are generated
due to resonant phenomena of cardiac structures like heart valves, the heart murmurs are
said to originate from blood flow turbulence through the cardiac chambers.
Figure 2.21 shows a typical PCG record with ECG and closure and opening of four car-
diac valves – tricuspid, aortic, mitral (or bicuspid), and pulmonary – indicated by binary
logic states (open: ‘0’ and close: ‘1’) [33].
Each cardiac cycle is considered to comprise of two periods, viz., systole, representing
the contraction or depolarization of cardiac chambers, and diastole, representing the relax-
ation or repolarization of cardiac chambers. Generation of ECG has already been briefly
stated in Section 2.3.1. The PCG consists of four major components I, II, III, and IV, also
named as S1, S2, S3, and S4. Out of these, S1 and S2 are generated due to valve closure,
have the largest intensity, and are audible as ‘lub’ and ‘dub’, respectively. The first heart
sound S1 is observed between closing of mitral (and bicuspid) valve and opening of aortic
(and pulmonary) valve. The second heart sound (S2) is audible between the exactly oppo-
site events, i.e., opening of aortic (and pulmonary) valves and closing of tricuspid (and
bicuspid) valves. S3 and S4 are with comparatively dull and weak in intensity, observed in
children and certain adults, and not related to valve activity.
In Figure 2.21, the R-peak position is considered the beginning of ventricular systole,
representing closure of AV valves (tricuspid and mitral), also known as isovolumetric
ECG
Mitral valve
Aortic valve
Bicuspid valve
Pulmonary valve
FIGURE 2.21
Representation of PCG with ECG and four cardiac valves.
Biomedical Sensors and Data Acquisition 39
ventricular contraction. This generates the first component S1 of the PCG, audible as
‘lub’. This can be decomposed into four subcomponents. The initial component is due
to shifting of ventricular blood toward the atria, closing both AV valves. The second
component is due to abrupt tension in closed AV valves, slowing down the blood inside
ventricles. The third component of S1 is due to oscillations of blood between root of
aorta and ventricular walls. The fourth component is due to oscillations caused due to
turbulent blood in the aorta and pulmonary valve. After the end of ventricular ejection,
isovolumetric ventricular relaxation starts, represented by the T-wave of the ECG. This
causes the fall of BP inside the ventricles below the large arterial pressure, resulting in
the closure of aortic and pulmonary valves. The second PCG component, S2, audible as
‘dub’, starts after the end of T-wave. Its two subcomponents represent the closure of aor-
tic valve (louder) and closure of pulmonary valve. The third heart sound component, S3,
is due to rapid filling of blood from atria to ventricles, with both AV valves open. This
causes the ventricular walls to vibrate. The final and fourth heart sound, S4, also known
as atrial heart sound and occurs during atrial contraction, synchronous to the PQ seg-
ment of ECG. It is not audible [1].
Pathological patterns in the PCG signal can be observed in case of cardiac malfunc-
tion, especially in case of aortic and mitral regurgitation and stenosis, both causing heart
murmurs. Wide splitting in S1 is caused by right bundle branch block (RBB), narrowing
of tricuspid valve, and atrial septal defect. On the other hand, the splitting between two
observable components of S1 disappears in case of left bundle branch block (LBB). Wide
splitting of S2 is caused by delayed pulmonary or advanced aortic valve closing. Decreased
intensity of S2 may be due to stiffened leaflets of aortic and pulmonary valve leaflets.
The heart sounds generated by all the valves travel in radial directions and can be audi-
ble by placing a stethoscope in chest surface. However, best auscultation sites are those
where the intensity of the heart sounds is maximum. Right second intercostal space cor-
responds to aortic area; left second intercostal space corresponds to pulmonic area; mid-
clavicular line located at fifth intercostal space corresponds to mitral area; and right fifth
intercostal space corresponds to the tricuspid area.
(a) (b)
xtM xtM
xt=xM
atR
at=xM atM
FIGURE 2.22
Schematic of PCG sensing: (a) absolute pickup; (b) relative pickup.
introduced. The principle of heart sound sensing is about converting the acoustic vibra-
tions into electrical signals through a contact (piezoelectric/accelerometer) or non-contact
(air-coupled) sensor. These are also known as absolute pickup and relative pickup sen-
sors, respectively, as shown in Figure 2.22a,b, respectively. In contact type, the sensor is
rigidly fixed on measurement site, and the vibration is averaged over the measurement
site. Thus the measuring area (aM) determines the contact area (at). In air-coupled sensor,
an air-filled circular cavity is fixed (sealed) at the periphery of the chest wall. The vibra-
tion is measured as a difference over displacement under the cavity (measuring area,
atM) and that under the edge (reference area, atR) of cavity. Thus, it is a kind of differential
measurement. The air pressure within the cavity is measured. The electronic stethoscope
is thus a relative pickup-type sensor.
The absolute and relative pickup PCG sensors need calibration with a standard accel-
erometer. The signal conditioning requirement of the raw sensor output specifies pre-
amplification and filtering of respiration signals (in case of patients suffering from lung
diseases), environmental vibrations, and air transmitted noises. A common practice
is to use four high-pass filters, with gradually increasing cutoff frequency, to get rid of
unwanted noises and artifact signals.
2.3.5 BP Measurement
Arterial blood pressure (ABP) is one of the prime physiological parameters used for deter-
mining the cardiovascular function and overall general healthiness. Arterial pressure is
defined as the hydrostatic pressure exerted by the circulating blood over the arteries as a
result of pumping action of the heart. Systolic pressure (SBP) is the highest pressure in a
cardiac systole (ventricular contraction), while diastolic pressure (DBP) refers to the low-
est (ventricular relaxation) one. Mean arterial pressure is the algebraic difference between
the SBP and DBP and is determined by dividing the area under the BP curve of one car-
diac cycle by its period. Pulse pressure (PP) is the algebraic difference between SBP and
DBP. The techniques for ABP measurements are generally classified as direct and indi-
rect methods. The direct technique, which measures the absolute BP at the measurement
sites by direct sensing are considered most accurate, and their accuracy is determined by
the physical characteristics of the sensor. However, it requires the insertion of the sensor
inside the measurement site. Hence, the direct ABP measurements are limited to critical
Biomedical Sensors and Data Acquisition 41
Sensing
port 3-way stopcock
FIGURE 2.23
Schematic of an extravascular direct ABP measurement system.
∆r
V0 = Vs , (2.11)
r
where Δr/r denotes the fractional change in resistance, which is nearly equal in all arms.
The bridge output is fed to an INA. The schematic connection is shown in Figure 2.24.
42 Health Monitoring Systems
+ Vcc
+ + Vcc
+2.5 V
r r -
- Vout
+
+ - Vcc
+2.5 V r r
-
- Vcc
FIGURE 2.24
Typical signal conditioning circuit for piezoresistive ABP sensor. (Printed with permissions from “Biomedical
sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer
Nature, 2017.)
In case of a capacitive sensor, the sensing diaphragm is coated with a conductive mate-
rial and used as the movable plate. The lead connectors and sensor can be micromachined
in a small encapsulated structure [36].
The risk of inducing current to the patient’s body can be avoided by using an optical
sensor. Also, the decoupling of associated electronics at the sensor site can be got rid of.
The basic principle of optical direct ABP sensing can be divided into the following catego-
ries: (a) intensity-reflective fiber-optic sensor, (b) light coupling between two fiber-optic
sensors, (c) microbending fiber optic sensor, and (d) fiber-optic sensing based on interfer-
ometer principles. The most simple configuration is based on passing or reflecting a light
beam through the deflection system (elastic member) [37,38] and measuring the modula-
tion. A classical configuration of a cantilever fiber-optic catheter probe along with its sig-
nal conditioning schematic are shown in Figure 2.25. A cantilever arrangement accepts the
pressure on a membrane coupled to a reflector, which faces the single fiber cable acting as
Cantilever Inlet
Outlet
P
Input / Output fiber
Catheter
Membrane Reflector
Legends:
a: Catheter
c b: Input / Output fiber
c: Optical source
d: optical detector
e: DC amplifier
f: zero adjustment ckt
a b d e f g h g: Filter
h: Final amplifier
FIGURE 2.25
Schematic of a cantilever-type fiber optic (FO) ABP sensor and its signal conditioning. (Printed with permis-
sions from “Biomedical sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George
et al. (Eds.), Springer Nature, 2017.)
Biomedical Sensors and Data Acquisition 43
Air cavity
P
Single mode fiber
Multimode fibre SiO2 diaphragm
FIGURE 2.26
Schematic of ABP sensing using FP interferometry. (Printed with permissions from “Biomedical sensors and
their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al. (Eds.), Springer Nature, 2017.)
both inlet and outlet, and guides the light pulses to the catheter tip. A change in pressure
alters the amount of reflected light into the output fiber. The modulation in light intensity
is sensed by a photoelectric detector and the final output is filtered and amplified to get a
scaled output. Fabry–Perot (FP) interferometry is described in [39], to measure aortic arch
and right coronary artery pressure. As shown schematically in Figure 2.26, it consists of a
single-mode fiber, a multimode fiber, and a SiO2 diaphragm. The SiO2 diaphragm couples
the pressure at the catheter tip. An FP cavity is formed by multimode fiber–air cavity
interface and air cavity–diaphragm interface. The single-mode fiber guides the light into
the FP cavity and also collects the reflected light. The multiple reflections of light in the FP
cavity form an interference pattern. The change in pressure on the diaphragm alters the
FP cavity length, and this is reflected in the phase change of the interference pattern. This
in turn brings sinusoidal changes to the intensity of the final reflected optical beam. The
reflected light can be split into two fibers using an optical splitter, one directly connected
to an optical detector (broadband channel) and the other via a tunable filter (narrowband
channel). The difference in pattern between the two detectors can be mathematically cor-
related to the blood pressure.
The schematic of an extrinsic fiber-optic interferometer ABP sensor is shown in
Figure 2.27. It contains an integrated fiber Bragg grating (FBG) core with periodic change
in the refractive index inside a single-mode fiber. The fiber is drawn into a glass capillary,
at the end of which a diaphragm is sealed by splicing a multimode fiber, which accepts
the blood pressure. An FP cavity is formed between the diaphragm and the FBG which
facilitates the reflection of light. The outside pressure can change the cavity length (ΔL)
through deformation of the diaphragm [39]. The measurement set-up consists of an optical
Diaphragm
OF cable
P
Light
(a)
Capillary Fiber Bragg Grating Cavity
Switch Coupler
Source
Catheter
Optical Spectrum Analyzer
(b)
DAQ card Computer
FIGURE 2.27
FBG-type FP interferometric ABP sensor: (a) schematic; (b) measurement set-up. (Printed with permissions
from “Biomedical sensors and their interfacing” in Advanced Interfacing Techniques for Sensors, B. George et al.
(Eds.), Springer Nature, 2017.)
44 Health Monitoring Systems
source, coupler with switch (operating in time multiplexing mode and controlled by data
acquisition (DAQ) card), an optical spectrum analyzer (OSA), DAQ card, and computer.
The coupler directs the light pulses from sources and redirects the reflected light into the
receiver fiber. The OSA converts the received radiation into intensity-modulated electrical
pulses, which are finally analyzed using a computer.
TABLE 2.2
Common EEG Waves and Frequency Bands
Name Frequency (in Hz) Characteristics
Alpha
Beta
Theta
Delta
(a) (b)
(c)
FIGURE 2.28
(a) EEG waveforms; (b) and (c) lead placement convention as per 10–20 system.
even numbers: right hemisphere. The 10–20 electrode system provides totally 21 electrodes
(19 placed in the scalp, 2 at the ears). Accordingly, modern EEG measurement systems pro-
vide 21 different signals, each referred to as a ‘channel’. For advanced applications, 32–256
channel systems are available commercially. Since an EEG voltage represents a difference
between the voltages at two electrodes, the display of the EEG may be set up in one of
several ways.
+ +
+
+
+
+
+
+
(a) (b)
+
(c)
FIGURE 2.29
Schematic of EEG montage connections: (a) bipolar connection; (b) unipolar connection; (c) average electrode
connection (lead positions are only representatives).
electrode connection, shown in Figure 2.29c, is a variant of the monopolar connection and
similar to WCT in ECG measurements.
Electrical contact between the skin and the amplifier input is an important issue for
faithful acquisition of the small EEG signal. This contact impedance (typically below 20 kΩ)
should be negligible with respect to the amplifier input resistance (typically above 10 MΩ)
and almost constant over the low frequencies. To meet these requirements, electrically con-
ducting electrodes (usually made of metal) are fixed on the skin by various techniques.
The electrode double layer generates a small potential, called half-cell potential at the
electrode–electrolyte interface. This differs from one metal to the other. Hence, heteroge-
neous materials cannot be used for EEG measurements as the common-mode voltage at the
differential amplifier inputs will not be identical and may not cancel each other. The small
potentials measured at the EEG electrodes are noisy signals, containing internal and exter-
nal artifacts. Among the major artifacts are muscle noise (electromyogram), residual ECG,
and PLI. In addition, electrode potentials of several magnitudes of EEG are also sensed at
the input of the EEG amplifier. Hence, to cancel out these effects, differential amplifiers are
employed. Use of electrolyte gel keeps the contact resistance between the skin and elec-
trodes low. Sometimes, notch filters are used to cut off the PLI at 50 Hz or 60 Hz.
The basic structure of an EEG amplifier is similar to that of an ECG amplifier, with the
total amplification factor (10,000–20,000) split into 2–3 stages. The first stage provides a
low gain 10–20. After the first stage, there is a high-pass filter (0.05 Hz) to block the DC
offset. This helps to interpret EEG signals which are superimposed on low-frequency com-
ponents arising out of artifacts or pathological activity. Also, the electrical safety of the
patients due to accidental current flowing through the brain due to failure of the elec-
tronic components is incorporated in the design as per IEC601 standard, which specifies
Biomedical Sensors and Data Acquisition 47
the maximum voltage flowing through the skin tissue at 100 µV. This can be achieved in
two ways, viz., connecting high resistances between electrode and input of amplifier and
using optical isolation. The CMRR of common EEG amplifier is around 80 dB or higher.
The structure of an EEG measurement system may vary among applications and types of
post-acquisition processing [41]. For a standalone system, a preamplifier and a main ampli-
fier transmit the ECG analog data to a local computer for storage. For a hospital environ-
ment, this computer may be connected to a remote-end server for data sharing and clinical
evaluation by multiple experts. A third configuration uses an integrated digital front end
(signal conditioning plus digital transmission unit) to wirelessly transfer the EEG data to a
local server. This can be used for monitoring multiple patients in activity [42]. Over the last
two decades, there has been some improvisations on EEG front-end signal conditioning
circuit development [43]. Among them, the initial designs aimed for low-noise, low-offset
ECG acquisition amplifier design [44–47]. Later on, the designs incorporated non-contact
sensing, portability, and wireless connectivity for ambulation analysis [48]. In [49], an
application-specific integrated circuit (ASIC) for an eight-channel EEG recording system
is described which uses AC coupled chopper stabilized INA to achieve 120 dB CMRR, and
the overall consumption is 66 µA at 3 V power. The digitized EEG is transferred at 11 bit
resolution at high sampling rate (25 ksps) using a wireless radio interface. A capacitive-
type EEG electrode design using polydimethylsiloxane (PDMS) and adhesive PDMS for
EEG monitoring is proposed in [50]. A report on advanced applications discussed the inte-
gration of EEG and near infrared spectroscopy (NIRS) for enhanced brain imaging [49].
2.4.2 EMG Signal
The electrical signal generated by muscle activity is referred to as the EMG. It is a compli-
cated signal affected by the anatomical and physiological properties of muscles and the
control scheme of the central and peripheral nervous system, as well as the characteristics
of the measurement system used to record it [33]. Recording of small electrical signals
from muscle cells was reported in 1849 by Raynold, in 1922 by Gasser and Erlanger, and
in 1944 by Inman. Traditionally, EMG has been primarily used for diagnostic purposes.
However, in the last two decades, it has been extensively used for assistive technologies
like artificial limb movement.
Unlike the myocardium, the skeletal muscles do not have any natural excitation source.
Rather, electrical excitation in skeletal muscle is initiated and regulated by the central
and peripheral nervous systems. The exact origin of electrochemical activity is beyond
the scope of this chapter. The muscle cells are activated by the central nervous system
through electric signals transmitted by motoneurons. A motoneuron innervates a group
of muscle fibers which constitute the smallest functional unit of the muscle, called a motor
unit (MU), the junction being called the end plate (Figure 2.30). The muscle fibers have
variable length and, if properly excited, can shorten their length [51]. The innervation ratio
determines how many muscle fibers are regulated by a single MU and varies among dif-
ferent body parts, smallest in the eye and largest in the leg.
The motoneurons carry nerve impulses from the brain through the spinal cord to the
nerve endings, where they connect to the muscle fibers. In brief, the origin of EMG is
attributed to spatial and time-averaged summation of APs that are generated as the end-
plate potentials due to the release of acetylcholine (Ach), a neurotransmitter, at the MU to
excite a muscle fiber. The electrical signal that emanates from the activation of the muscle
fibers of an MU that are in the detectable vicinity of an electrode is called the motor unit
action potential (MUAP). This consists of a fundamental unit of EMG signal. The shape of
48 Health Monitoring Systems
Motoneurons
Single fibre
1 2 potentials
(b)
(a)
FIGURE 2.30
EMG signal generation: (a) innervation of muscle fibers showing two MUs with intermingled muscle fibers;
(b) EMG signal collected using surface electrodes on the hand skin.
the MUAP is influenced by many factors, viz., relative position of the electrodes on mus-
cles, relative geometrical relationship between the electrode surfaces and muscle fibers on
the MU, size of the muscle fibers, and number of muscle fibers on the MU in the vicinity
of the electrode. The electrical manifestation of the MUAP is accompanied by a contractile
twitch of the muscle fibers. The MU must be activated continuously to sustain a muscle
contraction. This results in a MUAP train whose morphology remains constant if the geo-
metric relation between the electrode and the active muscle fiber remains constant. The
MUAP train can be represented as interpulse intervals; and the waveform of the MUAP, as
a sequence of delta functions convoluted over a filter h(t) that represents the MUAP:
n
pi (t) = ∑ hi ( t − tk ), (2.12)
k =1
where tk represents the time locations of the MUAPs, n is the total number of inter-pulse
intervals in a MUAPT, and i and k are integers that denote specific events.
The EMG signal over a surface area can be represented as a summation of such MUAP
trains [33]:
m(t , F ) = ∑ pk (t , F ), (2.13)
k
EMG signal
EMG signal
Amplifier
and Filter Amplifier
and Filter
Sensing Reference Sensing
electrode electrode (a) Reference (b)
electrodes
electrode
Unrelated Unrelated
Muscle
tissue Muscle tissue
tissue
tissue
FIGURE 2.31
EMG acquisition using (a) monopolar configuration; (b) bipolar configuration.
the muscle tissue. Thus the electrodes can pick up signals from only nearby tissues. The
other factors like distance between the sensing muscle fiber and detection surface and
orientation of the detection surface of the electrode with respect to length of muscle fiber
play important roles in EMG measurements.
As a special design consideration of EMG amplifiers, the electrode cables (leads) to
the input to the amplifier should be as short as possible and should not be susceptible to
movement. This is sometimes accomplished by placing the preamplifier near the electrode
housing, within 10 cm for surface EMG measurements. The recommended amplifier char-
acteristics of EMG measurements are as follows [33]:
CMRR: 85 dB or above
Input impedance: 1015 Ω in parallel with 7 pfd
Input bias current: less than 15 fA
Bandwidth: 20–500 (surface electrodes), 20–2,000 (wire electrodes), 20–5,000 (Needle
electrodes).
Most common EMG measurement configurations are available to collect 8–32 channel
data.
HF current path
LF current path
Blood vessel
FIGURE 2.32
Current path through body tissue containing cells, membranes, and blood vessels at HF and LF.
CC
M M1 M
PU
CC M2 R PU
CC CC
(a) (b) (c) (d)
FIGURE 2.33
Different surface electrode configurations for bioimpedance measurement (M: measuring, R: reference;
CC: current carrying; PU: pickup).
Biomedical Sensors and Data Acquisition 51
V
Impedance
measurement
Z= V/I
+ S +
I A2 A1
R
M
CC
FIGURE 2.34
(a–d) Typical 3-electrode measurement circuit for skin bioimpedance (M: measuring electrode; CC: current car-
rying electrode; R: reference electrode).
electrode CC, having much lower current density, will have negligible effect on measure-
ment, and may be regarded as neural electrode. A potential difference will be obtained if
the larger electrode (CC) is placed at an indifferent skin position and the smaller one (M) at
a nerve-activated skin position. If both the electrodes are placed on an active position, the
recorded voltage difference may be very low. A second monopolar configuration, a three-
electrode system (c), has one reference electrode and is used in high-resistivity skin tissue.
The measuring electrode (M) contributes more to the measurement result than the other
two electrodes. A bipolar electrode (configuration b) has equal contribution from both the
electrodes M1 and M2. Under PU configuration, these may be used to measure potential
difference due to remote organ activity resulting in a volume current flow spreading out
from the organ. Configuration (d) shows four-electrode tetrapolar system with two CC
and two PU electrodes, placed close to each other.
Figure 2.34 shows a quasi-monopolar three-electrode configuration for skin bioimped-
ance measurement [52]. Current is injected through CC at a body surface point using the
AC source (S) and amplifier A1. Considering the body surface in isopotential condition
(M and R at the same potential), A1 brings the internal body potential equal to excitation
potential (~30 mV). A2 is the current reading amplifier. The impedance between the CC
and M are measured as Z = V/I, where V is the constant PD between CC and R and I is the
sensed current. Due to the capacitive behavior of the skin, the measured current is phase-
shifted with respect to the excitation voltage. By using a synchronous rectifier circuit with
the excitation voltage (S) as reference, the impedance can be decomposed so that only the
in-phase conductance component G is measured.
Embedded Software
Legends:
Supervisory
Interface to Data flow
Biomedical Low-power transceivers Power supply
sensors digital
processor
User interface
FIGURE 2.35
Smart biomedical sensor block diagram.
demands, to achieve flexibility and remote access, these biomedical sensors are smaller,
compact, and intelligent for self-monitoring, enabled with energy harvesting for contin-
uous operation and embedded with powerful data analysis algorithms to determine a
patient’s condition. Thus, it is possible to develop autonomous systems which can be used
for the standalone (single patient monitoring) or networked (multiple patient monitoring)
mode [53]. The block diagram of a typical smart biomedical sensor is shown in Figure 2.35.
The list of sensors includes ECG; EEG; pulse oximeter; and sensors for position, motion
(accelerometers), blood pressure (non-invasive), glucose, airflow, etc. Various types of
microelectromechanical systems (MEMS) sensors find wide application in smart biomedi-
cal sensors. A low-power digital processor (DSP) is the heart of the unit and controls the
operation of the sensors (acquisition rate of the AD converter), wireless transceivers (data
delivery rate, error control, synchronization within a group, sleep mode, etc.), energy har-
vesting unit (battery condition monitoring), and the user interface (AV alarm generation).
In addition to this, the overall healthiness and functioning of the unit is supervised by
an embedded software. The energy harvesting unit uses one of the principles like ther-
moelectricity, piezoelectricity, electromagnetism, and RF to recharge a battery unit which
powers the different modules. Connectivity to local/remote relaying node/base station
can be achieved by using low-power industrial, scientific and medical (ISM) band stan-
dards like Bluetooth, WLAN, ZigBee, RFID (radio frequency identification), etc. [54]. These
modules exchange data through one of the serial protocols like I2C, SPI, UART. The low-
power DSP can extract useful features/events from a short span of biomedical signals to
communicate any abnormal situation/condition of the patent.
In the context of smart biomedical sensor applications in patient monitoring, there
are three types of applications, viz., (a) constant monitoring of vital signs of ambulatory
patients [55], (b) persons (non-patient) looking for improvement of their health condition
(like calories or, diabetes control), and (c) healthy persons (sportsperson, military person-
nel, rescue operators) tracking their activity during exercise or operations. Some essential
features to suit these demands are wearability [56,57], extreme low-power operation and
wireless connectivity and extremely small (chip area 2–4 cm2) board size. Three different
technologies have significantly contributed to achieve the mentioned features: (a) incor-
poration of mixed circuits (analog and digital) in the same circuit board using a technol-
ogy named systems-in-package (SiP) [58], where the individual dies are interconnected
through wires. (b) Three-dimensional stacking of circuit boards. Here, the circuits con-
sist of a layer of heterogeneous stack in a three-dimensional fashion. Thus, sensors can
be placed directly on top of processing and communication modules, leading to further
reduction of overall board area. (c) Adoption of flexible integrated circuits, especially for
Biomedical Sensors and Data Acquisition 53
FIGURE 2.36
Conceptual diagram of wearable sensing device with adhesive tape. (Printed with permissions from Telemedicine
and Electronic Medicine, J.G. Webster, H. Eren (Eds.), CRC Press, 2017.)
ambulatory monitoring. These circuits employ special types of fabrication techniques and
materials to fix them on flexible or even bendable parts (of the human body). An excellent
application of these flexible circuits is realized in the disposable adhesive tape wearable
biomedical sensor, a schematic of which is shown in Figure 2.36. The device is intended
to measure ECG, HR, skin temperature, blood pressure, body movement along with some
surrounding environmental condition (e.g., humidity). The most advantageous feature is
that it fits well on the skin surface (size of 20 mm × 60 mm) and allows normal activity of
the human subject. As shown in the figure, the components are stacked in a three-tier
structure, from human sensor die in the bottom (touching the skin) to analog and digital
ICs in the middle and environment sensing ICs on top. This smart integration and perfor-
mance is not possible in conventional electronics ICs mounted on printed circuit boards
(PCBs), due to enhanced cost of manufacturing and more bulky circuit. This ASIC uses
a T8051 core based on reversible logic as the central controller with 4 kB data memory,
CMOS-based sensor interface and ADC, interconnecting digital configuration bus-on-
chip, 1.2 GHz RF interface, and an in-built energy harvesting module using solar energy.
The details of development are available in [59].
2.7 Conclusion
From the era of dedicated, single patient monitoring, we are now in the age of distrib-
uted, ubiquitous patient monitoring. Over the last few decades, microelectronics, materials
science, and low-power computing technology have been the dominant players in devel-
opment of biomedical sensors used for monitoring applications. Wireless connectivity,
54 Health Monitoring Systems
self-monitoring, plug and play, and energy harvesting are some of the features of today’s
biomedical sensors. In some distributed monitoring applications, determining the patient’s
condition using vital sign analysis at the sensor node is another feature that has been
introduced. This has reduced the burden of centralized data analysis as well as data traf-
fic volume in sensor network systems. Many autonomous systems are now commercially
available which can continuously acquire physiological signals, analyze them, and pro-
vide advice for lifestyle management. Toward this end, mobile devices have played a key
role. With the advent of technological domains like m-health and e-health in the current
century, there is a broader scope for applications and R&D initiatives in the area of new
biomedical sensor development.
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3
Data Compression in Health Monitoring
Rajarshi Gupta
University of Calcutta
CONTENTS
3.1 Introduction........................................................................................................................... 57
3.2 Redundancy: An Essential Property for Compression................................................... 58
3.3 Distortion Measures ............................................................................................................ 60
3.3.1 Objective Assessment of Reconstructed Biosignals............................................ 60
3.3.2 Subjective Assessment of Reconstructed Biosignals........................................... 61
3.3.3 Diagnostic Distortion Measure for ECG................................................................63
3.4 Compression Techniques for Biomedical Signals............................................................65
3.4.1 Time-Domain ECG Data Compression..................................................................65
3.4.1.1 The Reconstruction Algorithm................................................................ 69
3.4.2 Transform-Domain Compression Methods: Wavelet and PCA......................... 73
3.4.3 Compression of PPG.................................................................................................85
3.4.4 Simultaneous Compression of Multiple Biomedical Signals.............................. 87
3.5 Guaranteeing the Reconstruction Quality in Compression...........................................90
3.6 Open Access ECG Database for Compression Algorithm Testing................................ 93
3.7 Conclusion............................................................................................................................. 94
Acknowledgments......................................................................................................................... 94
References........................................................................................................................................ 94
3.1 I ntroduction
Continuous monitoring of patients under treatment in a biomedical instrumentation set-
up generates large volume of data every day. In a hospital environment, the data volume
assumes an enormous amount due to monitoring of multiple critical patients and demands
high-capacity memory devices for data archiving. On the other hand, for remote monitor-
ing applications, patients’ physiological data are required to be transmitted to a distance
for analysis and interpretation by medical experts. Volume reduction of biomedical data
before transmission can save transmission time. For monitoring multiple patients using
wireless communication technology, this also enhances the transmission link efficiency.
Data compression for telemedical applications and continuous monitoring of patients
have been an active area of research from 1970s, especially after the introduction of
57
58 Health Monitoring Systems
(a)
Amplitude in mV
Intra-beat Inter-beat
Time in ms
Amplitude in mV
(b)
Time in ms
(c)
FIGURE 3.1
ECG patterns showing data redundancy among (a) intrabeat, (b) interbeat, and (c) interleads.
Amplitude in mV
Time
Time
D4 plot
Amplitude in arbitrary units
2nd. PC score
Time
D5 plot
Time
Time
D10 plot
3rd. PC score
Time (b) Time
(a)
FIGURE 3.2
Showing redundancy in time series ECG using (a) principal component decomposition and (b) DWT using
Daubechies6.
60 Health Monitoring Systems
The same principle follows when a biomedical signal is mapped in another domain
using a linear transform. The extent of redundancy may vary depending upon the fre-
quency spectrum, more specifically, the spread of clinical information in the signal.
∑( x − x )
2
i i
∑( x − x )
2
i i
∑( x − x )
2
i i
RMSE = i=1
. (3.3)
N
Cross correlation (CC) is the measure of similarity between two signals and is often used
to evaluate the closeness of the reconstructed biosignal to its original counterpart. CC is
expressed as
N
1
N ∑ ( x − m)( x − m)
i i
CC = N
i=1
N
, (3.4)
1
∑ ( x − m) 1
∑ ( x − m)
2 2
i i
N i=1
N i=1
where m = mean value of the reconstructed signal. Only in the case of a strict lossless
compression, CC becomes 1.
QS is the ratio of compression ratio (CR) and PRD and is often used to quantify the
overall performance of a biomedical signal compression algorithm using a single metric.
A high QS value is always desirable:
CR
QS = . (3.5)
PRD
Again, it is required to mention that, if the original biosignal contains low-frequency
noises, PRD becomes low, and as a consequence, QS becomes high, which is misguiding
too.
Maximum amplitude error (MAX) is a comparatively less popular yet efficient biosignal
distortion measure technique, which is expressed as
The metric MAX enumerates the maximum amplitude of the reconstruction error. The
smaller the value of PRD, PRDN, RMSE, and MAX, and the higher the value of CC and QS,
the better the reliability of the algorithm. However, except PRD, other objective assessment
techniques are not standardized yet.
case of a blind MOS test, first, the original signal is provided to the clinicians along with
a few diagnostic queries. At a later time, the reconstructed biosignal is also provided to
those clinicians along with those same queries. Now, answers of those queries are com-
pared to evaluate the MOS score. On the other hand, both the original and reconstructed
signals are provided to the clinicians in the case of semi-blind MOS test. Finally, average
MOS error values are calculated from the scores given by the experts [2]. Steps involved in
conducting a semi-blind MOS test are explained below.
STEP I: Print both the original and reconstructed biosignals in proper scale at which
doctors are habituated to see.
STEP II: Prepare and print a table as follows, and provide it to the doctors along with the
printed signals.
N e N fe
MOS =
1
N e N fe ∑∑Q ( e, f ) ,
e = 1 fe = 1
r e (3.7)
where
Ne = Total number of evaluators (doctors)
Nfe = Total number of considered features
Qr = Quality rating of the feth feature given by the eth evaluator.
Ne
MOS ( fe ) =
1
Ne ∑Q ( e, f ).
e=1
r e (3.8)
Data Compression in Health Monitoring 63
STEP IV: Calculate Gold standard MOS error of each feature as well as of the overall recon-
structed signal using Equations (3.9) and (3.10), respectively:
MOS ( fe )
MOS fe = 1 − , (3.9)
5
MOS
MOS e = 1 − . (3.10)
5
According to the Gold standard MOS error criteria, the reconstructed signal and also each
feature fall under the category ‘very good’ if the error values lie in the range 0–15; they fall
under the category ‘good’ if the error values lie in the range 15–35; if the MOS error values
lie in between 35 and 50, it is considered as ‘not good’, and ‘bad’ beyond 50.
It is not imperative to have all the evaluators be clinicians. Researchers who are working
in biosignal processing domains and have good understanding about the chosen signal
could also take part in the evaluation process [3]. Nevertheless it is good to have more
clinicians in the evaluation panel.
∑ ( B ( j) )
j=1
m
2
where W(m) is the weight of the mth band, n is the number of samples in each band,
and Bm(j) is the jth wavelet coefficient of the mth band of the original ECG. In the above
64 Health Monitoring Systems
n 2
∑
j=1
Bm ( j) − Bm ( j)
WPRD m = n , m = 1, 2,…, L + 1, (3.12)
∑ ( B ( j) )
j=1
m
2
where Bm ( j) is the jth wavelet coefficient of the mth band of the reconstructed ECG.
STEP V: Finally, WEDD is calculated as
L+1
WEDD (%) =
m= 1
∑
W (m) × WPRD m × 100.
(3.13)
MATLAB® code
compression algorithms are also proposed in [15,16], where the QRS-complex regions are
identified through a heuristically determined standard-deviation–based approach. First,
the standard deviation (SD) of a group of samples is calculated, and if the SD value is
found to be higher than that of a predefined threshold, then those samples are compressed
using the lossless compression algorithm [10]. Otherwise, the lossy compression algorithm
[12] is used to compress those samples.
Besides providing a high compression performance, these compression algorithms
based on ASCII character encoding also offer another benefit. The compressed ECG data
files contain only ASCII characters, and therefore the compressed data can easily be trans-
mitted to remote clinics or hospitals using low-cost telenetwork systems. The direct com-
pression technique based on ASCII character encoding is proposed in [17] for a lossless
compression of ECG signal is discussed elaborately hereunder. The technique is com-
pletely reversible, i.e., the original ECG signal can be fully recovered using an algorithm,
which is the reverse of the compression algorithm.
Block schematic of the lossless ECG compression algorithm based on ASCII character
encoding is shown in Figure 3.3.
Let us consider an array, X, containing the original ECG signal of length N. Now, the
first-difference of the ECG signal is computed:
Next, from the first-difference data (Y), at a time, eight consecutive samples are taken and
stored in another array (say Z). Let us take an example.
FIGURE 3.3
Block schematic of the lossless ECG compression algorithm based on ASCII character encoding.
Data Compression in Health Monitoring 67
Now, all the samples of the Z array are checked as to whether they are positive or nega-
tive. Positive samples of the array are marked by binary ‘0’s, and the negative samples are
marked by binary ‘1’s. Therefore, a binary string of length 8 is created with these 0s and 1s.
For the above example, the binary string looks like that given below.
0 0 1 1 0 0 0 0
The decimal equivalent of this binary string is calculated. Here, for this particular exam-
ple, the decimal equivalent of (00110000)2 is (48)10, and the decimal equivalent is considered
as the sign-byte of these eight samples. After calculating the sign byte, all the samples in
the array Z are made positive and amplified by a factor of 1000 thereafter and rounded off
to the nearest integer. For the above-given example, after sign modification and amplifica-
tion, the content of the Z array, which is shown above, will be as shown below.
1 5 1 2 0 0 1 3
Z(1) Z(2) Z(3) Z(4) Z(5) Z(6) Z(7) Z(8)
Now, these eight amplified integers of Z array are merged using various grouping tech-
niques to represent them using as few numbers of integers as possible. The grouping tech-
niques are described below.
Technique 1: If all those eight amplified integers are found identical, they are repre-
sented using only one integer. Let us take an example.
5 5 5 5 5 5 5 5
Z(1) Z(2) Z(3) Z(4) Z(5) Z(6) Z(7) Z(8)
In this case, all the eight amplified integers are represented using only one integer, i.e., 5.
Technique 2: If it is found that all the integers of Z are not identical, i.e., if the imple-
mentation of Technique 1 is not possible, then it is checked as to whether any two neigh-
bor integers are less than 10 or not. If it is found that two neighbor integers are less than
10, then the first integer is multiplied by 10 and added to the second. Let us take an
example.
1 5 1 2 0 0 1 3
Z(1) Z(2) Z(3) Z(4) Z(5) Z(6) Z(7) Z(8)
For this example, all the neighbor integers are less than 10, and therefore Z(1) is multi-
plied by 10 and added with Z(2); Z(3) is multiplied by 10 and added with Z(4); Z(5) is multi-
plied by 10 and added with Z(6) , and Z(7) is multiplied by 10 and added with Z(8). Hence,
eight amplified integers are now reduced to four grouped integers as shown below.
15 12 0 13
( Z (1) × 10 ) + Z (2) ( Z (3) × 10) + Z (4) ( Z (5) × 10) + Z (6) ( Z (7) × 10) + Z (8)
68 Health Monitoring Systems
Technique 3: If all those eight integers are grouped into four integers using Technique 2,
and if it is seen that all those four grouped integers are less than 15, then these four grouped
integers are regrouped in their binary domain. Each of these four grouped integers is con-
verted into 4-bit binary number; two neighbor nibbles (1 nibble = 4 bits) are concatenated
to form a byte (1 byte = 8 bits) and converted into decimal. Let us take an example.
Amplified integers 1 5 1 2 0 0 1 3
Technique 1 grouping 15 12 0 13
4-bit binary equivalent 1111 1100 0000 1101
Byte formation 11111100 00001101
Decimal equivalent 252 13
Amplified integers 1 5 14 9 12 14 1 3
Reduced dataset 15 14 9 12 14 13
Technique 2 Non-grouped Non-grouped Technique 2
grouping integers integers grouping
Content of K 0 0 1 1 1 1 0 0
Decimal equivalent of K 60
Finally, the sign byte and grouped, regrouped, and non-grouped integers are printed in
the output file in the form of their corresponding ASCII characters. The decimal equiva-
lent of K is printed in the form of an ASCII character only when there are non-groped
integers, i.e., Technique 4 is used. Let us take an example.
48 15 14 9 12 14 13 60
Sign byte Grouped/regrouped/non-grouped integers K
0 ☼ ♫ ○ ☺ ♫ ♪ <
ASCII characters
1. in the case of Technique 1 grouping, the corresponding character set contains only
two characters (sign byte and one character, which represent all the eight integers);
2. in the case of Technique 2 grouping, the corresponding character set contains five
characters (sign byte and four characters for four grouped integers);
Data Compression in Health Monitoring 69
FIGURE 3.4
Snapshot of the compressed ECG data file.
If the number of characters in a set varies from 7 to 10, it indicates that the set contains
either a combination of grouped and non-grouped integers or only non-grouped integers.
In this case, the last integer of the set, i.e., K, is converted into binary equivalent. In the
binary string, if any bit is found to be ‘0’, the corresponding integer is ungrouped using the
reverse approach of Technique 2. Otherwise, it is left unchanged. Let us take an example.
70 Health Monitoring Systems
Decimal Equivalent
of Sign Byte Decoded Integers
Expanded dataset 48 1 5 14 9 12 14 1 3
Deamplified numbers 0.001 0.005 0.014 0.009 0.012 0.014 0.001 0.003
Binary equivalent of 0 0 1 1 0 0 0 0
sign byte
Deamplified numbers 0.001 0.005 −0.014 −0.009 0.012 0.014 0.001 0.003
after sign modification
Thereafter, every number is added with its previous (reverse operation of first dif-
ference) to regenerate the original ECG data. The above-described decoding technique
is executed iteratively on all the character sets present in the compressed ECG data file.
Figures 3.5a–c show original ECGs, reconstructed ECGs, and the reconstruction errors
FIGURE 3.5
(a) Original ECG (MIT-BIH arrhythmia database, record no. 107), (b) reconstructed ECG, and (c) difference
between the signals shown in (a) and (b).
Data Compression in Health Monitoring 71
using the above-described compression algorithm. From this figure, it can be seen that the
compression algorithm is indeed lossless (Figures 3.6 and 3.7).
A simple and low-complexity lossless algorithm to encode the successive sample
differences (SSD) is presented in [18]. The principle is based on the fact that in terms of
variability, normally QRS complex denotes the maximum information and is clinically
FIGURE 3.6
(a) Original ECG (MIT-BIH arrhythmia database, record no. 232), (b) reconstructed ECG, and (c) difference
between the signals shown in (a) and (b).
FIGURE 3.7
(a) Original ECG (MIT-BIH arrhythmia database, record no. 200), (b) reconstructed ECG, and (c) difference
between the signals shown in (a) and (b).
72 Health Monitoring Systems
Pre-processing
(Data smoothing)
Raw ECG data
Down-sampling
(Single lead)
Compute SSD array
And normalize
FIGURE 3.8
SSD encoder: (a) SSD plot and (b) signal processing stages.
most significant in an ECG. The other regions have either low variability (P, T wave) or
almost no variability (equipotential segments PQ, ST, and TP). The normalized SSD plot
along with the raw ECG plot is shown in Figure 3.8a. It is observed that fluctuations in
QRS regions are only considerable, and the data in non-QRS regions can be represented
by 4 bits or lower. Hence, there is scope for the direct encoding of SSD array using simple
time-domain methods. The processing stages are shown in Figure 3.8b.
Although the process can be applied in real time using a short number of samples, the
CR can be enhanced by taking around 1,000 samples together, using the run-length encoder
(RLE). The objective of the different compression stages are briefed below:
Data smoothing: To reduce abrupt fluctuations and high-frequency noise in the dataset,
data smoothing can be performed either by a low-pass filter, or moving average filter, or a
cubic-spline interpolation operation.
Down sampling: In case the ECG is sampled at high frequency, say 500 Hz or more, then
this step may select optimum number of samples keeping the peak amplitudes intact.
SSD array generation and normalization: Keeping the maximum amplitude of SSD
elements at 99 by applying a normalization factor to convert them into signed integers.
Data grouping: This selects a group of eight consecutive elements from normalized
elements.
Sign and magnitude encoder: Using a binary sign representation of eight elements
(resulting a byte) and nibble combination of offset rule of encoder.
RLE: Consecutive zero elements at equipotential segments could be encoded using a
two-byte encoded data.
The advantageous feature of the encoder is that it can be implemented using low-end
microcontrollers [20] and requires practically no header information. Another success-
ful implementation to apply this encoder using a GSM-based telemonitoring service is
reported in [21].
A similar version of real-time ECG compression using zonal complexity measure is
reported in [22]. Here, the window is attributed as ‘complex’, ‘semi complex’, or ‘plain’
based on SD, and accordingly, different binary encoding rules are applied on the first- and
second-order SSD.
A cycle-by-cycle time-domain ECG compression technique based on the principle of
delta encoder of SSD is reported in [19]. A simplified compression flow logic diagram is
shown in Figure 3.9.
Data Compression in Health Monitoring 73
Pre-processing
Stop
Beat extraction, segmentation and
beat length adjustment Y
N End
Start processing within beat Of data
?
Y Block N
= ‘plain’
Compute SSD, Bias, ? Compute SSD, Bias
and No. of bits for
Merge encoding Nibble combination of Merge
offset encoder
Apply binary encoder
Next
Next Block= Y
Y Block ‘complex’
= ‘plain’ ?
? N
N Header byte generation
Header byte generation
Compressed packet for
Compressed packet for ‘complex’ block
‘plain’ block
End
N Y
Of beat
?
FIGURE 3.9
Logic flow diagram of the hybrid encoder [19].
Each beat or cardiac cycle is fragmented into blocks, which can be ‘plain’ or ‘complex’,
depending on its complexity decided by the SD. Accordingly, for plain ‘block’, a flexible bit
allocation is adopted where the SSD array is compressed by a binary encoder. Successive
‘plain’ blocks are merged to enhance the compression. For ‘complex’ blocks, fixed length
nibble combination or offset rule encoder is adopted. In this sense, the compressor is
hybrid. An important finding of the work is low bits/sample requirement using digitized
samples at 10 bit resolution. This enhances the link efficiency in telemonitoring application.
with reference to some fiducial point [24]. This representation, shown in Figure 3.10 maxi-
mally exploits intrabeat as well as interbeat correlations in the ECG data to achieve higher
compression and low distortion. In regard to lossy compression techniques, there are two
basic signal processing steps. First, the basic mathematical model based on which the com-
pression is done. This step provides the maximum data volume reduction. This includes
the transform function, the rule based on which the coefficients are selected (or discarded),
their resizing (truncation if any), and the quantization strategy for selected coefficients.
The other step is an information coding which is usually lossless. The steps for transform-
domain compression and their significance are summarized:
a. Preprocessing of data to remove unwanted noise and artifacts. Most of the physi-
ological signals (ECG, EEG, EOG) are of low frequency (below 100 Hz). Hence,
implementing a low-pass filter of appropriate frequency can significantly discard
the radiofrequency and electromagnetic interference. However, some of the arti-
facts lie within the clinical spectra and require special digital signal processing
technique to get a cleaner signal.
b. Delineation of the cardiac cycle is required if 2-D compression is implemented. For
ECG, this requires the detection of the R-peaks accurately in each cardiac cycle.
Till date, many successful implementation of R-peak detection algorithms are
available in published literature [25–28].
c. For 2-D compression, the extracted cardiac cycles are aligned with respect to
a major fiducial point (like R-peak in ECG), and beat lengths are equalized by
‘period normalization’ or ‘padding’ to form a 2-D matrix.
d. Orthogonal expansion is applied to get a representation of the 1-D or 2-D data
in different orthogonal dimensions, represented by expansion coefficients. These
coefficients have no redundancy among them.
e. The compression is carried out by two steps, viz., redundancy reduction and
information reduction. For physiological signals like ECG, it is observed that most
of the signal energy is represented in limited orthogonal dimensions (or scales)
and in fewer number of expansion coefficients in those dimensions. In other words,
the non-contributing dimensions (scales) and coefficients are considered redun-
dant. So, identifying the useful dimensions (scales) and selection of ‘contributing’
coefficients based on a rule constitutes the redundancy reduction step. Further
truncation of the selected coefficients using a thresholding rule (described later in
this chapter) constitutes the information reduction stage.
f. For digitized representation of the selected expansion coefficients for further use,
quantization is applied. The quantization can be fixed or adaptive based on their
zonal significance toward data reconstruction. This involves some loss of informa-
tion associated with rounding off the data.
g. Lossless ending of the coefficients is applied based on probability distribution.
Most common are delta, Huffman, and Arithmetic coders.
h. To include essential side information for decoding, data packetization is done for
each group of raw data samples considered for compression. The header bytes
containing this extra information are normally prefixed before each data packet.
Among the different mathematical tools, wavelet transform, discrete cosine trans-
form (DCT), Walsh transform, Karhunen Louve transform (KLT) (popularly known as
Data Compression in Health Monitoring 75
FIGURE 3.10
3D plot of beat matrix, with aligned R-peaks, showing interbeat redundancy.
1 t − b
ψ a , b (t) = ψ , (3.15)
a a
where a and b are scaling (dilation) and shifting (translation) parameters, respectively, and
t is the time. The mother wavelet ψa,b is compressed (a < 1) and dilated (a > 1) and shifted
over the waveform segment (for different b values) to realize the different frequency com-
ponents occurring in different time by a convolution operation. Lower a values provide
good frequency resolution but poor time information, whereas higher a values provide
good time resolution but poor frequency resolution [29]. This, in effect, can be under-
stood as a series of band-pass filters with varying band-pass and center frequencies being
76 Health Monitoring Systems
imposed on the investigated waveform. The utility of WT for ECG compression can be
realized from the fact that most of the QRS energy is represented in the lower-order sub-
bands, while the higher sub-bands mostly include noise representations [30].
The continuous wavelet transform (CWT) generates substantial redundant information
as the parameters a and b can assume any values. A good alternative is to implement DWT
where these parameters can be varied in discrete steps: a = 2j and b = k2j. The DWT can
be represented as a correlation of the mother wavelet ψ(t) and the ECG f(t) for scale and
location parameters j and k, respectively:
+∞
X j, k =
∫ f (t)Ψ
−∞
j, k (t) dt. (3.16)
In dyadic wavelet transform, the parameters a and b vary in a geometric scale of ratio 2.
This facilitates multiresolution analysis (MRA) decomposition of the ECG successively
using two basic functions, viz., the wavelet ψ(t) and the scaling ϕ(t), into two scales called
coarse and finer components [5]. These functions can be obtained as a weighted sum of the
shifted and dilated versions of the functions itself, represented as
where h[n] and g[n] are the half-band low-pass filter and high-pass filter, respectively.
Again the scaling and the wavelet functions ψ(t) and ϕ(t) discretized in scale j and transla-
tion k can be obtained from their original versions, represented as
1 t
ψ j , k (t) = ψ j − k ,
2
j 2
(3.18)
1 t
φ j , k (t) = φ j − k ,
2 j 2
where j and k control the dilation and translation, respectively. Filtering and down sam-
pling of the original discretized signal x[n] lead to an approximation A (lower frequency)
and detail D (high frequency) coefficients, given as
A[ k ] = ∑ x[n]
n
• h[2 k − n],
(3.19)
D[ k ] = ∑ n
x[n] • g[2 k − n].
Among the various compression techniques that are based on DWT decomposition of
ECG, the tree-based methods and threshold-based methods are most popular. Among
the tree-based methods, embedded zero tree wavelet (EZW) [31] and its extended
version, set partitioning in hierarchical trees (SPIHT) [32], have demonstrated good
performance for ECG compression. Here, the ECG dataset is successively decomposed
to form a pyramidal structure. Some of the decomposed coefficients are considered
as ‘significant’ when they are greater than a threshold and otherwise considered as
non-significant. EZW is based on following principles: (a) the position and sign of the
significant coefficients are considered; (b) non-significant coefficients are compactly
encoded based on self-similarity across sub-bands; (c) successive approximation of
the significant coefficients. The SPIHT algorithm sorts the coefficients transformed by
wavelet-based decomposition in order of their significance or magnitude. This partial
ordering is done by comparing them with an octavely decreasing threshold. For trans-
mission applications, coefficients with low bit rate are ordered first, with gradually
decreasing significant ones.
Algorithms based on wavelet threshold are computationally most simple among
transform-domain compression techniques. The wavelet coefficients are ordered based
on their significance and thresholded based on a fixed energy packing efficiency (EPE)
[33,34], either targeting a bit rate or the criteria based on reconstruction error. A binary
significance map (BSM) is created to encode the positions of selected wavelet coefficients.
The selected set of DWT coefficients are then quantized and compressed using a lossless
encoder like delta or Huffman encoder. There are two types of thresholding applied, ‘hard’
and ‘soft’. The hard thresholding is based on ‘keep or kill’, i.e., the coefficients greater than
the threshold are retained, and those with lesser values are ‘killed’. The soft thresholding
is based on ‘shrink or kill’, i.e., the coefficients greater than the threshold are decreased
by the amount of threshold, and those with lesser values are ‘killed’. Mathematically, the
thresholding rules are represented as
dˆ i , l = di , l if di , l ≥ th if di , l ≥ th : ‘hard’ thresholding
= 0 otherwise,
(3.21)
(
dˆ i , l = sign di , l − th ) if di , l ≥ th : ‘soft’ thresholding
= 0 otherwise,
∑d 2
i,l
ECE = l
, (3.22)
∑∑
i
l
d
2
i,l
where the inner (outer) summation in denominator indicates summation within (across)
a sub-band. The selected coefficients are quantized using a linear quantization (LQ)
strategy. The final data packet consists of encoded BSM followed by the lossless encoded
amplitudes of the selected coefficients. In [35], an iterative threshold adjustment was
carried out until a fixed percentage of DWT coefficients assumed zero value. Some
applications of quality control over reconstructed data and/or bit rate control is avail-
able using DWT-based compression. In [36], a predefined PRD could be achieved by the
DWT-based compression based on adaptive quantization strategy of the orthonormal
DWT coefficients, followed by an entropy encoder utilizing Lempel–Ziv–Welch (LZW)
encoder. In [37], noise estimation from each block of ECG data, consisting of 512 samples,
was done and used as the t hreshold to compress the DWT coefficient in the coding stage.
The noise level was e stimated by adding different known noises to the clean ECG data
and then computing the RMSE for each block between the noisy signal and clean signal.
It was observed that the distortion level is minimum when the applied threshold equals
the noise level. In another approach [38], the authors decomposed the ECG data up to five
levels, packed them in three frames, and concatenated them. The less significant DWT
coefficients were thresholded. A uniform scalar zero zone quantizer (USZZQ) was used
for quantizing significant wavelet coefficients. Finally, Huffman coding of the difference
between the significant coefficients was done.
For real-time applications like telecardiology, modification of the traditional, recursive
wavelet-based decomposition of the ECG data was suggested to meet the desired latency
as well as low distortion. In [39], a 1-D round-off non-recursive DWT scheme is presented
to achieve a linear quality control over the reconstruction error. A bit allocation scheme for
quantization of DWT coefficients based on zonal complexity of the ECG data is presented
in [40] to achieve low delay in the compression process. The payload after DWT decompo-
sition can be reduced by an approach described in [41], which preserves the bit depths to
secure bits of interest after DWT decomposition, followed by RLE.
Major parameters which determine the performance of the DWT-based compression
method in terms of CR, PRDN, latency are the choice of mother wavelet, levels of decom-
position and their individual significance toward representing the total energy and clini-
cal information, type of thresholding rule, quantizing principle (linear or non-linear), and
finally, the lossless compression of the BSM and quantized coefficients. In the published
literature on ECG compression, Coiflet, biorthogonal spline, and Daubechies wavelets
have been the most popular use, due to their morphological similarity with the QRS zone
of the ECG. This ensures that most of the ECG energy is captured in limited sub-bands
with fewer number of DWT coefficients. In [42], the wavelet that best matches the shape of
the ECG signal has been addressed. The threshold estimation is based on two principles,
viz., retaining the coefficients with value higher than the threshold and compromising
between the number of retained coefficients and target bit rate reconstruction error. In
reported works, the following thresholding rules have been applied: (a) energy packing
efficiency [33]; (b) total percentage of DWT coefficients to be zeroed [35]; (c) fixed number of
coefficients from sorted DWT decomposed array, sorted in descending order of amplitude;
and (d) recursive selection of coefficients for achieving distortion value [43]. The length
Data Compression in Health Monitoring 79
of the window plays an important role for latency and buffer requirement for hardware
implementation, the most common choices being 512, 1,024, and 2,048 number of samples.
There are a few wavelet-based 2-D compression works available in published literature
[24,44,45]. The motivation behind all of these techniques is to exploit the interbeat correla-
tion in a single-lead ECG data. After R-peak detection, the 1-D ECG data is cut and aligned
with reference to the R-peak to form a 2-D image matrix. Thereafter, compression based on
either wavelet packet or vector quantization (VQ) has been applied. In VQ, the compres-
sion is performed by quantizing wavelet coefficients into a reduced set of vectors, defined
in the codebook, C, defined as
C = c1 c2 cm , (3.23)
( ) ∑ X
2
d Xk , c j = k − c j , k , (3.24)
j=1
where n is the number of subsets where the Xk belongs to. The minimum error (d) pro-
vides the best match of Xk. In VQ, instead of the whole Xk, the index of c, i.e., j, is stored. In
[45], the DWT coefficients are arranged in a tree structure to form a codebook vector. The
energy concentration occurs in low-frequency sub-bands. These coefficients are lossless
encoded to form compressed data packets.
A quality-controlled lossy compression using average beat subtraction and lossless
encoder was proposed [46]. Since the consecutive ECG beats show adequate similarity
(unless an abnormal beat occurs), a block of 10 ECG beats were considered at a time, and
one progressive average beat (PAB) and 10 residues were computed using the equations
1
ba (i) =
2
[bav (i − 1) + b(i)] ,
(3.25)
res(i) = bav (10) − b(i),
where b(i) denotes the ith beat vector, formed by period equalization from the group;
ba(1) = b1 and i starts from 1; ba(10) is the final PAB; and res(i) is ith residue. The compres-
sion flow consists of preprocessing, beat extraction, formation of 2-D beat matrix, comput-
ing the PAB and residuals, applying DWT on them, and finally selection of coefficients and
their lossless encoding, as shown in Figure 3.11. Coiflet 4 was used as mother wavelet to
decompose the PAB and residuals.
The DWT coefficients from ba and residual vectors were selected on 5% PRDN c riteria
and 99.9% ECE criteria, respectively, by ordering them in decreasing order of signifi-
cance. Fixed quantization levels of 8 and 5 bits were used for selected PAB and residuals.
Figure 3.12 shows the reconstruction quality for MIT-BIH arrhythmia 117 data, which is
used by many researchers. Here, the ECE is used for reconstruction with a PRDN control
limit of 5%, yielding a CR of 10.59 and PRDN of 4.91. As shown in the figure, the recon-
structed plot closely follows the original signal, with minor deviations in the QRS regions.
Diagnostic ECG, specially arrhythmia records, often contain abnormal patterns
like premature ventricular contraction (PVC), atrial premature beat (APB), left bundle
branch block (LBB), etc. Here, it is essential that the pathological information should be
80 Health Monitoring Systems
R-peak detection
Quantization
Compressed packets
FIGURE 3.11
Process flow diagram of residual encoding using PAB.
FIGURE 3.12
Original reconstructed ECG data and error plot for MIT-BIH arrhythmia data 117 using residual encoder
compression.
FIGURE 3.13
Abnormal beat morphology distortion in uniform compression for achieving higher compression.
PCA is the other ECG compression tool that received significant attention. Mathemati
cally, it can be defined as a linear transform that projects the multiple ECG measurements
in few orthogonal dimensions, called the basis vectors or eigenvectors, based on a hid-
den data pattern within the dataset. The projections, called scores, signify the variability
within a dataset in the transformed space.
In the context of ECG compression, this variability can be explored by forming a 2-D
matrix using ‘cut and align method’ to form a beat matrix B and then executing the
PCA using the eigenvalue decomposition of the covariance matrix (also called variance–
covariance matrix) of B. The [B]m×n consists of m beat vectors, each consisting of n samples
after period equalization, and is represented as
B = B1 B2 Bn ,
(3.26)
T
where Bk = bk 1 bk 2 bkm ,
pc = Ψ T × B,
Ψ = ψ 1 ψ 2 ψ m , (3.27)
T
pc = pc1 pc 2 pc m .
where ψ k defines the eigenvector and the pck, the principal component score. It is observed
that the ECG signal energy in the transform-domain is represented in fewer scores,
called significant scores, as shown in Figure 3.2a. Thus, the m-dimensional data can be
represented in the transform-domain by first few (say t, where t < m) scores, providing
a scope of dimensionality reduction [47]. In PCA-based ECG compression techniques,
the transformed scores and eigenvectors are used for further processing. Selection of
the number of scores depends on the target reconstruction quality or bit rate. In [48], a
PCA-based single-ECG compression is described where the PCs and their quantization
levels are recursively selected based on predefined distortion measures and/or CR. The
signal processing flow is shown in Figure 3.14, which consists of independent control over
distortion and bit rate.
82 Health Monitoring Systems
R-peak detection
Legends:
Beat extraction and formation of 2-D N_pc= retained number of
matrix using ‘cut and align method’ PCs/ eigenvectors
© [ψ ], [pc]
Distortion control Bit rate control flow
flow
Fixed quantization of selected [ψ], [pc] Variable quantization of selected [ψ1], [pc]
Is error Desired
Include next pc, modify within bit rate Vary quantization, modify
tolerance? achieved ?
N N
Y Y
Compress [ψ], [pc] using delta and Huffman coder
FIGURE 3.14
Signal processing flow for PCA-based compression with independent distortion and bit rate control strategy.
For distortion control flow, two parameters, viz., PRDN and MAE, were checked after
each iteration, and the number of scores were recursively increased until the desired tar-
get distortion level was met with. However, quantization levels of eigenvectors and scores
were kept fixed. For bit rate control, however, a single score was considered, and the quan-
tization level of the score was varied until the CR per group of data was achieved. Two
types of myocardial infarction (MI) abnormality, viz., anterior and interior, along with
the normal pattern were evaluated from Physionet ptbdb data [49]. Figure 3.15 shows the
distortion control (panel a) and bit rate control (panel b). Although both plots show accept-
able reconstruction, the CR for bit rate control is 45.75, at the cost of higher PRDN at 11.16,
whereas the same for distortion control is 12 but with PRDN of 4.57. For MIT-BIH arrhyth-
mia data with normal rhythm (MIT-BIH arrhythmia 117), the reconstruction pattern with
error control is shown in Figure 3.16a. The research [48] showed that there is a limit (CR of
68.96 MIT-BIH arrhythmia 117 from Physionet) up to which the bit rate control could be
achieved keeping the reconstruction quality clinically acceptable. The variation of PRDN
and MAE with target bit rate (or CR) is shown in Figure 3.16b,c, respectively.
Instead of the fixed quantization level, a joint optimal selection of for the PCs and
eigenvectors along with their quantization level can provide better control over the
Data Compression in Health Monitoring 83
FIGURE 3.15
Compression performance with IMI abnormality from ptbdb: (a) reconstruction control and (b) bit rate control.
FIGURE 3.16
Compression performance with MIT-BIH arrhythmia data record 117: (a) using reconstruction control, (b) PRDN
variation with CR, and (c) MAE variation with CR.
84 Health Monitoring Systems
over which the (bpc)k and (beig)k can traverse. The velocity and position of the participles are
updated as
where c1 and c2 are positive constants (cognitive variables), φ1 and φ 2 are two random vari-
ables (social variables) with uniform distribution between 0 and 1, w is the inertia weight
which shows the effect of previous velocity vector [vi(t)] on the new vector [vi(t + 1)]. xi(t + 1)
is the updated position vector of xi(t). Best visited position for the particular particle is pbest
and best position explored for all particles is g best.
The results with optimal bit allocation are shown in Figure 3.17, with a limiting PRDN of
5%, using MIT-BIH arrhythmia data 117. Panel (a) shows the bit allocation for different PC
scores and eigenvectors under different PRD constraints. An interesting point to note is that
the bits are not always allocated as per the general notion, i.e., allocating a higher number
of bits to PCs contributing more energy, e.g., for 2% PRDN limit, p3 is allocated with 8 bits
against p2 which is allocated with 7 bits. Panel (b) shows the deviation in few clinical signa-
tures: QRS width, QRS amplitude, T-height, P-amplitude, etc. against fixed bit and variable
(optimal) quantization levels. It is clearly observed that except the P-amplitude, the optimal
bit allocation provides least distortion in clinical signatures. The panel (c) shows the origi-
nal, reconstructed plot along with sample to sample error. Compared to fixed quantization
(shown in Figure 3.15), the optimal quantization technique provides better results in terms of
PRDN, at the cost of lower CR.
FIGURE 3.17
Performance analysis of optimal bit allocation for eigenvectors and PCs using MIT-BIH arrhythmia data record
117: (a) the bit allocation, (b) distortion measures, (c) original and reconstructed ECG data with error plot (target
PRDN 5%). For ease of interpretation, the error signal is plotted with a bias.
Data Compression in Health Monitoring 85
3.4.3 Compression of PPG
Compression of PPG has received attention from the signal processing research
community in the last decade only, due to gradual expansion in the use of pulse oxim-
etry in clinical heath monitoring. Compared to ECG, a PPG signal has less complicated
morphology, containing an anacrotic phase (ventricular systole) and a catacrotic phase
(ventricular diastole). Till date, only limited published literature is available on PPG com-
pression and can be divided into two broad categories, viz., time-domain methods [50–54]
and transform-domain methods [55,56]. The time-domain methods majorly use delta-type
encoder which are lossless or lossy.
Application of double derivative and Huffman coding for lossless PPG compression is
illustrated in [51]. The signal processing flow diagram is shown in Figure 3.18. At first, the
raw PPG samples are scaled in the range of 10–1,000. Then two successive first order deriv-
atives (d1 and d2) are applied; each time a biasing was applied to make each data element
positive. Huffman encoding is applied on second-derivative array. Its justification lies in
the fact that the PPG morphology has slow slope changes in the anacrotic and catacrotic
phases, as compared to ECG, where the slope changes in QRS complex are sharp. Thus,
the number of symbols with larger occurrence will be higher, compared to those with
smaller number of occurrence. Although the coding is simple, based on probability distri-
bution of the unique symbols, the header bytes require a lot of information to be included
for proper decoding. The work [51] shows that using 10 bit resolution data sampled at
128 Hz, major clinical features like systolic upstroke time and systolic amplitude in the
reconstructed signal are within 1% from their respective original values. A salient fea-
ture of the work is that it can be implemented by a low-end microcontroller (like 8051)
in real-time monitoring. The work reported in [50] shows another simple technique
which is u seful for real-time PPG compression. Here the morphological complexity of
the current window is determined by SD of the first derivative and tagged as either
‘simple’ or ‘complex’. Based on zonal complexity, RLE is implemented on ‘simple’ zones
with a thresholding rule, followed by a selective biasing or nibble combination. The
work achieves a CR of 3.84 with a PRDN of 7.57. The signal processing flow diagram is
shown in Figure 3.19.
Till date, the work reported in [53] has achieved highest CR of 122 and low distortion
(less than 1%) with PPG data sampled at 500 Hz and 24 bit resolution. The processing
includes computing the second derivative from the raw samples, their scaling, and five
types of grouping scheme to reduce the intra-zone redundancy. Additionally, the tech-
nique applied an encryption technique based on choosing an encryption key and XOR
Huffman symbol
compression
FIGURE 3.18
Compression flow for lossless PPG compression using Huffman coder.
86 Health Monitoring Systems
PPG data
N Zone = Y
complex
?
‘Hard’ thresholding on
‘delta’ values
Selective biasing
FIGURE 3.19
Compression flow of zonal-complexity–based PPG encoder [50].
e(n) = x( k + 1) − x( k ). (3.30)
To mitigate the slope overload effect, the step size (resolution, or Δ1) of the analog to digital
converter (ADC) was adapted using adjustment of the reference voltage (Vr), represented as
Vr
∆1 = , (3.31)
2n
where n (the number of bits) was reduced from 16 to 10. The compressed data was acquired
in a PC using Bluetooth communication, achieving a CR of 41 with a low PRD value.
Among the transform-domain methods, the first work described the use of Fourier
series analysis for PPG compression [55]. At first, the cardiac cycles are extracted from
single-channel PPG record, and then Fourier series expansion is applied as follows:
FIGURE 3.20
Compression flow diagram for band-limited estimation-based PPG compression.
The signal is reconstructed with reduced number of coefficients (nominally 50% of total
number of coefficients), allowing some reconstruction error. This method reports a data
compression factor on 12 and also simultaneously reports a motion artifact reduction by a
factor of 35 dB.
An improved technique employing PPG compression based on Fourier series is
reported recently [57]. The signal flow diagram is shown in Figure 3.20. The technique is
based on the key concept that the PPG signal frequency content is highly person specific
and computing the upper and lower limits of frequency band can significantly reduce
the redundancy as well as the computation time for Fourier series expansion. It adap-
tively band-limits the signal components based on frequency content of contiguous over-
lapping PPG segments to determine the upper and lower cutoff frequencies. The systolic
peak indices provide an estimation of average heart rate, which determines the lower
cutoff. The upper cutoff is decided on choosing the 3rd to 4th harmonics of the funda-
mental. Each block is then decomposed to obtain the expansion coefficients, which are
selected based on band limit estimation. The coefficients are scaled based on energy
retention criteria, and bits are assigned to quantize them to form a binary significance
map. This work reports a CR of 36 with a PRD below 4% with PPG data collected at 16 bit
resolution.
Quality-controlled PPG compression is reported in [56]. The technique adopts
cycleby-cycle compression by segmenting the PPG data array into the constituent beats
and forms a 2-D matrix, similar to [48]. PCA decomposition of the covariance matrix of
beat-segmented data is done to get PC scores and eigenvectors. The error control is per-
formed by recursive selection of PCs and their quantization level to limit the PRD and
MAX at certain predetermined values while reconstructing the data. The compression
of PC scores and eigenvectors were performed using lossless delta encoder. The work
also reports effect of HR variability as well as noise due to motion artifacts (MAs) on the
compression parameters.
few attempts have also been made to acquire, transmit, monitor, and process MBioSigs
such as ECG, PPG, ballistocardiogram (BCG), electromyogram (EMG) signals, respira-
tion, and body temperature [61]. Approximately 1.45 MB of computer memory is required
to store only 12-lead ECG data of 1 min duration sampled at 1 kHz rate and with a 16 bit
resolution, which is large enough, and the size keeps increasing with time and with
addition of other biosignals. However, the area of compression of MBioSigs still remains
underexplored [62]. In this section, an MBioSigs compression algorithm along with its
MATLAB code is dicussed. ECG is considered as the most important medical signal,
and PPG is considered as the second most important one. Hence, only MECG and PPG
signals are considered here. Four standard ECG leads (lead III and the augmented limb
leads aVR, aVL, aVF) out of 12 are linearly related to lead I and II, and hence these four
leads are excluded from processing. Henceforth, MECG refers to eight ECG leads (I, II,
V1–V6) in this section.
First, both the MECG and PPG signals are denoized. The clinical bandwidths of ECG
and PPG signals are considered to be within 0.5–100 Hz and 0.5–15 Hz, respectively [62,63].
Hence, a bidirectional Butterworth band-pass filters are used for denoizing these signals.
A notch filter is also used to remove the 50/60 Hz power-line noise from MECG.
Next, each lead of MECG and PPG is placed alongside to form a 2-D array or matrix,
with the number of samples in each ECG lead and PPG are equal. The structure of the
matrix is shown below.
Now, the matrix is decomposed using singular value decomposition (SVD) technique.
SVD is a widely used technique of matrix factorization [64]. It factorizes a matrix into
the product of another three matrices: a diagonal matrix (S), an orthogonal matrix (U), and
the transpose of an orthogonal matrix (V). Therefore, we can write
Ar × c = U r × r Sr × c VcT× c , (3.33)
ET = σ 12 + σ 22 + σ 32 + + σ c2 , (3.34)
Data Compression in Health Monitoring 89
where σ 12 ≥ σ 22 ≥ σ 32 ≥ ≥ σ c2 ≥ 0.
A very striking feature of SVD is that it is able to extract the fundamental structural
modes of a composite signal, which can be utilized to analyze a signal exhibiting quasi-
periodic nature such as ECG. If the correlation of the data present in a matrix is high, then
most of the energy of the data is expected to be concentrated over the first few singular
values. Hence, the original data matrix can be approximated with a low and acceptable
level of distortion by discarding the rest of the singular values. From Equation (3.34), it
can be noted that the singular values (σ) are arranged in the decreasing order of mag-
nitude. Therefore, truncating the singular values to a lower number helps in achieving
data compression. The compression performance is inversely proportional to the singu-
lar value truncation factor (β). If we consider only eight ECG leads and the PPG signal,
then β could be varied from 1 to 9. Compression performance is high at a low value of
β and vice-versa. Also, the error between the original and reconstructed signals is high
at a low value of β and vice-versa. The truncation factor of the singular values solely
depends on the tolerable level of data loss upon reconstruction. After truncation, the
truncated U, S, and V matrices are to be compressed using either lossless or near-lossless
compression techniques. The technique described in Section 3.4.1 could also be used.
During reconstruction, first, U, S, and V matrices are to be decompressed using an
approach, which is the reverse of the compression technique, and finally, the decom-
pressed U, S, and V matrices are multiplied using Equation (3.14) to reform the MBioSigs
matrix. It is expected that the module would perform better when MBioSigs are collected
simultaneously from the same subject and at the same sampling rate.
MATLAB® code
% Plot the columns of A and A_rec matrices to verify the original and
% reconstructed signals.
If the coefficients of the ‘Ssmaller’, ‘Usmaller’, and ‘Vsmaller’ matrices of the above-written
MATLAB code could be compressed and reconstructed in a lossless or near-lossless
manner, then the reconstructed MBioSigs would exactly look like as shown in Figures 3.21
and 3.22 at β = 4 and 9, respectively.
90 Health Monitoring Systems
FIGURE 3.21
(a) Original MBioSigs, (b) reconstructed MBioSigs at β = 4, and (c) error between the signals shown in (a) and (b).
FIGURE 3.22
(a) Original MBioSigs, (b) reconstructed MBioSigs at β = 9, and (c) error between the signals shown in (a) and (b).
biosignal compression algorithm is to convert the original biosignal into some other
domain, then discard the comparatively less significant coefficients using a threshold-
based technique, and finally, encode the rest of the coefficients in a lossless or near-
lossless fashion such as Huffman coding, RLE. Therefore, the distortion or loss, which
enters into the signal upon reconstruction, is mostly due to the transformation and
thresholding operations. In the cases where the remaining coefficients after thresh-
olding out the less significant ones are compressed using lossless compression meth-
ods, it is possible to estimate the amount of distortion which would be present in the
reconstructed signal accurately, and near-perfectly if the coefficients are compressed
using near-lossless techniques. PRD, PRDN, or any other metrics, which are discussed
in Section 3.3.1, could be used as the measures of distortion. WEDD could also be used
but only for ECG.
MATLAB code of such a quality-guaranteed ECG compression algorithm using DCT
and taking PRD as the quality controlling metric is given below, and the flow chart of
the algorithm is shown in Figure 3.23. Please note that the signal MUST be filtered for the
elimination of high- and low-frequency noises prior to compression. Here, in the MATLAB
code, it has been assumed that the ECG signal has been refined using a fourth-order bidi-
rectional Butterworth band-pass filter having lower and upper cutoff frequencies 0.5 Hz
and 100 Hz, respectively. The frequency response of such a band-pass filter is shown in
Figure 3.24.
FIGURE 3.23
Flow chart of the DCT-based quality-guaranteed ECG compression algorithm.
92 Health Monitoring Systems
FIGURE 3.24
Frequency response of a 4th-order Butterworth band-pass filter (lower and upper cutoff frequencies 0.5 and
100 Hz, respectively).
MATLAB® code
FIGURE 3.25
Original (MIT-BIH arrhythmia database, record no. 100) and reconstructed ECGs plotted on top of each other
for different values of UDPRD.
If the coefficients of the ‘temp’ array of the above written MATLAB code could be
c ompressed and reconstructed in a lossless manner, then the original and reconstructed
ECGs would exactly look like as shown in Figure 3.25 for different values of maximum-
tolerable-PRD or user-defined-PRD (UDPRD).
The time complexity of the algorithm depends on the number of samples present in
the signal as well as the chosen value of PRD, and in the MATLAB code, the number of
DCT coefficients are reduced based on their length; i.e., initially only one DCT coefficient
is taken, and then the number is increased one by one until the PRD value comes down
below the predefined measure; other thresholding methods could also be used.
ECG records, collected at 250 Hz sampling. However, till date, the use of this database is
comparatively less, probably due to the unviability of annotations.
For multilead ECG compression performance, the researchers have used PTB Diagnostic
ECG database, popularly known as ptddb, which contains 15 channel (12 standard leads and
3 Frank vectorcardiographic leads) ECG records containing various cardiac abnormalities.
For research on PPG compression, until now there is no established benchmark database
available. Some researchers have used Multiparameter Intelligent Monitoring in Intensive
Care (MIMIC) database which is a collection of 5–6 critical parameters like ECG, respira-
tion, PPG, etc.
3.7 Conclusion
In this chapter, some of the major approaches used in ECG and PPG compression in
the last decade are described. Biomedical signal compression has been an active area of
research for the last 40 years. Until now, numerous methods have been successfully imple-
mented for ECG data compression, while PPG compression is a relatively new area of
research, mainly due to its wider application in cardiovascular health monitoring being
exercised in last two decades. Initial application of ECG compression was mainly confined
to continuous data recorders. However, with expansion of ambulatory monitoring and
wireless sensor networks in healthcare, the need for biosignal compression has gradu-
ally been expanded in continuous health monitoring using wearable biomedical systems.
Advent of low-power microcontrollers with limited on-chip buffer has made it possible to
implement lightweight compression algorithms for real-time systems [65]. Compressed
sensing is one such technique that has been utilized [66,67] for energy efficient wearable
biomedical systems.
There is a trend of imaging-based diagnosis (e.g., echocardiography) replacing the con-
ventional paper-based records for more in-depth information. However, ECG and PPG still
continue to be the low-cost and cost-effective diagnostic tools in the coming years. Thus,
biomedical signal compression will continue as one of the potential of areas of research.
Acknowledgments
Rajarshi Gupta acknowledges Dipankar Das and Subhajit Das, M. Tech students of
Instrumentation & Control Engineering, 2016, from University of Calcutta for the test
results of Figure 3.17 of this chapter.
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Data Compression in Health Monitoring 97
Domenico Balsamo
Newcastle University
Saptarshi Das
University of Exeter
CONTENTS
4.1 Energy-Constrained IoT Systems....................................................................................... 99
4.1.1 Introduction............................................................................................................... 99
4.1.2 EH Principles........................................................................................................... 100
4.1.3 EH IoT System Design............................................................................................ 102
4.1.4 IoT Wireless Protocols............................................................................................ 105
4.1.4.1 Bluetooth Low Energy............................................................................. 106
4.1.4.2 IEEE 802.15.4.............................................................................................. 107
4.1.4.3 Zigbee Standard....................................................................................... 107
4.1.4.4 Wi-Fi........................................................................................................... 108
4.1.4.5 LoRa and SigFox....................................................................................... 108
4.2 Biomedical Signal Processing and Machine Learning Challenges in IoT Research.....109
4.2.1 IoT Network for Healthcare................................................................................... 109
4.2.2 IoT-Based Healthcare Services.............................................................................. 109
4.2.3 IoT-Based Healthcare Applications...................................................................... 110
4.2.4 Security in IoT-Based Healthcare......................................................................... 110
4.2.5 IoT-Based ECG Signal Processing......................................................................... 110
4.2.6 IoT-Based Machine Learning Method for Heart Disease Detection............... 111
4.2.7 Community-Based IoT Personalized Healthcare System................................. 112
4.2.8 IoT-Based Biosignal Compression........................................................................ 113
4.3 Recent Research Trends in IoT-Based Healthcare Monitoring..................................... 113
4.4 Industrial IoT Data Analytics: A Case Study.................................................................. 115
4.5 Conclusion........................................................................................................................... 118
References...................................................................................................................................... 118
99
100 Health Monitoring Systems
standard communication protocols, such as internet protocol (IP). This provides services
to end users and helps to engineer new solutions to societal-scale problems [1].
Fundamental to realizing this are networked devices, collections of tens to thousands
of nodes, each of which are organized into a part of a larger cooperative network. Such
devices encompass ultralow-power embedded devices used as IoT sensing devices,
through to the low-power mobile platforms which form IoT edge devices and data aggre-
gators. Each IoT device is typically equipped with sensors to detect physical phenomena
and gather information about their environment; actuators to take actions on the moni-
tored environment; diverse computing units, such as microcontrollers (MCUs), central
processing units (CPUs), graphics processing unit (GPU), digital signal processors (DSP)
etc., to process data; memory for data storage; and finally, radio frequency (RF) transceiv-
ers for communication.
For example, smart healthcare is fuelling interest in IoT applications which require
embedded sensors and actuators in patients to monitor physiological status. These are
typically utilized to collect and process data and gather useful information to make suit-
able actions and decisions regarding the patients [2].
Technological advances and changes in the social perception of technology are also fuel-
ling interest in flexible, wearable, and implantable devices. These target a range of applica-
tion domains, including fitness, assisted living, and healthcare. For reasons of installation
ease, location, or aesthetics, many of the networked sensing devices which underpin these
applications do not have access to a wired electricity source and instead rely on batteries
as their power source. Battery-powered devices inevitably experience market demands
requiring long lifetime and small physical dimensions and weight.
While different types of wireless communication technologies and protocols have
already been put in place to support IoT systems, providing reliable power supply for
autonomous devices deployed in remote locations still remains a major challenge. Scaling
CMOS device geometry has far outpaced the scaling of energy densities in batteries, mean-
ing that power supply is often the largest and most expensive part of IoT devices. In fact,
the high cost and disruption associated with replacing the batteries is limiting the large-
scale deployment of IoT systems [3].
As a result, research effort has been initiated toward replenishing batteries with charges
using the principle of energy harvesting (EH), defined as the process of harnessing electri-
cal energy from alternative sources such as light, wind, heat vibration, and movement, and
using it to power IoT sensing devices [4]. This chapter provides a comprehensive review on
IoT systems with focus on energy-constrained IoT devices. In Section 4.1.1, EH principles are
presented followed by a discussion on EH IoT system design (Section 4.1.3). This chapter is
concluded with a discussion on standard IoT protocols and their properties in Section 4.1.4.
4.1.2 EH Principles
In this section, EH principles are discussed with emphasis on the technologies that are
typically used in EH IoT system design.
EH simply refers to exploitation of any source to create electricity. It is highly benefi-
cial for widely distributed and hard-to-reach networks, as ambient resources in a device’s
environment are utilized to extract energy. There are a variety of EH techniques currently
in use, whereby an energy budget is autonomously (re)constituted to replenish the storage
without requiring maintenance or human supervision. In general, EH mechanisms are
commonly categorized into five groups, namely mechanical, thermal, fluid flow, radiant,
and wireless EH [5].
Health Monitoring Based on IoT 101
Solar power, harnessed using the principle of the photovoltaic (PV) effect, is widely used in
rural sites to generate electricity. For indoor applications, specialized PV cells, better suited
to diffused lights, convert artificial propagation into usable electrical energy. However, this
energy is restricted by size of the PV cell, which limits PV utilization, especially in indoors
due to the small form factors of sensor nodes and the limited operation area. Similar to solar
EH, wind and hydro-power, i.e., flow energy, offers an alternative to operate wireless IoT
devices and larger systems, in a self-sufficient way. Any mechanical movement (e.g., vibra-
tion, pressure variation) can be converted into a reliable energy source, thanks to piezoelec-
tric materials. Similarly, thermoelectric generators (TEG) take advantage of high temperature
gradients, assuring battery-less operation of emerging wireless devices. TEGs have been
extensively utilized in diverse domains such as wearable electronics. In urban areas on the
other hand, a recent technology, in which the RF signals in abundance are exploited to pro-
duce energy, has started replacing the conventional methods of power provisioning.
A specialized version of EH, namely wireless EH or wireless power transfer (WPT),
which refers to exploitation of RF signals, enables remote energization of wireless devices
at a distance. The recently emerged WPT technology therefore stands highly promising
to mitigate battery constraints and eliminate the need for maintenance. It has been exten-
sively studied to be put into practice for sustaining IoT services in diverse domains [6].
As shown in Table 4.1, the power density of the exploitable resources is typically low
and varies depending on the harvester efficiency, deployment location, and many other
unforeseeable and uncontrollable factors. At the same time, the workload profile of IoT
TABLE 4.1
Existing EH Techniques [4,7,8]
Category Source Polarity Efficiency Harvested Power Characteristics
Radiant Light DC ~10%–30% 100 mW/cm 2 Not always available, hard
(outdoor, solar) to deploy outdoor
100 μW/cm2 (office, Requires maximum power
diffused light) point tracking
Fluid flow Wind AC ~39% 35 μW/cm2 Available day and night but
(@<1 m/s, wind) has fluctuating density
~41% 3.5 mW/cm2 Requires impedance
(@8:4 m/s, matching
generator)
Mechanical Vibration, human AC ~25%–50% ~800 mW (machines Compact configuration and
activity – kHz) lightweight
~4 μW/cm3 (motion High fluctuations at the
– Hz) output
Requires rectifier and
step-up/step-down
circuits
Thermal Thermal gradient DC ~5% ~1–10 mW/cm2 Low maintenance cost,
(industrial) scalable
Requires efficient heat
sinking
Wireless RF AC ~50% 0.1 μW/cm2 (GSM, Allows mobility, abundant
900 MHz) in urban areas
1 μW/cm2 (Wi-Fi, Requires impedance
2.45 GHz) matching
102 Health Monitoring Systems
systems is typically ‘bursty’ in nature, meaning that they remain in a low-power mode
or ‘sleep’ mode for most of the time, waking up for taking measurements (periodically
or when something happens), performing computation, and communication. This mis-
match between energy availability and power utilization has to be taken into account at
the time of design. In the next section, different approaches and techniques are presented
to address this research challenge.
FIGURE 4.1
Example outputs from EH systems: power from three PV modules located in three different sites over a day,
voltage from two wind harvesters over one ‘gust’.
Health Monitoring Based on IoT 103
FIGURE 4.2
MPPT system architecture for energy-neutral systems.
Additionally, these systems also require a maximum power point tracking (MPPT) unit
to maximize power extraction under variable environmental conditions. For example,
MPPT techniques are used with systems powered by PV modules to continually maxi-
mize the output power which depends on solar radiation and temperature. As shown in
Figure 4.2, the MPPT unit typically relies on a switching-mode DC–DC converter, a maxi-
mum power point (MPP) controller that monitors the voltage and current, and controls the
charging current to the energy storage, which in turn provides energy to the computing
unit. This additional energy storage also decouples the load from the EH source dynamics,
managing the mismatch between the maximum power point current and the load current.
This system architecture enables energy-neutral operation, which attempts to balance
the long-term energy consumption against the harvested energy [9]. A system is energy-
neutral over a period of time T, if the energy stored in the storage, after this time, is greater
than or equal to the initial energy stored. This can be expressed as
T T
∫ 0
Ph (t) dt
∫
0
Pc (t) dt , (4.1)
where Ph (t) is the instantaneous power harvested at time t, and Pc (t) is the power con-
sumed by the computing unit (e.g., an MCU) at that time. Equation (4.1) is met with if
E E0 , (4.2)
where E is the energy stored in the buffer after time T and E0 is initial energy stored at time
0. Energy-neutral operation in IoT devices is typically achieved if a desired functionality
can be supported perpetually (i.e., infinite lifetime), by balancing the consumed energy
against that harvested over T.
Energy-neutral systems have been largely used in domains such as wireless sensor net-
works (WSNs), where Equation (4.2) is met with by adaptively adjusting the workload and
hence the power consumption [10,11]. Methods for achieving workload adaptation include
adjusting device activity (e.g., changing sample/transmit duty cycles [12]), adaptive sleep
[13], or participation in network activity (e.g., packet routing) [14].
For IoT devices that have constrained dimensions, it is desirable and convenient to
power them directly from the harvester without any additional storage other than decou-
pling capacitance C. In this application domain, attempting to use a storage inevitably
leads to high losses due to power harvesting costs, self-discharge, and converter inefficien-
cies. However, this makes systems susceptible to frequent power interruptions and resets
caused by the intermittent source.
104 Health Monitoring Systems
This issue inherently promotes multiple sources, i.e., hybrid, EH procedures, where
numerous systems have already been equipped with distinct, yet collaborative EH mecha-
nisms to overcome the ongoing constraints [8]. Extracting energy from multiple resources
reduces the variance on a single EH’s output enabling power neutrality and thus contrib-
utes to non-intermittent behavior [15]. Power neutrality refers to the possibility of adjusting
the system’s performance dynamically such that instantaneous harvested power matches
the instantaneous power requirement, negating the need for additional storage. This can
be achieved by tracking the available harvested power [16].
However, power-neutral operation is applicable only when the average harvested power
is comparable (i.e., equal or bigger) to the required power for sustained system operation
(gray area in Figure 4.3). This can be formalized as
If this is the case, the computing unit can be directly coupled with the variable EH source as
shown in Figure 4.4. Here, the processing element tracks the instantaneous harvested power
by monitoring the voltage across the decoupling capacitance C (shown by dotted arrow).
In order to achieve power neutrality, different control schemes are used to adjust the
power consumption dynamically depending on the type of processing unit. In smaller-
sized single-core MCUs, dynamic frequency scaling (DFS) can be used in which the clock
frequency of the processing element is adjusted to the specific power constraints, whilst
with multicore systems more sophisticated techniques exist, such as dynamic voltage and
frequency scaling (DVFS), where the supply voltage of the processing unit is also adjusted,
and core hot-plugging technique to dynamically switch cores on or off. Different runtime
approaches to manage and adapt these controls have been proposed for power-neutral
management on both single and multicore systems [17,18].
FIGURE 4.3
An EH system under power neutrality, showing the available harvested power and the consumed power.
FIGURE 4.4
Storage-less architecture for power-neutral systems.
Health Monitoring Based on IoT 105
FIGURE 4.5
Typical communication between IoT devices.
106 Health Monitoring Systems
FIGURE 4.6
An example of mesh network typically used for WSNs.
TABLE 4.2
Examples of Available Wireless Transceivers for IoT Devices
Current Consumption (mA)
Technology Transceiver Tx Rx
Bluetooth Texas Instruments CC2640b 6 (0 dBm) 6
Zigbee Texas Instruments CC2630b 6 (0 dBm) 6
Wi-Fi Texas Instruments CC3200b 229 59
LoRa Sentech SX1272 28 (+13 dBm) 10
of meters (i.e., Wi-Fi technology, IEEE 802.11); and wireless wide area networks (WWANs)
which offer citywide coverage range, including cellular networks [23].
Another important aspect with wireless networks is their topologies, i.e., star or mesh
type. In star networks, each IoT sensing device can communicate directly with the data
aggregator using a point-to-point communication, whilst a mesh network refers to a rich
interconnection among multiple sensing devices and one or more data aggregators. Mesh
topologies are necessary when the protocol adopted is, for example, a short-range protocol
and the total coverage area is bigger than this range. As a result, the network typically relies
on a multi-hop structure (shown in Figure 4.6), where IoT sensing devices can communicate
with their neighbors, increasing the overhead and thus having an impact on the energy
efficiency [24]. In the following, some existing and forthcoming protocols are discussed,
considering their coverage range, network topology, and data rate (shown in Table 4.2).
BLE employs frequency hopping over 37 channels for (bidirectional) communication and
3 for (unidirectional) advertising, with a bit rate of 1 Mbps. For the discovery mechanism,
slaves send packets to the master using these advertising channels, which are scanned by the
master. However, its star topology limits network coverage range and precludes end-to-end
path diversity. In contrast to this, other competing technologies, such as IEEE 802.15.4-based
standards (like Zigbee), overcome such constraints by supporting mesh network topology.
4.1.4.2 IEEE 802.15.4
The IEEE 802.15.4 is a standard that specifies the physical layer (PHY) and the sub-layer for
Medium Access Control (MAC) for low-data-rate WPAN (LR-WPAN) [27]. This standard
has been intensively used for IoT applications for healthcare, wellness, and fitness. IEEE
802.15.4-based technologies (e.g., Zigbee Health Care) are typically employed for these IoT
applications due to their low power consumption and cost, their ability to manage large
number of nodes, and their operability among multiple IoT platforms. They also provide
high-level security, encryption, and authentication services.
IEEE 802.15.4 supports three frequency channel bands and utilizes a direct sequence
spread spectrum (DSSS) method. Based on the used frequency channels, the physical layer
transmits and receives data over three data rates: 250 kbps at 2.4 GHz, 40 kbps at 915 MHz,
and 20 kbps at 868 MHz. IEEE 802.15.4 MAC utilizes the carrier sense multiple access with
collision avoidance (CSMA/CA) protocol to reduce potential collisions.
IEEE 802.15.4 defines two types of devices:
• Full Function Device (FFD): high-cost devices with high power consumption (typ-
ically main powered) and with extended functions and services
• Reduced Function Device (RFD): low-cost devices with low power consumption
(typically battery-powered or supplied by EH sources) and limited functionalities.
There are many IEEE 802.15.4-based standards, such as Zigbee, 6LoWPAN, IPv6, Thread,
etc. Some of them are still under development and standardization. In the following, some
insights are provided on Zigbee standard which is the most adopted one for healthcare
applications.
4.1.4.3 Zigbee Standard
Zigbee defines a complete open-global standard for reliable, cost-effective, low-power,
wirelessly networked products addressing monitoring and control. Zigbee is built on the
top of the IEEE 802.15.4 standard by adding the network, security, and application frame-
work layers [28,29]. Zigbee’s typical coverage ranges up to hundreds of meters and defines
the specification for a low-rate mesh network topology.
Specifically, the network layer includes some important networking functions such as
the following:
Additionally, Zigbee standard defines three types of logical devices (coordinator, router,
and end device):
• Coordinator is the main governor of the network, with the capability of starting
the network, setting the security level, and enabling the relevant actions associ-
ated to full functioning of the network.
• Router (FFD-associated device) establishes the connection from the coordinator to
other router or from router to end devices, enabling a wide range of communication.
• End device (RFD-associated device) is a low-powered device with reduced func-
tionalities; i.e., they never collect information from other networks.
Zigbee standard has been extensively used for IoT applications related to smart health,
wellness, and fitness for improving personal healthcare. On this purpose, the Zigbee
Alliance has joined forces with the Continua Health Alliance, a non-profit, open industry
coalition of the finest healthcare and technology companies collaborating to improve the
quality of personal healthcare. Zigbee Health Care defines a global standard to enable
secure and reliable monitoring and management of non-critical, low-acuity healthcare
services targeted at chronic disease, aging independence and general health, wellness,
and fitness. Leading healthcare and technology companies are supporting the develop-
ment of Zigbee Health Care, including Motorola, Phillips, NXP Semiconductors, and RF
Technologies [30].
4.1.4.4 Wi-Fi
Wi-Fi is the de-facto standard for WLAN and is based on the IEEE 802.11 standard, which
operates in the 2.4 and 5 GHz ISM bands. Wi-Fi is designed to provide high-speed (i.e., up
to 10 Mbps) wireless links for devices that can access the Internet directly in the range up
to hundreds of meters, via access points (APs) using a star network topology.
Due to the higher power requirements of the transceivers compared to BLE and
Zigbee, Wi-Fi is not yet a reference standard for IoT applications. However, deploying
IoT devices with Wi-Fi standard has a distinct advantage: these devices can utilize the
well-established Wi-Fi APs in buildings and cities, reducing additional costs on new
gateway infrastructure. This is an interesting research area that still requires further
investigation [31].
LoRaWAN, the MAC protocol for WANs, is based on the ALOHA protocol and is ideal
for applications with low-traffic and sporadic communication requirements. One of the
features making LoRa attractive is its energy efficiency for uplink communication while
achieving a long range. In LoRaWAN, the distributed battery-operated sensor nodes send
data directly to an always-on gateway. The energy efficiency comes by low duty cycling of
the main radio transceiver when idle.
Another emerging proprietary low-power WAN technology is SigFox, which uses as
well the unlicensed spectrum in the sub-GHz band. It offers low-power and low-cost
transceivers, enabling IoT devices to achieve long-range communication with gateways
kilometers apart with a star topology.
Such an index captures various aspects of the signal quality and may corrupt the signal
to different extents on a case-by-case basis for a specific wearable IoT device. Based on the
SQI, a signal quality grade (SQG) is proposed as
Good SQI = 0
SQG = Intermediate SQI = 0.5 . (4.5)
Bad SQI > 0
Finally, a sensitivity score has been proposed to compare the quality of the ECG signals
given by
TP
Sensitivity = × 100%, (4.6)
FN+TP
where TP denotes the true positive correctly detected segments and FN denotes false
negatives missed segments. The method has been benchmarked on the PhysioNet and
MIT-BIH arrhythmia databases using standard QRS detector with template matching and
R-peak detectors and sub-band power using higher order moments.
p
logit(p) = b0 + b1X 1 + b2 X 2 + + b7 X 7 = ln . (4.7)
1 − p
Here, p denotes the probability of presence of the dependent variable, i.e., heart disease
(in 0 or 1), and b denotes the respective coefficients for each variable. The odds ratio is
expressed as
exp ( b0 + b1X 1 + b2 X 2 + + b7 X 7 )
p= . (4.9)
1 + exp ( b0 + b1X 1 + b2 X 2 + + b7 X 7 )
The IoT-based health monitoring model has been developed in the Apache Mahout and
elastic MapReduce framework using cloud computing. For each variable, initially, coeffi-
cients, standard error, Z-score, p-value, and odds ratio were calculated. Next, the confusion
matrix was derived, and a more detailed analysis of the classification performance was
made using the following metrics apart from the sensitivity:
TN
Specificity = , (4.10)
FP+TN
Sensitivity
Positive Likelihood Ratio (PLR) = , (4.11)
100 − Specificity
100 − Sensitivity
Negative Likelihood Ratio (NLR) = , (4.12)
Specificity
TP
Positive Predictive Value (PPV) = , (4.13)
TP+FP
TN
Negative Predictive Value (NPV) = , (4.14)
TN+FN
TP+FN
Disease Prevalence (DP) = . (4.15)
TP+FP+TN+FN
Here, TN and FP denote the true negative and false positive rates, and the classification
metrics are all measured in percentage. These measures indicate different aspects of the
receiver operator characteristic (ROC) curve. For continuous monitoring through IoT
devices, using the measures and the important variables, approximate age-based inference
mechanism has also been developed for newborns, children with an age of few months to
years and adults.
( )
SNR = Psignal Pnoise , SNR(dB) = 10log 10 Psignal − 10log 10 ( Pnoise ) , (4.16)
Health Monitoring Based on IoT 113
Here, d0 is the closest in reference distance, determined from measurements close to the
transmitter.
FIGURE 4.7
Word clouds of the titles of recent IoT-based research papers. (Source: Scopus.)
114 Health Monitoring Systems
FIGURE 4.8
Types of recent papers on IoT applications in healthcare. (Source: Scopus.)
this field which are actively growing. Figure 4.8 also shows a bar chart of recently pub-
lished works which shows there were twice more conference papers published over jour-
nal articles, followed by review articles and book chapters.
Similar to exploring the article titles, Figure 4.9 shows the indexed and author’s key-
words in the top 2,000 papers in this active research area. As opposed to the title-based
text analysis, often the keyword-based analysis is capable of capturing finer technical
details or an emphasis of fewer most popular technologies. In both the word clouds, apart
from IoT, the most frequent words were computing, systems, networks, sensor, cloud, etc.
FIGURE 4.9
Indexed and author’s keywords for recent papers on IoT applications in healthcare. (Source: Scopus.)
Health Monitoring Based on IoT 115
FIGURE 4.10
Year-wise citations of recent papers on IoT applications in healthcare. (Source: Scopus.)
which shows the finer details of the computing and communication technologies gradu-
ally increasing over the years. While exploring the citation patterns in this research field,
it is clear that the citations of the papers on IoT application in healthcare jumped down
suddenly from 2010 (Figure 4.10). The gradual decrease in the citation almost follows an
exponential trend, because the citation dynamics is usually a cumulative phenomenon
and as such recent technological advancements take years to get assimilated in industrial
and academic practice. Also, there are more outliers in the papers from 2015 onward which
shows there are few highly impactful papers compared to the citation of average papers
published in the recent years.
Although many significant steps have been taken in the field of IoT-based health moni-
toring, still there are many open research areas that need to be addressed in future. This
includes standardization, IoT healthcare platforms, cost analysis, app development pro-
cess, technology transition, low-power protocol, network type, scalability, continuous
monitoring, new diseases and disorders, identification, business model, quality of service
(QoS), data protection, mobility, edge analytics, and ecological impact. Amongst these
challenges, data protection through resource efficient security, physical security, secure
routing, data transparency, secure handling IoT big data, etc. is very crucial to restrict
illicit access.
FIGURE 4.11
Correlation plot amongst the variables for the industrial IoT dataset.
coefficient for each pair of variables. It is evident that the season and distance variables are
highly interdependent on each other (r = 0.93), followed by pair number vs. area (r = 0.85)
and pair number vs. season (r = 0.84). It is apparent that most of the univariate histograms
are highly skewed and contain multiple modes; therefore the Pearson’s correlation has not
been investigated; rather rank-based correlation measures are better suitable for identify-
ing variable interdependencies. Rest of the variables do not show any strong correlation
pattern as such.
In industrial IoT setting, such statistical analysis is highly relevant. Because of the abun-
dant and cheap IoT devices, it is likely to gather huge amount of information from various
sensors, which can then be represented in compact manner using low-dimensional projec-
tions. Next, we explore an unsupervised dimensionality reduction method applied on this
data. We here employ the t-distributed stochastic neighbor embedding (t-SNE) method for
a low-dimensional representation of this industrial IoT dataset [43]. The commonly used
t-SNE hyperparameters are the distance measures and perplexity parameter. Figure 4.12
shows the 2D projection of the t-SNE components using two principal components (PCs)
with four different distance measures – Euclidean, Mahalanobis, Chebyshev, and cosine
while keeping the perplexity parameter fixed at 30. Next, we explore the effect of perplex-
ity variation (5, 30, 50, 100) on the big industrial IoT data in Figure 4.13 with respect to the
fixed Euclidean distance measure. It is interesting to see that there are very clearly identifi-
able groups or non-overlapping clusters in the original 7D industrial IoT data in terms of
the original features – demand, area, season, energy, cost, pair number, and distance in
Figure 4.11. However, in the corresponding 2D projections in the t-SNE space, there are
many groups, which share similarities with respect to certain distance measures as shown
in Figures 4.12 and 4.13. Therefore, such methods could be applied for visualization and
automated clustering or grouping of large volume of industrial IoT datasets recorded by
different types of biomedical sensors. However, the only concern for such analysis is for
large number of datapoints, t-SNE becomes computationally demanding to calculate the
Health Monitoring Based on IoT 117
FIGURE 4.12
t-SNE plots for the industrial IoT dataset with four different distance measures.
FIGURE 4.13
t-SNE plots for the industrial IoT dataset with four different perplexities.
64-bit Windows PC with 64 GB memory and an AMD Ryzen 7, 3.6 GHz processor. It is also
evident from Figures 4.12 and 4.13 that although the variation of the distance measures
does not have a very clear impact on the cluster formation, an increase in the perplexity
parameter clearly gives rise to fewer number of compact clusters in the projected 2D t-SNE
component space which share some degree of similarity with the original 7D industrial
IoT dataset.
4.5 Conclusion
In this chapter, a comprehensive review on IoT systems with focus on energy-constrained
IoT devices is presented. Specifically, EH principles are first discussed, and then a taxonomy
describing and classifying the landscape of EH computing systems is p resented. This
chapter is then concluded with a discussion on standard IoT protocols and their properties,
presenting the most used standards such as Zigbee, Bluetooth, and LoRa.
Following this, a review of various existing technologies and open challenges of using
IoT technologies in future healthcare services is presented. Amongst these, energy-
constrained IoT technologies and IoT data processing using signal processing and
machine learning techniques are especially highlighted. It is apprehended that in future
many more healthcare service industries will pose their IoT big data analytics challenges
in open competitions like Kaggle which will attract invention of new solution methods
and digital technologies from future computer scientists, mathematicians, and engineers
by collaborative research efforts.
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5
Telemedicine Technology
Jayanta Mukhopadhyay
Indian Institute of Technology Kharagpur
CONTENTS
5.1 Introduction......................................................................................................................... 122
5.2 Core Technology................................................................................................................. 122
5.2.1 Medical Instrumentation....................................................................................... 122
5.2.2 Information Technology........................................................................................ 124
5.2.3 Telecommunication Technology........................................................................... 125
5.2.3.1 Computer Networking............................................................................ 125
5.3 Medical Informatics............................................................................................................ 127
5.3.1 HL7............................................................................................................................ 128
5.3.1.1 Messages.................................................................................................... 128
5.3.2 DICOM..................................................................................................................... 130
5.3.3 RIS and PACS........................................................................................................... 131
5.4 Telemedicine Systems........................................................................................................ 131
5.4.1 Early Telemedicine Systems.................................................................................. 132
5.4.2 Telemedicine Systems at the Dawn of Internet Age.......................................... 132
5.4.3 Modern Day Telemedicine Systems..................................................................... 134
5.5 iMediK: A Case Study........................................................................................................ 136
5.5.1 System Architecture............................................................................................... 137
5.5.1.1 Database Layer......................................................................................... 137
5.5.1.2 Business Logic Layer............................................................................... 138
5.5.1.3 Presentation Layer................................................................................... 138
5.5.1.4 Web Proxy Layer...................................................................................... 139
5.5.2 Implementation....................................................................................................... 139
5.5.3 The Data................................................................................................................... 140
5.5.4 Data Conferencing.................................................................................................. 140
5.5.5 Summarized Display of Medical Information................................................... 141
5.5.6 Specialized Modules.............................................................................................. 141
5.5.7 Interfaces with Mobile Devices............................................................................. 141
5.6 Distributed Telemedicine Systems................................................................................... 143
5.6.1 Two-Tier Server Model........................................................................................... 143
5.6.2 Hybrid Server Model.............................................................................................. 143
5.6.3 Hierarchical Server Model..................................................................................... 143
5.7 Privacy and Confidentiality.............................................................................................. 143
5.8 Conclusion........................................................................................................................... 144
References...................................................................................................................................... 144
121
122 Health Monitoring Systems
5.1 Introduction
Telemedicine is the remote delivery of healthcare services using telecommunication
infrastructure, so that in-person visits of recipients of these services to the care provid-
ers are not required. The services could be in the form of providing clinical care, diag-
nosis, consultation of medical cases, etc. From the very beginning of wired and wireless
communication technology, there were efforts for providing healthcare services from the
distant end. In 1920s in USA, maritime sailors used to get clinical care from the shore
through radio communication. Later in 1960s, NASA performed regular assessments of
the health of astronauts in space from the earth and provided clinical advice whenever
needed. In 1970s, paramedics in sparsely populated regions of Alaska and Canada used
to get training and advice from city hospitals using satellite communication systems. Yet,
for a long time, telemedicine services could not be established in large scale even in devel-
oped countries. There were several reasons for its very restricted use, such as high cost of
technology, low reliability of functional components of data acquisition and telecommu-
nication system, lack of skilled manpower for their daily operation and maintenance, etc.
The scenario changed in the beginning of this century.
The digital revolution in late nineties of the last century made a quantum jump in remov-
ing the technological barriers mentioned above, and since then telemedicine services have
been leaving more and more footprints around the world, not only in advanced countries
[1–3], but also in developing countries like India [4], China [5], Thailand [6], etc. In 2015, the
global telemedicine market was estimated at USD 17,878.7 million,1 and since then it has
been rapidly growing.
5.2 Core Technology
Digital revolution not only made the telecommunication technology reliable and affordable
with increasing capacity of information exchange, but it also brought a new era of infor-
mation technologies with the capability of growing in terms of data acquisition, s torage,
and processing, enabling them to be used as household commodities. Telemedicine is the
result of convergence of all these different technologies, namely medical instrumentation,
information technology, and telecommunication technology. In this section, we briefly discuss a
few aspects and concepts related to these technologies.
5.2.1 Medical Instrumentation
Use of digital technology in medical instrumentation has increased the reliability and
accuracy of diagnosis of several diseases. In many cases, it is not required to have the
presence of a patient during the analysis of data acquired by these instruments. There
are various medical imaging systems and modalities available for capturing radiolog-
ical images of a patient such as X-Ray, Computed Tomography (CT), Magnetic Resonance
(MR), Positron Emission Tomography (PET), etc. These images are available in the digital
form through conversion from analog signal by sampling and quantization. Different types
1 www.mordorintelligence.com/industry-reports/market-entry-telemedicine-industry-in-israel.
Telemedicine Technology 123
5.2.2 Information Technology
Information technology deals with representation, processing, storage, retrieval, com-
munication, and presentation of data. High-end servers supported by back-end relational
database management systems (RDBMSs) are being used for providing various healthcare
solutions. Multimedia systems with advanced graphics hardware and high-resolution
color display monitors are not only capable of displaying images and videos, but also are
used for visualization of realistic images and animations enhancing human perception
and understanding about a process or a phenomenon. There has been rapid advance-
ment in the computing power of these systems as well as in computation techniques for
processing multimedia data, which include text, audio, image, video, etc. Rapid advance-
ment in web technology and data communication infrastructure enables these systems to
exchange information and provide services across the globe. That is why this era of infor-
mation technology is witnessing phenomenal growth in telemedicine services.
For exchanging multimedia data and information, it is desirable to follow an inter-
national standard or format of data representation, thus making the system integration
simpler and effective. These standards specify the syntax and semantics of representa-
tion of data and its content. They also specify how the content should be delivered or
visualized. For storage, transmission, and efficient representation, various standards
for compressing data exist. These standards specify alternative description of the same
content, specially required while compressing multimedia data. Typical examples of these
standards are MPEG for audio; JPEG and JPEG-2000 for digital images; and MPEG-II,
H.263, H.264, etc. for videos. These compression techniques could be lossy or lossless. In a
lossy scheme, the decompressed data is not the exact copy of the original one, but it is very
close to it. The degree of approximation determines the quality of decompressed data. In
the lossless scheme, the recovered data exactly matches the original one. During telemedi-
cine sessions, there are requirements for real-time transmission of audio and video for
conferencing. They are compressed using lossy schemes such as MPEG, H.264, etc. But if
digital images are required to be transmitted for diagnostic purposes, there should not be
any loss of information. In such cases, lossless or semi-lossless (e.g., JPEG-LS) schemes are
used. As lossy compression techniques provide higher compression compared to lossless
techniques, under a resource-constrained environment, they may be used in providing
services. For example, for transmission of data in a low-bandwidth channel, pragmatic
uses of lossy compression schemes (such as JPEG) are observed. In Table 5.1, data rates
required to transmit different compressed multimedia data are shown.
TABLE 5.1
Data Rates for Real-Time Transmission of
Different Compressed Multimedia Data
Multimedia Data Data Rates
Usual data 100 bps–2 kbps
Image 40–150 kbps
Voice 4–80 kbps
Stereo audio 125–700 kbps
VCR quality video 1.5–4 Mbps
3D medical images 6–120 Mbps
HDTV 110–800 Mbps
Scientific visualization 200–1,000 Mbps
Telemedicine Technology 125
5.2.3 Telecommunication Technology
Telecommunication technology involves the transmission of signal through a communica-
tion channel, which could be a coaxial cable, free space, optical fiber, etc. In transmission
of digital data, it is required to convert or encode the data into analog signal or symbols.
At the receiving end, the encoded analog signal is decoded in the form of digital data. To
bring the bandwidth of the transmitting signal within the bandwidth of the communica-
tion channel, it is required to modulate the input signal. At the receiving end, the inverse
operation, called demodulation, is carried out to reconstruct the signal in its original form.
Usually, in a modulation technique, a carrier sinusoidal signal is used whose amplitude,
shifts in frequency, or phase vary in proportion to the input signal, which is referred to as
amplitude, frequency, and phase modulation, respectively.
The voice signal transmitted through analog telephony has a bandwidth of approxi-
mately 4 kHz. Communication links in this system also operate in the same range. That
is why no modulation–demodulation is required in local exchanges of analog telephony.
But, in digital telephony, it is required to convert digital data to analog symbols during
transmission, requiring modulation of signal in the transmitting end and demodulation
in the receiving end. For duplex communication, the device interfacing with the commu-
nication link is required to perform both the tasks, and it is called modem (modulator and
demodulator). A modem acts as an interfacing device between a computer and a communi-
cation link, such as dial-up lines, satellite links, etc. while communicating data.
If the bandwidth of a communication channel is insufficient for accommodating the
signal bandwidth, the receiving end gets a distorted signal, leading to loss of informa-
tion. The bandwidth of a communication link is a key technical feature determining the
quality of data transmission. For example, a real-time television standard video signal
has a bandwidth of about 7.5 MHz. It is not possible to transmit this signal through usual
telephone lines. However low-quality video transmission is possible through communica-
tion links with much lower bandwidth. In the context of digital transmission, bandwidth
is also specified by an equivalent data rate of transmission, expressed in bits per second
(bps). For example, using a modem in an ordinary telephone line, on the average a data
rate of 30 kbps (maximum 57.6 kbps) could be supported. It is not possible to transmit
real-time MPEG-I video at a rate of 1.54 Mbps through this link. On the other hand, a
T-1 telephone link running at 1.54 Mbps could be used for transmission of such a video.
With the advancement of technology, more and more data communication services offer-
ing higher data rates are made available at affordable cost. In this regard, optical commu-
nication links play a major role. They support data rate in ranges of gigabits per second
(Gbps). A few examples of data communication services available from a service provider
are given in Table 5.2.
Apart from wired links, wireless communication is also used for this purpose. With the
rapid advancement of mobile communication technology, mobile telephone services are
increasingly used for data communication. In Table 5.3, the data rates supported by differ-
ent wireless technologies are summarized.
5.2.3.1 Computer Networking
Computer networking connects a group of computers for exchanging information. They
communicate data following some protocols, understood by all the participating nodes.
Here, a computer connected to a network is called a node. A node plays different roles in
this network. For example, it may act as a terminal host to run the service of telemedicine,
or it may perform similar tasks of a telephone exchange for establishing communication
126 Health Monitoring Systems
TABLE 5.2
Different Communication Links
Links Bandwidth
POTS 2.4–57.6 kbps
ISDN (BRI) 128 kbps
T-1 and fractional T-1 384 kbps–1.54 Mbps
DSL 128 kbps–9 Mbps
Primary rate ISDN 1.54 Mbps
T-2 leased lines 6.312 Mbps
T-3 leased lines 46 Mbps
T-4 leased lines 273 Mbps
Broadband ISDN 150–1,200 Mbps
SONET 51.84–4,976 Mbps
TABLE 5.3
Different Wireless Communication Services
Links Bandwidth
GSM 9.6–43.3 kbps
GPRS 171.2 kbps
2G 64 kbps
3G 2 Mbps
4G 50–100 Mbps
Satellite 2.4 kbps–155 Mbps
links between two terminal hosts. For data communication, International Standard
Organization (ISO) has identified seven layers of Open System Interconnection (OSI)
protocols, namely physical, data link, network, transport, session, presentation, and application
layers. Higher l ayers are those which appear later in this sequence. They take care of
communication of data directly related to users, applications, processes, etc., while lower
layers implement the protocols for basic signaling, channel sharing, and routing, being
more integrated with network architecture and topology. There are various types of topol-
ogies for connecting computers. They may be connected through a grid of point-to-point
connections, as it is done in a telephone network. They may share a common channel for
transmission and reception, such as a common coaxial cable. This topology is referred
to as bus topology. In a ring topology, they may form a ring, where each node is connected
to two nodes. They may also be connected to a switch in a star topology. The switch estab-
lishes a link between two communicating nodes like a local loop of a telephone exchange.
Different networking technologies are available depending upon the area of coverage. For
example, for connecting computers in a campus, organization, etc., a local area network-
ing (LAN) is used. On the other hand, wide area networking (WAN) connects computers
situated in distant geographic locations. For LAN, different options are available on bus
architecture (Ethernet and Token Bus protocols), ring architecture (Token Ring protocol),
and tree architecture (nodes acting as switches, e.g., ATM cell switching, Gigabit Ethernet
protocol, etc.). In Table 5.4, data rates supported by different protocols are listed.
Wide area networking (WAN) technologies are available as different services provided
by telecommunication service providers. For example, in conventional telephone lines,
Telemedicine Technology 127
TABLE 5.4
Data Rates Supported by Different LAN Technologies
LAN Technology Data Rate
Ethernet 10 Mbps
Token Ring 4–16 Mbps
Token Bus 16 Mbps
ATM 155 Mbps
Gigabit Ethernet 1,000 Mbps
services such as local loop subscription, integrated services digital network (ISDN), digital
subscriber line (DSL), etc. are available. In some countries, there may exist special data
communication infrastructure such as a ring architecture linking different service stations
in a large area for supporting higher data rates. There are protocols for communicating
data at a very high rate for running services in this kind of specialized infrastructure,
e.g., switched multimegabit data services (SMDS), Frame Relay, Synchronous Time Division
Multiplexing (STDM in SONET), etc.
5.3 Medical Informatics
Medical informatics primarily focusses on representation and computation on medical and
health information. These are needed for design and development of a medical information
system providing healthcare services and business. Different international standards have
been evolved for exchange and management of information related to these areas. These
standards are developed so that the systems designed by different organizations and run-
ning on different platforms could be made interoperable. Let us consider a few example
cases. If a hospital requires to send information and a query regarding a patient to another
referral hospital, the systems at both ends should have a protocol for exchange of informa-
tion. In another situation, for getting payment from an insurance company, the hospital may
require to send relevant information of their services and their charges to them. All these
different types of information related to healthcare services should be sent in a format fol-
lowing a standard of data representation. For exchanging messages and queries among mul-
tiple information systems operating independently, there should be a common language and
a common format for representing patients’ data. Two such standards are very widely used
in medical business world, namely HL7, which is a messaging protocol specifically devel-
oped to exchange health/medical/patient information between information systems, and
the other standard, DICOM, which is used for representing medical images, waveforms, etc.
and exchange of information in radiological imaging systems such as Picture Archiving and
Communication System (PACS). There are also other types of health standards, which focus
on requirements of a particular department or domain. For example, International Statistical
Classification of Diseases and Related Health Problems (ICD9, and ICD10) are meant for
identifying and classifying diseases. The standard Logical Observation Identifiers Names
and Codes (LOINC) is used for denoting laboratory observations, health measurements,
observations, and documents. Another standard Systematized Nomenclature of Medicine –
Clinical Terms (SNOMED CT) targets at defining multilingual vocabulary of clinical terms.
128 Health Monitoring Systems
5.3.1 HL7
Health Level Seven International (HL7), one of several ANSI-certified standards operating
in the healthcare arena, aims at providing standards for the exchange, management, and
integration of data related to healthcare services. It aims at facilitating clinical patient care
and the management, delivery, and evaluation of healthcare services. HL7 focusses on
the interface requirements of the entire healthcare organization. It tries to model different
events that take place in healthcare services. Consider an event of a patient’s admission to a
hospital. The Patient Administration System (PAS) logs the details about demographics of
the patient, and the admission, such as the name of admitting doctor, ward of admission,
financial transactions, etc. Several other systems in the hospital, such as pathology labora-
tory information system or pharmacy system, will require these details at certain stages
of care and services. Hence, this event in PAS may trigger forwarding of a message to
each of the interested systems about the patient. Otherwise, those systems may wait until
the patient needs their service. In any case, the PAS requires to communicate information
about a patient to these systems. Before the introduction of HL7, developers of various
information systems that needed to communicate had to discuss among themselves and
work out a mutually acceptable common format for information exchange for developing
interfaces. This is not only an expensive process, but it is also very time consuming. HL7
provides a common language for all health/medical information systems for exchanging
and sharing of information. Developers need to make their systems HL7 compliant to con-
nect to other systems through requests and responses to queries.
5.3.1.1 Messages
An information system usually creates and sends a HL7 message in response to an event.
Examples of such events are patient admission, discharge, ordering diagnostic tests,
reporting of test results, etc. It may also be a response to a query from another system.
Each message depicts information about that event following a predefined syntax. Each
HL7 message consists of a set of segments, which are its building blocks. Each segment is
uniquely identified by a three-character code followed by a predefined format of specific
fields. The end of a segment is denoted by the end of line (or carriage return) character. Every
message begins with a message header segment (identified by the code ‘MSH’). In a segment,
pieces of related information are kept together. For example, the Patient Identification
(PID) segment identifies a single patient, by the patient’s ID number, name, address, and
date of birth. The fields within a segment are separated by the | (pipe) character, and com-
ponents within a field by the ^ character. The components may further be divided into
subcomponents (with the & character). For specifying a sequence of data values within a
field, the character ~ is used as a separator. These symbols of separators are defined in the
message header of the segment. In HL7 messages, all data are represented by a selected set
of displayable characters. The default is ASCII displayable character set.
Different types of messages are triggered by different HL7 events. Each message type
defines a sequence of segments, providing all the required information regarding that
event. In a message, there are some segments, which are mandatory, and some are optional.
For example, in the event of ‘Admission and Visit Notification’ (ADT message), there has
to be a minimum of four segments, such as Message Header (MSH), Event Type (EVN),
PID, and Patient’s Visit (PV1). But many other optional segments may exist there, describ-
ing information about a patient’s next of kin, disability, allergy, previous diagnosis, etc.
Similarly, in a segment, which is constructed as a sequence of data fields, some of these
Telemedicine Technology 129
fields are mandatory. For an optional but empty field, field separator symbols are simply
repeated. However, the sequence is terminated by the last non-empty field, without denot-
ing subsequent stream of empty fields. For example in the PID segment, there could be as
many as 30 fields. However, only two of them, namely patient’s internal ID and patient’s
name, are minimally required for forming the segment. As these fields occur in third and
fifth places, respectively, a valid PID segment could be as given below:
PID|||2-647009||Ghosh^Kallol
MSH|^~\&|BPHC||SDH|Reg|||ADT^A01|MSG00005|P|2.3 EVN|A01|201605061725
PID|||2-647009||Ghosh^Kallol||19610416|M|||24, Bataram
Lane^^Kharagpur^West Bengal^721302
||(3222)277-2345|(3222)277-2346||S
PV1||E|Emergency||||1234^Hossain^Sirajuddin|||SUR
1. Admit/Visit Notification
1. Message Header
a. From: BPHC
b. To: SDH
2. Event
a. Date: 2016-05-06
b. Time: 17:25
3. Patient Identification
a. Internal Patient ID Number: 2-647009
b. Family Name: Ghosh
c. Given Name: Kallol
d. Birth Date: 1961-04-16
e. Sex: M
f. Street Address: 24, Bataram Lane
g. City: Kharagpur
h. State or Province: West Bengal
i. Zip or Postal Code: 721302
j. Phone Number Home: (3222)277-2345
k. Phone Number Business: (3222)277-2346
l. Marital Status: S
4. Patient Visit
a. Patient Class: E
b. Point of Care: Emergency
c. Attending Doctor’s ID Number: 1234
d. Family Name: Hossain
130 Health Monitoring Systems
5.3.2 DICOM
The DICOM standard came out of the collaboration between American College of Radio
logy (ACR), American College of Cardiology (ACC), American Society of Echocardiography
(ASE), European Society of Cardiology (ESC), American Society of Nuclear Cardiology
(ASNC), and the National Electrical Manufacturer’s Association (NEMA). Previously the
standard was called ACR/NEMA. Later, with several revisions, it was renamed DICOM
in 1991. In its present release, there are 21 parts in DICOM conformance statement cover-
ing various issues such as information object definitions, service class definitions, data
structures and encoding, data elements dictionary, message exchange protocol, network
communication support for message exchange, media storage and file format, grayscale
standard display function, security and system management profiles, web services,
application hosting, imaging reports using HL7 Clinical Document Architecture, trans-
formations between DICOM and other representations, etc.
A DICOM file has a preamble of 128 bytes, a prefix of 4 bytes denoting ‘DICM’ as
identification to DICOM standard, and a list of data elements. Each data element encodes
related information about a patient or metadata information about the representation and
organization of data in the file. There is a special data element called ‘Transfer Syntax’,
which is used for describing how the fields in a data element are to be interpreted. A data
element, may either be represented by explicit value representation or by implicit value rep-
resentation. In the first case, data types of the values are explicitly specified. In this case,
a data element has four fields, namely tag (4 bytes), value representation (VR of 2 bytes),
value length (VL of 2 bytes), and value field (VF of an even number of bytes as mentioned
in VL). For implicit value representation, the default data type, as defined in the standard,
is assumed, and it is not described in the data element. Hence in this case, it requires three
fields, namely tag (4 bytes), VL (4 bytes) and VF. A tag denotes the identity of a variable,
whose value is stored in the element. The tag is encoded by a 2 bytes group code and a
2 bytes element code. For example, the variable, a patient’s name, is under the group patient,
and it is encoded as (0x0010, 0x0010). In this case, the group, patient, is identified by its
group code (0x0010), and the variable, patient’s name, is identified by the element code of that
group (0x0010). A few examples of some other groups are shown in Table 5.5.
TABLE 5.5
Typical Groups in DICOM Standard
Group Name Hex Encoding
File meta information 0x0002
General series information 0x0008
Patient information 0x0010
General study information 0x0020
Image information 0x0028
Image pixel data 0x7FE0
Waveform information 0x5400
Equipment information 0x003A
Telemedicine Technology 131
TABLE 5.6
Some Vital Tags for Rendering Images in DICOM
Standard
Tag Description Hex Encoding (Group, Element)
Transfer syntax tag (0x0002, 0x0010)
Samples per pixel (0x0028, 0x0002)
Number of frames (0x0028, 0x0008)
Number of rows (0x0028, 0x0010)
Number of columns (0x0028, 0x0011)
Number of bits allocated (0x0028, 0x0100)
Number of bits stored (0x0028, 0x0101)
Pixel data (0x7FE0, 0x0010)
A few examples of elements relevant for rendering an image in DICOM format are also
listed in Table 5.6.
Different types of services are also specified in DICOM standard. They are called service
classes. These services could cater to storage, query, and retrieval of data. It may be for
printing images. There are service classes for managing different schedules. These are
known as SOP classes, and each of them has a unique identifier.
5.4 Telemedicine Systems
From the early 20th century, there have been various initiatives on running telemedicine
services using the state-of-the-art telecommunication technology of those years. Many of
these systems were built up to provide the proof of concepts and deployed under vari-
ous pilot projects. Even today, various such pilot projects are being reported. However, in
many hospitals and healthcare organizations, systematic and regular use of telemedicine
services has been taking place.
132 Health Monitoring Systems
FIGURE 5.1
Integration of RIS and PACS.
technology at a much lower cost in comparison to earlier systems. These are the years of
preinternet or early internet era, when web technology was not yet matured. A few such
examples are given below.
In Japan, a telemedicine network [12] was created for the purpose of education and train-
ing of medical professionals. The project started in 1994, when a system was built up by
connecting National Cancer Centers in Tokyo and Chiba, separated by a distance of 30 km.
The services were run over an optical leased line with 6 Mbps data rate. Later the services
were catered through an upgraded link supporting 18 Mbps data transfer rate using ATM
protocol. Additionally an experimental B-ISDN link with 156 Mbps data rate was used to
enhance the quality of services. In later years, the network was expanded to include as
many as 14 regional cancer centers using frame relay communication services [12]. The
system was facilitated by TV conferencing. The participants could discuss sharing a still
image of HDTV resolution. Regular conferences on various areas of healthcare, such as
nursing, radiology, oncology, pathology, etc., were held using this network with an annual
participation of more than 15,000 people.
In Lincolnshire, U.K., in 1996, telemedicine had been regularly used for handling a ccident
and emergency cases [13]. In this system, specialists from a District General Hospital
(DGH) at Boston were used to provide consultation to two Minor Injury Units (MIU) at
Skegness and Johnson via an ISDN line supporting 128 kbps data rate. The system had a
desktop videoconferencing unit on a Pentium PC and used the store and forward technol-
ogy for image transfer. For digitizing patient’s records, it used scanners, digital cameras,
etc. The system mostly handled orthopedic emergency cases. Consultation and treatment
of patients predominantly suffering from fractures, sprains, strains, and laceration were
carried out, using online transmission of X-Ray images.
Demonstration of viability of telepathology using the store and forward technology [14]
was reported from Korea. At the Samsung Medical Center, Seoul, pathological slides were
captured by a microscope attached with a digital camera using a phototube adapter. These
images were sent to two different places, namely Korea University Hospital, Seoul, Korea,
and John Hunter Hospital, Newcastle, Australia. In addition, the slides were also sent to
an independent pathologist. File sizes of images were reduced to a great extent by com-
pressing them using the JPEG compression scheme at moderate quality. A high degree of
concurrence of pathological reports with the independent reports by directly viewing the
slides was reported in this experimentation. 95% concurrence in diagnosis was achieved
with those from Korea University Hospital, and the reports from John Hunter Hospital
concurred in 97% cases.
A pilot project on remote diagnosis of pediatric echocardiograms [15] using real-time
transmission on an ISDN link was reported from the Duke University Medical Center,
Durham, North Carolina. The center provided expert opinions to nine small centers
equipped with a USG imaging system and a cardiologist (for adults) but without having
any specialist for pediatric echocardiography. Distances of subcenters from the referral
centers vary from 9 to 200 km (on the average 160 km). A videoconferencing unit was
used for transmission of cardiograms at 15–18 frames per second. An independent study
of the videotaped echocardiograms was carried out by a pediatric cardiologist during
the period between January 1998 and January 2001. It was found that there was concur-
rence of reports in 383 cases out of 401. However, it was difficult to interpret colored
Doppler images using the system.
In Taiwan [16], a telemedicine network was established for clinical consultation using
T1 leased line and ISDN link. In this network, the National Taiwan University Hospital
(NTUH) acted as the referral center, which was connected to several other remote sites,
134 Health Monitoring Systems
e.g., with Chinshan Health Station and National Chung Kung University Hospital.
At NTUH, teleconferencing sessions were conducted. The participants could get access to
a multimedia database. Images in different modalities such as CT, MR, and X-Ray repre-
sented in DICOM 3.0 standard were used in consultations. MPEG-compressed videos of
USG, endoscopes, etc. were transmitted. The system was also capable of using the store
and forward image transfer. A high degree of acceptance of the system was reported from
the feedback from patients, consulting physicians, and technicians.
In 2001, at the Indian Institute of Technology, Kharagpur, a telemedicine system [17]
was developed for the treatment of leprosy and other skin-related diseases. Later the
system catered services to some other areas of medicine, such as teleradiology and
hematology. The system was called TelemediK and designed for usage in a country like
India, where basic healthcare facilities were poor and data communication infrastruc-
ture was also not developed during that period. The system was designed to run even on
low-data-rate plain old telephone system (POTS) links. However, its features were scal-
able as and when higher data rate links were made available. It had a back-end MS-SQL
RDBMS server and used the store and forward technology for performing consultation
on patient records. The system provided interfaces for entering patient records, organiz-
ing them as a single unit, and transferring them to the remote end using the FTP. At
the remote end, the experts used a browser to view the incoming patients’ records and
provided their opinions, which were again transmitted back to the nodal centers. It had
also an online data conferencing module, which enabled consulting physicians to dis-
cuss and annotate on shared images. The conversation was carried out over a separate
dedicated telephone line. The system could operate with low-cost peripheral equipment
such as ordinary digital camera, scanner, webcams, etc. It was operational between the
School of Tropical Medicine, Kolkata (which acted as the referral Institution) and Habra
State General Hospital, 24 Parganas(N), and Cooch Bihar M J N Hospital, Cooch Bihar
(which acted as nodal centers). About 1,500 patients were treated by this system for a
period of 2 years.
2 https://digital.nhs.uk/services/health-and-social-care-network.
Telemedicine Technology 135
It provides the infrastructure facilitating coordination of health and social care services
through reliable exchange of medical information.
During this period, a good number of modern-day telemedicine systems are report-
edly designed using internet protocols and web technology. Costs of these systems have
significantly reduced, and they could be accessed by standard internet browsers from
remote locations. With the development of cloud technology, cost of hosting such a sys-
tem has further reduced. In addition, penetration of mobile technology in our society
and availability of moderately high bandwidth wireless data services make the systems
easy to access and easy to deploy. Different useful apps in mobile environment are
increasingly being reported [20]. Many of these systems are designed and implemented
targeting specialized areas of healthcare. A few typical initiatives during this period are
discussed below.
In Brazil, healthcare services have been provided using the Santa Catarina State
Integrated Telemedicine and Telehealth System (STT) [21] since 2005. The system was
developed as a partnership between the Santa Catarina State Health Department (SES/
SC) and the Federal University of Santa Catarina (UFSC). It uses a statewide network for
healthcare services run by Brazilian universalized public healthcare system (SUS), which
includes remote diagnosis on several specialties such as electrocardiography, dermatol-
ogy, electroencephalography, radiology, etc. It was observed that there was a quantum
jump in the utilization of the resources, when a PACS network was integrated with the
system.
In the Department of Psychiatry, University of California, San Francisco (UCSF), USA,
a program3 has been initiated by the Young Adult and Family Center to help young
adults and adolescents suffering from mental health using various internet-based tech-
nologies, videoconferencing, text messaging and chats, and social media platforms, such
as Facebook.
In 2006, a door-to-door screening for patients suffering from diabetes in 42 villages
in Southern India was carried out using a mobile van [22]. Further screening for
complications, on the patients found to be suffering from the disease, was carried out by
performing a series of tests on them. These included tests related to retinopathy using ret-
inal photography and slit lamp, peripheral vascular diseases using Doppler ultrasound
imaging, etc. The van was fully equipped with advanced equipment run by paramedical
staff. Consultations for diabetic retinopathy and foot problems were carried out with a
specialist in Chennai (capital of the Indian state Tamil Nadu) using the very small aper-
ture terminal (VSAT) communication link of Indian Space Research Organization (ISRO).
Retinal images and images of foot lesions were transmitted using this link. The other
partner of the program was World Diabetes Foundation in Denmark. The program ran
for 4 years, and it was reported that, during this period, only 2% of the patients needed to
be sent to the hospital in Chennai.
In [23], implementation and testing of a system for teleophthalomogy has been reported.
Medical videos of HDTV quality were transmitted to the ophthalmologists, whereas
moderate- to low-quality videos were used in videoconferencing between a patient and a
doctor. Requests for teleconsultation were sent a priori following a secured communication
protocol between the hospital information systems at both ends. The system used private
protocols based on TCP and UDP connections under OpenSSL frame work for exchange
of information. The system was experimentally tested by running trials on 100 patients
of Tan Tock Seng Hospital (TTSH, Singapore) with a clinic. Two ophthalmologists were
3 http://psych.ucsf.edu/telemedicine-project.
136 Health Monitoring Systems
involved in checking the patients in this trial. One of them consulted the patient in the
clinic with the conventional face-to-face mode, and the other expert consulted the same
patient with TeleOph independently. Based on their diagnosis reports, it was observed
that both of them had agreement in all the cases.
An interesting application of telemedicine using wearable smart garment has been
reported in [24]. The garment was fitted with devices monitoring ECG, respiration, and
movement of a patient. It was used for the home monitoring of cardiac patients. These
signals were stored and analyzed in a touchscreen computer with a dedicated software
for data handling and a universal mobile telecommunications system (UMTS) dongle for data
transmission, via email, to three cardiologists. In a pilot study, three patients participated
daily in 3 min telemonitoring sessions for 30 days by using the platform. The system
functioned reliably by transmitting good quality biomedical signals within this period. In
another study, the same garment was used to evaluate the effect of high-altitude hypoxia
on 30 healthy subjects living at a height between 3,500 and 5,400 m. In this case also, the
study could be continued without much trouble. The subjects were found to be comfort-
able with the vest in most cases.
At the Indian Institute of Technology (IIT), Kharagpur, a web-based centralized
telemedicine system [25] was developed in 2008, named iMediK, with Windows operating
system with a back-end MS-SQL RDBMS. In addition to facilitating remote clinical con-
sultations on different areas of medicine, the system had also a few specialized modules
for treating pediatric HIV patients and patients suffering from drug-resistant tuberculosis.
The system had interfaces with mobile devices and was operational in government hospi-
tals of West Bengal for a period of 5 years. In 2011, iMediK became operational in govern-
ment hospitals of Tripura, another northeastern state of India. Before using iMediK, these
hospitals were using the peer-to-peer telemedicine system TelemediK2005, developed
by the same Institute. In 2016, iMediK was replaced by a s imilar system, iMediX, which
operates with Linux OS and mySQL as the back-end RDBMS. It is currently being used
by more than 20 hospitals in Tripura with an annual patient turnover of more than 7,000.
In 2011, a system for managing neonatal patients in the NICU of IPGMER, Kolkata,
had been installed [26]. This also used a web-based centralized server and performed
teleconsulation with the special newborn care units (SNCU) of the hospitals of West
Bengal, Chhattishgarh, Odisha, and Rajasthan in India.
5.5.1 System Architecture
A web-based telemedicine system should be secure, robust, flexible, and interoperable,
so that it (a) protects the privacy, confidentiality, and integrity of the patient records from
external as well as insider attacks; (b) accommodates a large number of simultaneous
users without failing; (c) adapts easily to new changes, if required; and (d) is capable of
exchanging information with other systems. In addition, it is desirable to keep the cost of
development and maintenance low.
For making the system secure and reliable, a multi-tier application development
architecture has been followed in the design [25]. In a typical multi-tier system, differ-
ent segments of the application are implemented in separate layers. The modules in these
layers communicate among themselves to serve a request generated from the client’s end.
In iMediK, there are four layers, namely the Database Layer, the Business Logic Layer,
the Presentation Layer, and the Web Proxy Layer. A firewall is to be placed before the
Presentation Layer, and the Web Proxy Layer accepts the request from the client and
forwards it to the Presentation Layer for its processing. In Figure 5.2, a brief schematic
diagram [25] of the four-tier system is shown. Descriptions of different layers are briefly
presented below.
5.5.1.1 Database Layer
It is the lowermost layer. In the RDBMS, all the medical records of the patients as well
as their personal details are stored. However, to restrict the access of a patient’s identity
by users other the attending doctors, the clinical records are kept separately from per-
sonal information. The medical information is stored in a structured form in a large num-
ber of relational tables of the database. There are different types of logins for different
types of users in the database. The database can only be accessed from the Business Logic
Layer. Depending upon the roles of users, appropriate logins are used to get access of the
database.
138 Health Monitoring Systems
FIGURE 5.2
Four-layer architecture.
5.5.1.3 Presentation Layer
In the Presentation Layer, the raw data is converted into user friendly HTML formats. This
layer operates behind the firewall by accepting requests from the Web Proxy Layer, pars-
ing it, and then forwarding it to the Business Logic Layer by invoking necessary modules
in the Business Logic Layer to perform data insertion/retrieval operations. Once it gets the
Telemedicine Technology 139
results of the processing from the Business Logic Layer, it generates response to the client
according to its desirable format and forwards it to the Web Proxy Layer. The Presentation
Layer is capable of handling flexibly different requirements of presentation format for gen-
erating responses. For example, for a mobile device, it generates HTML pages according to
the display characteristics of the device.
5.5.2 Implementation
Microsoft.Net Framework was used in the implementation of the system [28]. For the
back-end database, Microsoft SQL server was used. All other layers were hosted in
Windows server using Internet Information Services (IIS). The Web Proxy application
was written in C#, handling all HTTP requests to the web site. It was a custom imple-
mentation of the HttpHandler class in .Net platform implementing several submod-
ules, such as UrlManager, SessionMappingManager, CookieManager, AppLogManager,
FormsManager, and ResponseToClientManager.
In the Presentation Layer, there were a set of HTML, ASP, and ASP.Net pages. The
Business Logic Layer had .Net Remoting components, hosted in the IIS server. There
were interfaces with these components in the Presentation Layer, and all communications
between them were SOAP formatted. For a secured access by a client, the system was
hosted by an HTTPS server.
140 Health Monitoring Systems
5.5.3 The Data
There were different types of data used in the system, and they could be broadly categorized
into five classes:
5.5.4 Data Conferencing
Apart from offline data uploading and downloading in the system, there were provi-
sions for online chatting and data conferencing with multiple participants. During data
conferencing, the participants would communicate by sending text and graphics primi-
tives. The interface provides a common canvas for conferencing with graphic primitives
(i.e., by drawing, annotating, etc.) of a patient’s data. The canvas could be filled with vari-
ous background images, such as white background or whiteboard, a medical test image,
special anatomical images such as human profiles, etc. It is possible to do conferencing
with multiple canvases. The interface implemented operations such as drawing lines,
contours, circles, ellipses, text annotations, etc. Some of the image processing operations,
such as zooming of a portion of image, edge extraction, enhancement, etc., could also be
carried out using these modules. Annotations and drawing of different participants were
identified by distinct colors of text and graphics.
Telemedicine Technology 141
5.5.6 Specialized Modules
iMediK had specialized modules for treating patients suffering from pediatric HIV [29]
and drug resistant tuberculosis. There were special interfaces for data entry and visual-
ization of data in charts and graphs. A few examples are shown in Figure 5.4. There were
decision support systems for prescribing drugs following WHO guidelines (Figure 5.5).
These modules also had specialized interfaces for mobile devices.
FIGURE 5.3
Summarized report generated for a dummy pediatric HIV patient.
142 Health Monitoring Systems
FIGURE 5.4
(a) Chart showing history of HIV infection in a family and (b) growth chart of a pediatric patient.
FIGURE 5.5
Prescribing drugs and doses for (a) pediatric HIV patients, and (b) drug-resistant tuberculosis.
FIGURE 5.6
(a) Zoomed portion of an image of a pathology slide, (b) display of an ECG waveform, and (c) growth chart of
a patient.
Telemedicine Technology 143
made transparent to the users, so that the system and services are trusted by both the
patients and service providers. In USA, healthcare service providers and technology devel-
opers need to follow the principles and guidelines set by the Health Insurance Portability
and Accountability Act (HIPAA) of 1996. The Govt. of India is considering passing a simi-
lar kind of law in the form of Digital Information Security in Healthcare Act (DISHA).
However, these laws regulate the activities of hospitals and clinicians while acquiring
patient’s medical information and sharing them for consultation. There is no regula-
tory agency to check the use of patient’s data by the technology solution providers, when
their systems directly acquire data from a patient with a routine consent from them. For
example, routine transmission of patient’s medical data from an app or a medical device
may be shared with an advertiser sponsoring the app [32]. Presently, it depends upon the
technological companies how the information will be used and to what extent it would be
disclosed to a patient. There is no such legal limit. There is always a risk of compromising
patient’s privacy in such cases.
In telemedicine system, sufficient care should be taken, so that even if an unauthorized
person or process gets access to the medical records, the data should appear meaningless.
Encryption of data at different levels can enforce this property. The data may be stored in
encrypted form in the database. It needs encryption during transmission, and finally the
recipient and the sender may use an end-to-end encryption–decryption scheme. The first
two tasks are taken care of by the database system and the communication protocol fol-
lowed by the browser and server (e.g., HTTPS). But the third measure may be required to
be implemented in the telemedicine system.
5.8 Conclusion
The present trend in delivery of healthcare is to make it synonymous to home care. Under
this context, telemedicine technology is becoming more and more relevant and has huge
potential to grow. The emergence of health gadgets based on IoT (Internet of Things),
expansion of cloud computing infrastructure, and large-scale penetration of smartphones
empowered by powerful processors and graphics engines have brought another paradigm
shift in the design and development of this technology. Next-generation telemedicine
technology would be more focussed on a specific domain of healthcare and oriented to
cater to needs related to personal health. On the other hand, more and more hospitals
would be connected, and the systems would become interoperable following the interna-
tional standards. This would also eliminate the boundary between a hospital management
information system (HMIS) and a telemedicine system.
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6
Biomedical Signal Analysis1
CONTENTS
6.1 Introduction......................................................................................................................... 148
6.2 EMG Signal.......................................................................................................................... 148
6.2.1 Signal Description.................................................................................................. 148
6.2.2 Signal Acquisition................................................................................................... 149
6.3 EEG Signal........................................................................................................................... 150
6.3.1 Signal Description.................................................................................................. 150
6.3.2 Signal Acquisition................................................................................................... 151
6.4 Electrodermal Activity....................................................................................................... 152
6.4.1 Signal Description.................................................................................................. 152
6.4.2 Signal Acquisition................................................................................................... 154
6.5 Signal Analysis.................................................................................................................... 158
6.5.1 Time-Domain Analysis.......................................................................................... 158
6.5.1.1 Integrated EMG (IEMG).......................................................................... 159
6.5.1.2 Mean Absolute Value............................................................................... 159
6.5.1.3 Variance (VAR)......................................................................................... 159
6.5.1.4 Root Mean Square (RMS)........................................................................ 159
6.5.1.5 Waveform Length (WL).......................................................................... 159
6.5.1.6 Zero Crossing (ZC).................................................................................. 160
6.5.1.7 Willison Amplitude (WAMP)................................................................. 160
6.5.2 Frequency-Domain Analysis................................................................................. 160
6.5.2.1 Fourier Transform.................................................................................... 160
6.5.2.2 Short-Time Fourier Transform............................................................... 161
6.5.2.3 Modified Mean Frequency (MMNF).................................................... 162
6.5.2.4 Canonical Correlation Analysis (CCA)................................................. 162
6.5.3 Time-Frequency Analysis...................................................................................... 162
6.5.3.1 Wavelet Transform Overview................................................................ 162
6.5.4 Pattern Recognition Analysis................................................................................ 164
6.5.5 Hidden Markov Model (HMM)............................................................................ 164
6.5.6 Overview of the Artificial Neural Networks (NNs).......................................... 165
6.5.7 Overview of the Support Vector Machine ......................................................... 167
147
148 Health Monitoring Systems
6.1 Introduction
Traditionally, the monitoring of physiological signals has been conducted in labs and at
medical premises. Reliable data used by clinicians to track our health status or smarten
artificial intelligence methods help identifying important events and providing immedi-
ate feedback. Bringing such physiological signal monitoring practice to the final user in
daily life has great potential to improve our quality of life. Furthermore, all these data
can be used offline to deepen our understanding of the human body and its behavior. The
traditional way of collecting biomedical signals (like electroencephalogram [EEG], explor-
atory data analysis [EDA], and electromyography [EMG]) using electrodes connected to a
biomedical equipment through wire cables can be uncomfortable for the users. The cur-
rent acquisition systems, such as EEG, EDA, EMG, and electrocardiogram (ECG), are typi-
cally fixed, wired and cumbersome. Consequently, to enable ambulatory monitoring, they
must be substituted with smart, wireless, comfortable and wearable solutions. Some prog-
ress has been accomplished in this field [1].
This chapter introduces some of the typical signals used to monitor physiological infor-
mation from the human body. We start with a brief overview of EEG, EMG, and EDA by
presenting their nature and how they can be collected. Then, we provide an overview of
common feature extraction techniques to process the acquired raw data, both in the time
and frequency domain.
Finally, we describe some popular machine learning algorithms used for activity and ges-
ture recognition with biosignals. Concerning, the described acquisition system and inter-
face, we mainly introduce acquisition interfaces that enable the development of wearable
devices, considering low-cost energy-efficient platforms. We choose computational meth-
ods that can be embedded on state-of-the-art advanced but low-power microcontrollers.
6.2 EMG Signal
6.2.1 Signal Description
The EMG signal measures the electrical activation of the muscular fibers. Similar to the
nerves, the muscle tissue conducts electrical potentials, and these electrical potentials are
called muscle action potentials (APs). Along the nerve cell membranes, the APs are gener-
ated by the passage of Na+ ions and K+ ions. These reactions are the electrical messages
Biomedical Signal Analysis 149
FIGURE 6.1
EMG and muscular contraction.
sent by the brain to the muscles to start contractions. The Na+ ions flow through the cel-
lular membrane via the Na channels. Due to the influx of Na+ ions, the cell membrane
depolarizes and the nerve impulses propagates toward the target muscle cells (Figure 6.1).
The Na+ ions’ flow into the nerve cell is an essential step in the transmission of the AP to
the nerve fibers and along the axons, and it causes a release of the Ca++ ions, causing cross-
bridge binding and muscle sarcomere contraction [2].
The EMG signal, acquired by electrodes placed on the skin surface, is composed of
all the AP of the cells underlying the electrodes that are aligned parallel to the muscle
fibers. The surface EMG sensors consist of two conductive plates, and each one is con-
nected to the inputs of a differential amplifier that can sense the AP of the muscular cells.
The maximum amplitude of such signal is 20 mV (−10 to +10), depending on several fac-
tors, such as the diameter of the muscle fiber, the distance between the active muscle fiber,
and the detection site and the properties of the electrode. Even if the maximum bandwidth
of the amplifier does not exceed 2 kHz, this signal is also very noisy and difficult to man-
age. The main causes for this are the noises caused by motion artifacts, fiber crosstalk,
electrical equipment, and the floating ground of the human body, not referred to a solid
ground potential [3,4].
6.2.2 Signal Acquisition
The typical EMG acquisition is based on active (high-end devices with integrated condi-
tioning circuitry) or passive (low-cost passive patches that needs external conditioning
circuitry) sensors (Figure 6.2).
Active sensors provide a high-quality preamplified EMG signal suitable for direct inter-
face with microcontrollers. For instance, Ottobock sensors [5] represent the commercial
solution for EMG acquisition for high-end prosthetics, both in research and industrial appli-
cations. These sensors perform a full-analog signal conditioning based on a bandpass dis-
crete filter, an instrumentation amplifier with a high gain stage, and an offset cancellation
feedback circuit that requires the use of a dedicated metal plate as the reference electrode
150 Health Monitoring Systems
FIGURE 6.2
Acquisition setup for passive (high) and passive (low) EMG sensors. In the passive sensor setup, the ADC
directly acquires the signal, while the filtering and the feature extraction are made in the digital domain. In the
active electrode setup, filtering and feature extraction are made directly by the active sensor, while the digital
platform performs only the last part of the processing [25].
for each sensor. In an Ottobock sensor, the EMG signal is amplified and integrated to reach
an output span of 0–3.3 V, ideal for the single-ended stage of an embedded microcontroller
analog to digital convertor (ADC). The bandwidth of the Ottobock sensor is 90 Hz to 450 Hz
with a further Notch filter for the 50 Hz. This is because the sensors for the classification
of the gestures do not need extensive frequency information but a clear low noise signal.
Passive sensors are conductive plates (Ag-AgCl or Au), which can be connected to the
conditioning circuitry, such as a biopotential analog front end (AFE) or an operational
amplifier with a high-common-mode rejection ratio (>90 dB) and a high-input impedance
(>1 GΩ) amplifier. With passive sensors, the signal quality depends on the conditioning
circuitry. It is possible to acquire raw EMG data with a frequency up to 20 kHz while the
signal conditioning or filtering is performed in the digital domain.
6.3 EEG Signal
6.3.1 Signal Description
EEG measures the electric brain activity caused by electric currents that flow within the
neurons of the brain. Neurons are the cells that constitute the brain tissues and their struc-
ture is depicted in Figure 6.3. Despite the apparent simplicity of the neural cell structure,
the biophysics of neural current flow relies on complex models of ionic current generation
and conduction. When a neuron is excited by other neurons through the burst of APs,
excitatory postsynaptic potentials (EPSPs) are generated at its haptic dendritic branches.
The dendritic membrane becomes depolarized and extracellularly electronegative with
respect to the cell body. This potential difference causes a current, called primary current,
to flow from the non-excited membrane of the body to the dendritic tree, sustaining the
Biomedical Signal Analysis 151
FIGURE 6.3
Structure of a neuron. The cell body (a) contains the cell nucleus and acts as the cell’s life support center. The
cell body gathers and aggregates the signals arriving from other cells though dendrites (b). The axon (c) allows
the neuron to spread the signal away from the cell body to other neurons. The information is constituted by a
neural impulse (d), flowing from the cell body to the peripheral synaptic terminals (e), which in turn commu-
nicate with another neuron.
EPSPs [6]. The principle of conservation of electric charges imposes that the flow is looped
with extracellular currents, called secondary current. Both, the primary and secondary
current contribute to generate the magnetic field measured by the electrodes on the scalp;
however, the spatial cell arrangement is critical for the superposition neural currents to
produce measurable fields. In fact, the measured EEG signal is the result the EPSPs of
thousands of synchronously activated neurons because of the coherent distribution of their
large dendritic trunks that are locally oriented in parallel and perpendicularly pointed
to the cortical surface [6]. The signal must cross several layers before reaching the elec-
trodes placed on the scalp, especially the skull, which attenuates the signal approximately
100 times more than the soft layers [7]. The noise within the brain and over the scalp con-
tributes in the lowering of the signal-to-noise ratio (SNR); therefore, only a large amount
of active neurons can generate enough potential to be recorded by the scalp electrodes [7].
6.3.2 Signal Acquisition
The EEG signal acquisition is an easy and non-invasive operation, which allows a great
spread of this technique over others (magnetoencephalography, functional magnetic reso-
nance imaging, and Electrocorticography). In fact, to acquire the EEG signal, it is sufficient
to measure a set of electric potential differences between pairs of electrodes attached on the
scalp. However, detecting EEG activity is not a trivial task, as the sensors and circuitry must
cope with non-stable, skin-electrode interface, as well as with an intrinsically high-noise
signal. Apart from brain activity, which are unrelated to the steady-state visually evoked
potentials (SSVEPs), the additional sources of noise can come from the acquisition system
like electrical noise and external interference. The most common source of EEG-signal deg-
radation is the finite contact impedance at the interface between the electrode and the skin.
A high value of contact impedance leads to a potential divider effect at the amplifier input.
152 Health Monitoring Systems
FIGURE 6.4
(a) Picture of the g.SAHARA dry electrode with custom amplifier PCB. (b) Electrical schematics of the custom
amplifier PCB.
This causes a reduction of the capability to reject common-mode noise, such as that from
mains. It increases the noise generated at the metal-skin interface and augments the effect
of interference, coupling through capacitive effects to the cables or the artifacts because of
cable movement and the microphone and piezoelectric effect. The contact impedance is
minimized in clinical EEG protocols by removing the superficial skin layers by abrasion and
inserting a conductive gel or paste in-between the two surfaces. Obviously, the skin prepara-
tion is unsuitable for non-clinical settings, where the system setup needs to be as quick and
easy as possible for an untrained person, and the associated risk of infection is unacceptable.
To minimize the setup time and allow self-positioning of the system, zero-preparation
electrodes can be adopted as an interface between the system and subject. The two options
available are dry and wet electrodes. The dry electrodes are recognized as the best option
for zero-preparation time. However, they present contact impedance up to three orders of
magnitude higher than wet electrodes with skin preparation; hence, to mitigate such high
contact impedance, an amplification stage is directly placed on the electrode.
Figure 6.4a and b respectively show a picture and the schematic of the active sensor
custom printed circuit board (PCB) designed in Ref. [8]. As the single-ended amplification
stages with a gain higher than one reduce the rejection of common mode noise, only a
signal buffering is performed on the active electrode by a low-power, low-noise, and rail-
to-rail operational amplifier, connected as a unity-gain buffer. Protection resistors with
68 kΩ are used to limit patient auxiliary current in cases of single fault condition below
the applicable limit of 50 μA. The operational amplifier is an AD8603 from analog devices,
which has a quiescent current of 50 μA and a low-voltage noise (2.3 μV peak-to-peak in
the 0.1–10 Hz band and 25 nV / Hz at 1 kHz). While the total input capacitance is below
5 pF, the input leakage current is below 1 pA at room temperature, which translates into an
input impedance in excess of 500 MΩ in the EEG band.
6.4 Electrodermal Activity
6.4.1 Signal Description
The changes in the electrical properties of the skin, that is, the skin conductance, are com-
monly referred to as electrodermal activity. Since the second half of 19th century, when
psychological factors related to electrodermal phenomena were first discovered, electro-
dermal recording has become a huge topic of interest in the field of psychophysiology.
Biomedical Signal Analysis 153
The internal variables that could affect the EDA measurements include disruptions of
the skin-electrode interface (mechanical pressures on the electrodes, loose contact, and/
or changes of the skin caused by the electrolyte solution) and artifacts, such as body move-
ments, speech, and irregular breathing. In addition, there are wide individual differences in
both tonic and phasic EDA related to demographic variables, such as age, gender, and ethnic-
ity [10]. The external variables that influence electrodermal recording include the environ-
mental variables, such as temperature, humidity, and ambient noise. Long-term ambulatory
recording of skin conductance outside of a temperature-controlled laboratory has also
found EDA measures to be positively correlated with changes in ambient temperature (in
Ref. [7] recordings duration was about 7 h continuously; in Ref. [11] EDA was recorded for
up to 28 h continuously). Medication is also considered an artifact that significantly influ-
ences the EDA values, especially the psychiatric medicines, such as anti-depressants.
6.4.2 Signal Acquisition
Biosignals have very specific requirements, and many projects end up heavily bounded by
high-cost hardware materials. Physiological computing poses different challenges related
to biosignal acquisition like the need for high SNR or greater accuracy in the sampling
rate. We explore EDA signal acquisition using BITalino [12], a commercial platform that is
a good compromise between different requirements, such as matching a low-cost, form-
factored support for easy prototyping and obtaining sufficient signal quality for a wear-
able EDA platform.
BITalino is a low-cost modular biosensor kit. It consists of different independent func-
tional blocks, such as sensor chips (EEG, ECG, EDA, EMG, accelerometer, and light sensor);
a Bluetooth chip; a rechargeable battery supply (500 mA 3.7 lithium polymer); a power
management unit, which acts as a voltage regulator and provides information on battery
status; an 8-bit Atmega microcontroller; and input–output units, such as buttons and LEDs.
The EDA sensor (Figure 6.5a) has an inbuilt analog circuitry to improve the SNR. The
bandwidth of the filter is designed to pass frequencies between 0 and 2.8 Hz. So, a sam-
pling rate of 10 Hz is used. The operating voltage is 3.3 V, and the range of skin conductance
that can be measured is between 0 and 25 µS.
The transfer function that translates the ADC units to the equivalent skin conductance
value is as follows:
ADC
n ⋅ VCC
2
EDA (µS) = (6.1)
0.132
where EDA is the skin conductance value in microsiemens; ADC is the value sampled
from the input channel; n is the ADC (number of bits) resolution; and VCC is the regulated
voltage from the power supply.
Dry electrodes are used to overcome the disadvantages of gel electrodes, such as lack
of reusability, longevity, and comfort. In dry electrodes, the metal electrodes are directly
placed on the skin surface. Since no gels are used, they establish a good contact after per-
spiration, where the sweat acts as the electrolyte at the skin-electrode interface. A dry
electrode has similar characteristics to a polarizable electrode, considered as a leaky
capacitor. This imposes the need for analog conditioning circuitry to have a very high
input impedance in the range of GΩ. Moreover, the circuitry should be placed very close
to the e lectrode to prevent an electromagnetic interference. BITalino is interfaced with
Biomedical Signal Analysis 155
(a)
(b)
FIGURE 6.5
(a) BITalino EDA sensors, pins and dimensions. (b) Hardware building blocks of the acquisition setup.
a Cortex-M4 microcontroller (i.e., STM32F4 integrating the ADS1298 AFE). Figure 6.5b
shows the hardware acquisition building blocks.
STM32 is programmed to receive the analog signal from the BITalino EDA sensor unit.
One of the 12-bit ADCs of the microcontroller is configured to sample the data at a pre-
defined interval (in this case, 100 ms). The sampled ADC bits are then transferred to the
personal computer (PC) by configuring the universal asynchronous receiver/transmitter
communication protocol. The baud rate used is 921,600 kbps. The EDA sensor is supplied
with a reference voltage of VCC/2 (i.e., 1.65 V) by the power management unit. The EDA
being a slowly changing signal and the 0–2.8 Hz bandpass filter used in the sensor warranted
a low sampling rate of 10 Hz. Furthermore, with such a sampling rate, the motion artifacts
are almost avoided during the acquisition. However, it is necessary to use a moving average
filter to avoid the false identification of the cognitive event. To perform an acquisition test,
156 Health Monitoring Systems
FIGURE 6.6
Electrodes placements for the EDA acquisition - scheme and real: A, B = dry electrodes for the fingers; C, D = both
dry and gel electrodes for the palm; E, F = dry electrodes for the wrist.
some shrewdness must be applied. Since the EDA signals are strongly dependent on the
environment, it is better to maintain a stable room temperature. We acquired the signal with
both gel and dry electrodes on the palm, and only with dry electrodes in the other position
(Figure 6.6). Furthermore, to obtain the subject’s EDA reaction and repeatable experiments,
we applied the following rules. Firstly, the subjects were asked to limit as much physical
movements as possible to relax completely and sit in a posture they were most comfortable
with. Secondly, the subject’s signals were recorded for 60 s while they relaxed to find the
baseline of each subject. Then, they were asked to perform the Valsalva maneuver [7,13,14]
for about 10 s. The total experiment was limited to 120 s, so the remaining 50s were used to
further relax. The Valsalva maneuver was chosen as it stimulates the SNS [15]. We repeated
the protocol before and after asking the subjects to wash the areas where the electrodes were
applied. Figure 6.7 shows the signal before and after the Valsalva maneuver, performed at
60 s in the plot. The rise time indicates the number of seconds it takes to reach the SCR
peak, while the decay time is during which the signal falls to 50% of the peak amplitude.
Figure 6.8 shows the signal acquired from different positions with the described setup. The
raw EDA signal is smoothed using an exponential smoothing function.
From our results, it has been demonstrated that palm is the most sensitive part of the
body to acquire a strong EDA signal. Signal captured from fingers also proved to suffi-
ciently reflect responses to psychological stimuli. The EDA signals acquired from the wrist
were the most inconsistent with a corresponding low amplitude (in the range of µS) and
therefore difficult to distinguish from noise. Furthermore, according to the literature and
our observed acquisitions, each subject has a different skin-conductance baseline. In addi-
tion, signal amplitude changes after washing hands, that is, skin conductance decreases
after washing hands. All these factors determine high signal variability among subjects
and in time. This along with the difficulty of preserving adequate conditions to minimize
noise due to temperature changes and body movements makes the EDA an unreliable
signal to be used independently to evaluate psychophysiology conditions of a subject, and
thus it is generally used in combination with EEG, ECG, etc. [11].
Biomedical Signal Analysis 157
FIGURE 6.7
Characteristics of the acquired EDA Signal.
FIGURE 6.8
EDA Signal acquired at different positions using different electrodes - from top left to bottom right: wet
electrodes on palm, dry electrodes on fingers, dry electrodes on wrist, dry electrodes on palm.
158 Health Monitoring Systems
6.5 Signal Analysis
Biosignals contain essential qualitative and quantitative information about a given event
that, in most cases, is not visible to the naked eye. In most cases, this is due to, among
numerous reasons, the superposition of several signals from different sources that do not
allow recognizing the significant patterns of a given event. Using several mathematical
transformation and filtering techniques, it is possible to extract only relevant information.
These, so-called features of the signal, can be roughly divided into three different catego-
ries, namely, time, frequency, and time frequency and are employed depending on the
nature of the phenomena that is studied. In this section, we will discuss the extraction of
these features while delineating general guidelines for their use.
6.5.1 Time-Domain Analysis
A signal in correspondence to a given event could be retrieved in the form of consecutive
time-series raw samples that indicate the evolution of the phenomena. This signal might
also include information about other events out of interest and noise. Thus, it is essential
to transform the signals in different domains to better represent and separate the clinically
significant components. These features are time-locked, mostly with some latency, with
respect to the original event. In the following section, we discuss the most common feature
extraction mechanisms used for biosignal processing (EMG and EEG) and in Figure 6.9 we
show some examples of the extracted features.
FIGURE 6.9
Example of the feature extraction output from an EMG signal. The original signal contains information about
two channels with two different consecutive contractions.
Biomedical Signal Analysis 159
IEMG k = ∑x
n= 1
n (6.2)
where k is the single output from the evaluation window, m is the evaluation window size,
and X n is a single point in the evaluation window.
MMAVk = ∑W
n= 1
n Xn (6.3)
6.5.1.3 Variance (VAR)
The VAR includes information on how the samples vary from mean, thereby providing
information on the signal spread. It can be calculated as follows:
m
VAR k =
1
m−1 ∑X
n= 1
2
n (6.4)
RMS k =
1
m ∑Xn= 1
2
n (6.5)
WL k = ∑X
n= 1
n+ 1 − Xn (6.6)
m− 1
ZC k = ∑sgn ( X
n= 1
n Xn+ 1 ) ∩X n X n + 1 ≥ threshold (6.7)
where sgn(x) is equal to one if the signal is above the threshold and zero otherwise.
N −1
WAMPk = ∑f ( X
n= 1
n+ 1 − Xn ) (6.8)
where f ( x) is equal to one if x ≥ threshold and zero otherwise. The WAMP provides infor-
mation about the firing of the motor unit APs on EMG signals.
With a few exceptions, the features presented up to now are more applicable to EMG sig-
nals. In the following section, we introduce the frequency-domain features that are more
recurrent in EEG signals. When possible, a time-domain feature might be selected since
the complexity of the calculation could be less computationally expensive. Nevertheless,
this is only possible when the signal of interest has a larger SNR than other components.
6.5.2 Frequency-Domain Analysis
These features are based on the spectral content of the signal. This process can itself con-
stitute feature extraction and, indeed, has been used largely in EEG [19,20]. Nevertheless,
further information can also be extracted after this transformation. Figure 6.10 shows a
few examples of the extracted features.
6.5.2.1 Fourier Transform
The Fourier transform allows signal conversion from the time domain to the frequency
domain. When working with discrete signals, the discrete Fourier transform (DFT) offers
the only workable solution that can provide computationally efficient calculations deploy-
able using low-power microcontrollers. The DFT is denoted by:
m− 1 i2n
∑X
kn
Xk = n em (6.9)
n= 1
Computing the spectrum directly from the definition is not practical. Instead, the preferred
method employs the fast Fourier transform, reducing the complexity of the computation
from O(m2) to O(m log m). This modification allows the computation of even large windows
Biomedical Signal Analysis 161
FIGURE 6.10
Example of the feature extraction considering the frequency components of the signal.
in a relatively short time, much desired in real-time and embedded applications. There are
several algorithms to compute the DFT, the Cooley–Tukey being the most widely used. The
DFT provides information about the phase and magnitude of the signal. The magnitude is
squared to estimate the power of the spectra. The features presented below start from this
calculation and aim to provide more information about the studied phenomena.
∑
kn
Xk = X n Wn e m (6.10)
n= 1
∑fA
j=1
j j
MMNFk = M (6.11)
∑j=1
Aj
where Aj corresponds to the spectrum power of the signal at the j frequency component.
6.5.3 Time-Frequency Analysis
Discrete wavelet transform (DWT) is the most popular signal processing tool in the time-
frequency domain and performs well with EMG signal pattern recognition [22]. The wave-
let transform is based on the multiresolution analysis of a given signal through a scalable
modulated sliding window.
γ ( s, τ ) =
∫ f (t) Ψ s ,τ (t) dt (6.12)
The wavelets are a set of basic functions Ψ s ,τ (t) obtained by scaling and translating a func-
tion Ψ(t) called mother wavelet:
1 t −τ
Ψ s ,τ (t) = Ψ (6.13)
s s
As the value of s is increased, the wavelet is dilated, ranging the analysis from the higher
to lower frequencies. The result is the transformation value. For each value of s, the wave-
let is shifted, increasing the value of τ. The implementation of this analysis requires using
Biomedical Signal Analysis 163
a wavelet that can be translated and scaled not continuously but in discrete steps, as
showed below:
1 t − kτ 0 s0j
Ψ j , k (t) = Ψ (6.14)
s0j s0j
with j and k integer values. The results of the transformation performed with discrete
wavelets is a series of coefficients, named wavelet decompositions.
Although a computer can compute this CWT, it is highly redundant and computation-
ally demanding because of all the combinations of s and τ. The CWT provides correlation
between a given signal and a test function (the wavelet) at different scales. In the discrete
case (the DWT), the signal is passed through a cascade of both low pass and high pass
filters to obtain information on high and low frequency bands, remembering that filtering
a signal is equal to performing the convolution of the signal with the impulse response of
the filter. To calculate both the components we introduce the scaling function φ:
1 if 0 < st < 1
ϕ ( st) = (6.15)
0 otherwise
The wavelet functions Ψ() are associated to the high pass components while the scaling
functions ϕ () are related to low pass filtering. The filtering changes the signal resolution,
and the scale is changed by downsampling the original signal.
The wavelet transform can be considered a bandpass filter and the series of scaled wave-
let a filter bank. Using a finite number of values for the decomposition, we extract infor-
mation in time-frequency domain by the recursive filtering of the given signal. Figure 6.11
shows the sequences of high and low pass filtering sequence performed to obtain the DWT
coefficients. For a DWT decomposition of level n, the coefficients of the low pass filters
are named detail coefficients (Dn) while the coefficients of the high pass filters are named
approximation coefficients (An).
FIGURE 6.11
Wavelet decomposition scheme.
164 Health Monitoring Systems
1. The state of the system at any given instant t depends only on the state at instant
t – 1.
2. The observations o at any instant t depends only on the state at the instant t.
• A set of N hidden states, S = {s1 , s2 , , sN }. Often these states are related to gesture
recognition.
• A set of M distinct observation symbols, V = {v1 , v2 ,…, v M }. These represent the
values of the observations as output of the system.
{ }
• The state transition matrix, A = aij , where aij is the probability of making a transi-
( )
tion from state si to state s j : aij = P qt = Sj |qt − 1 = Si , with 1 ≤ i, j ≤ N and where qt
denotes the state at time t.
Biomedical Signal Analysis 165
FIGURE 6.12
Example of a processing unit (neuron), where inputs (xd) and weights (wd) are used for a weighted sum (Σ), and
the sum passes through an activation function, producing an output (z).
{ }
• The observation symbol probability distribution matrix, B = b j ( k ) , where b j ( k )
( )
is the probability of emitting vk in state s j at time t: b j ( k ) = P ot = vk |qt = s j , with
1 ≤ j ≤ M ; 1 ≤ k ≤ M, and where ot denotes the observation at time t.
• The initial state distribution matrix, Π = ( π i ) where π i is the probability that si is
the initial state: π i = P ( q1 = si ), with 1 ≤ N.
λ = ( A, B, π) (6.18)
The transition probability matrix (A) and the observation symbol probability distribution
matrix (B) that describe the HMMs are computed using the MATLAB® function hmmesti-
mate. Before training the HMM, each training set is quantized into 12 levels; the same is
done with the test sets before the classification. With 4 states and 12 possible emissions
for each state, matrix A results to be 4 × 4 and matrix B to be 4 × 12. The definition of the
matrices is performed offline with MATLAB, while the online recognition is calculated by
multiplying A and B matrices and does not affect the real-time computing requirements.
a= ∑w x + w
i =1
i i 0 (6.19)
166 Health Monitoring Systems
We can rewrite the previous equation considering the bias as an extra output ( x0 ) set per-
manently to +1. The equation becomes as follows:
d
a= ∑w x
i=0
i i (6.20)
The weights and the bias can be negative or positive. The final output z is obtained by pass-
ing a through an activation function g()
z = g ( a) (6.21)
The non-linear activation function is of different types, such as linear, threshold, threshold
linear, and sigmoidal.
An NN is composed of a set of processing units (neurons) and weighted connections
between neurons. Of the several classes of NN, two fundamental classes are identifiable.
The first one is the feedforward network (Figure 6.13a) in which the neurons are grouped
in layers – one input layer, n hidden processing layers, and one output layer. Each neuron
of each layer can be connected with the neurons of the next layer (in the output direction).
A feedforward network is fully connected if each neuron is connected with all the neurons
of the next layer. In addition, each input–output set is independent of other states. The sec-
ond class is known as the recurrent network (Figure 6.13b) with at least one feedback from
the kth output to the (k + 1)th input. In some NNs, neurons can be connected to themselves
(direct recurrences), to neurons on the preceding layers (indirect recurrences), and/or to
neurons within the same layer (lateral recurrences).
The learning process of an NN can be performed in several ways. In unsupervised learn-
ing, the network tries to find similarity in the training set (a set of input patterns), generat-
ing pattern classes. Another possibility is the reinforcement learning, where the network
receives a value that indicates if the result is correct or not and possibly how correct it is.
The last one is supervised learning, the most popular way of training, where the training
set is composed of input patterns and labels, which indicate the true class of each input
[25]. Among all the possible NN topology and learning methods, the most common are
FIGURE 6.13
(a) An example of a fully connected feedforward NN with one hidden layer. (b) An example of a recurrent NN
with one hidden layer.
Biomedical Signal Analysis 167
The learning process begins with random weights, and the three steps are reiterated until
the error is minimal. The optimal weights are computed by minimizing the following
function through gradient descent:
∂E
∆wi = −α (6.22)
∂wi
where E is obtained by subtracting the real output to the one in output to the NN, and α
is the learning rate (a constant, in each iteration). The ∆wi is the adjustment of each weight
and depends on the amount of error generated by the previous weights.
( )
hθ ( x) = g θ T x (6.23)
where
1
g ( z) = (6.24)
1 + e− z
This is called the sigmoid or logistic function and gives the estimated probability that the
output y = 1 with a given new input x is:
hθ ( x) = P ( y = 1|x ; θ ) (6.25)
cost ( hθ ( x , y )) =
1
m ∑ − y (i)
( ( )) ( ) ( ( ))
log hθ x( i ) − 1 − y ( i ) log 1 − hθ x( i )
(6.26)
i=1
The computer science community developed the SVM in the 1990s in the framework of
the statistical learning theory. The basic idea of this supervised learning algorithm is to
168 Health Monitoring Systems
FIGURE 6.14
SVM algorithm diagram.
improve the performance of the classification obtained with logistic regression methods
through the application of large margin classification [24]. The offline training phase of the
algorithm uses labeled instances of data to calculate the optimal separation hyperplane
(maximum margin hyperplane) between two classes of data through the solution of a con-
vex optimization problem. Such separation plane is represented by a set of data vectors,
namely the support vectors, which belong to the borders of the two classes and are used
to classify new data instances. A diagram of SVM training and recognition is illustrated
in Figure 6.14.
When the two classes are not linearly separable, the input data space can be mapped to
a higher-dimensional space through a kernel function [27] to allow an effective separation
[16,28]. The kernel is based on a radial basis function (RBF) and use a Gaussian function
expressed by:
x − l( n)
exp − (6.27)
2σ 2
where x is the input vector, li is the i support vector, and σ is the variance.
Having two possible classes, denoted as Cl1 and Cl2, the formula of the decision function
to classify a new input instance is:
N SV
f ( x) = ∑y α K x, s
i=1
i i i −ρ f ( x) > 0, x ∈ Cl 1 , f ( x) < 0, x ∈ Cl 2 (6.28)
where x ∈ NF is the input features vector, Si ∈ NF , i = 1, , N SV are the support vectors,
αi are the support values, with yi depending on the class of reference (y i = +1 for Cl1, y i = −1
for Cl2), and K .,. denotes the kernel function.
The SVM is a binary classification algorithm, but its application can be extended for a
multiclass scenario by reducing the problem to multiple binary classification problems for
each class.
alternative to numbers). This high dimension allows a large number of nearly orthogo-
nal hyper-vectors, which can be combined into a new hyper-vector through well-defined
vector space operations. Multiplication, addition, and permutation (MAP) are the three
main operations used in HDC. The hyper-vectors can be binary vectors composed of an
equal number of randomly placed 0s and 1s, {0,1} . A measure of similarity between two
D
hyper-vectors can be obtained by using the cosine similarity, which can be substituted by
the Hamming distance in the binary case. The Hamming distance computes the num-
ber of bits at which two hyper-vectors differ. All the MAP operations can be replaced by
simpler operations when computing binary hyper-vectors; in fact, addition (+) becomes
a component-wise majority, multiplication a component-wise XOR (⊕), and permutation
(ρ) a 1-bit rotation. The addition operation produces a new hyper-vector similar to the
hyper-vectors in input (useful to represent sets), multiplication produces a dissimilar one
(for binding two hyper-vectors), while permutation is well suited to store a sequence as it
produces a dissimilar pseudo-orthogonal hyper-vector.
The HDC algorithm is composed of three main modules (Figure 6.15). In the first
one, we map the samples into a hyper-dimensional space using the item memory matrix,
which is composed of (pseudo)random orthogonal ( ⊥ ) hyper-vectors assigned to each
acquisition channel (i.e., E1 ⊥ E2 ⊥ Ei ). In the continuous item memory matrix, another
matrix used for mapping, hyper-vectors are assigned to the possible analog values that
the signal can assume. Orthogonal endpoint hyper-vectors are generated for the maxi-
mum and minimum signal level, and the other values between these two are derived
from a linear interpolation. For instance, discretizing the features in K levels leads to
K hyper-vectors (V1 , V2 , , VK ), where V1 ⊥ VK . These two matrices are computed at the
beginning of the training phase and remain constant throughout the lifetime of the
application. In the second kernel, HDC encodes the features into a hyper-vector using
the MAP operations to represent the event of interest. The spatial encoder captures the
FIGURE 6.15
HDC processing chain.
170 Health Monitoring Systems
spatial information contained in the signal using the component-wise XOR and the
component-wise majority:
( ) (
St = E1 ⊕ Vl (1)t + + Ei ⊕ Vl (i)t ) (6.29)
Sometimes, having the spatial information contained in St is not enough, we also need to
include the temporal information. The temporal encoder uses permutations and multiplica-
tions between sequences of N hyper-vectors to create an N-gram, where N is the length of
the temporal window we are considering.
The N-gram is created with the following equation:
where ρk indicates a rotation of k positions. These kernels are common for both training
and classification. During training, the N-grams derived from features of the same class
are added to form a prototype hyper-vector, which is stored on the associative memory
matrix with the prototypes of the other classes involved in the elaboration. During clas-
sification, the N-grams in the output to the encoders are called query hyper-vectors and
derived from unseen features. Hence, the query is classified computing the Hamming dis-
tances between the query and all the prototype hyper-vectors contained in the associative
memory matrix. The label associated to the minimum distance is assigned to the query.
The HDC algorithm is very robust and the dimensions of the structures required to
perform all the elaboration are fixed. The most remarkable advantage of this approach is
the capability of performing the training ‘one shot’, which means that there is no need of
time-consuming back propagation (like in the NN) or other convergence methods. For
this reason, HDC is feasible for an embedded implementation to perform on-chip training.
FIGURE 6.16
Experimental setup for the SSVEP classification © 2019 IEEE. Reprinted, with permission, from IEEE Internet
of Things Journal (Volume: 6 , Issue: 1 , Feb. 2019 ), Salvaro et al., “A Minimally Invasive Low-Power Platform for
Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis”.
FIGURE 6.17
CCA analysis of the SSVEP signals.
stimulus and can be extracted from the array of electrodes using the CCA. Figure 6.17
shows an example of the CCA of the EEG signals from the three channels used while
using four target frequencies. The pre-processing includes a high pass filter to eliminate
the signal offset and drift. Finally, the class is deduced from the correlation values, where
the highest correlation will be associated with the checkerboard that the subject is seeing.
While the checkerboards are flickering, the subject fixes the sight at a given target.
The SSVEP response is later captured by the Internet of Things node, and the results of the
classification are transmitted wirelessly to a host device.
In this test, the stimulus is presented for 10 s. The subject is asked to fix the sight at the
different checkerboards, sequentially.
The CCA is calculated in three main steps that include a QR decomposition, singular
value decomposition, and the extraction of the canonical coefficients. Finally, the Euclidian
172 Health Monitoring Systems
FIGURE 6.18
Real-time CCA classification chain.
norm is used as a feature extraction for the classification. Figure 6.18 illustrates the com-
plete processing chain for the extraction and classification of the EEG signals.
The complete system runs in real time on a low-power microcontroller and provides a
peak information transfer rate of 1.57 bps with a power envelope below 30 mW, providing
up to 122 h of continuous operation. The system has several applications including robotic
control and closed-loop HMI.
Here, we have combined different DSP techniques passing from offline experimenta-
tion and profiling to finally produce a device that can handle data processing while being
energy-efficient. This achievement denotes that the efforts for practical systems must
always be extended up to the architectural issues (i.e., computational power and power
consumption).
FIGURE 6.19
Algorithm workflow for SVM gesture classification.
FIGURE 6.20
FSM for prosthetic control.
FIGURE 6.21
EMG control strategy.
174 Health Monitoring Systems
plot. The red solid line shows the output of the controller. Once the gesture is recognized,
the controller stays in the same state and, to perform new movements, it always returns to
the “rest position” (open hand).
6.6 Conclusions
In this chapter, we have introduced some fundamental concepts regarding biopotentials,
acquisition methods, and processing that take advantage of the ability of the human
body to provide complementary information through biosignals. The current evolution
of these methods, coupled with the new trend in wearable and ultra-low power architec-
tures, allows reaching more ambitious application scenarios such as industry, gaming,
and learning, while improving medical applications toward more useful and practical
devices.
In particular, we explored the nature of EEG, EMG, and EDA signals that are the source
of current HMI systems, and we described the state-of-the-art algorithms for the extrac-
tion of useful information. We also glanced upon the new low-power embedded platforms,
which can process and classify different HMI paradigms in real time, also offering some
insights on recent technology for dry signal acquisition.
To wrap all the presented information, we have provided two case studies for the evalu-
ation and implementation of HMI systems, passing from the signal characterization and
experimentation, digital signal processing, and embedded deployment. We believe that
all this information presents a general picture of the challenges on the design and imple-
mentation of these devices and some possible practical solutions that will potentiate future
applications.
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176 Health Monitoring Systems
CONTENTS
7.1 Introduction......................................................................................................................... 177
7.2 Reported Solutions at a Glance......................................................................................... 178
7.3 Explanation of the State-of-the-Art Methodologies....................................................... 180
7.4 Conclusion........................................................................................................................... 209
References...................................................................................................................................... 210
7.1 Introduction
Remote areas, generally, encounter scarcity of cardiologists and progressive facilities
for the treatment of cardiovascular diseases (CVDs). However, same constraints will be
self-addressed by the exploitation of recent advancements in wireless technology and its
ubiquity. Clinical CVD identification is, generally, applied exploitation either of ordinary
12-Lead (S12) or Mason–Likar 12-Lead (ML12) system [1]. These systems encompass eight
freelance leads/signals which limit their usage in telemonitoring applications with per-
sonalized remote health observation, home observation, etc. to name a few [1]. Credits
go to high information measure and storage needs, knowledge, and low compression
quantitative relation from signal compression techniques. Moreover, the reduced lead
(RL) systems with 2–3 leads are generally utilized in telemonitoring application, which
is not decent for diagnosis. Diagnosis from 12-lead electrocardiography (ECG) is cum-
bersome and time-consuming. Therefore, it is required to form a reduced 3-lead (R3L)
system from S12 and ML12 systems at the transmission end, which reduces the number of
signals to 3 and then reconstructs the S12 and ML12 systems at the receiver end through
signal reconstruction. This can eradicate the aforementioned limitations. It can also be
the reconstruction of standard 12-lead (S12) system from Frank vectorcardiographic (FV).
Ap ersonalized reduced 2/3 lead system is required which can offer equivalent informa-
tion as contained in S12 system, so as to accurately reconstruct S12 system from RL system
for diagnosis. Among the 12-lead system, selection of such RL systems suitable for per-
sonalized remote health-monitoring applications is also a challenge, followed by its sub-
sequent successful reconstruction of S12-lead. The accuracy and real-time performance of
traditional methods needs improvement. Also if accurate Frank’s vectorcardiogram (VCG)
177
178 Health Monitoring Systems
system is made available avoiding the limitations of proper electrode settings and hard-
ware complexity, VCG will complement S12 system in diagnosis of CVDs.
There exists some literature that provides various solutions as described below.
reconstruction of S12 system from derived VCG, obtained using proposed PCA-
based method and compared it with results obtained when originally measured
Frank leads were used.
F. In the study presented in Ref. [5], authors have presented a methodology based
on CNNs for the synthesis of missing precordial leads [5]. The results in Ref. [5]
show that the proposed method receives better similarity and consumes less time
using the PTB database. Particularly, the performance of the methodology shows
cent percent accuracy in reconstructing the pathological ECG signal, which is
crucial for cardiac diagnosis. The CNN-based method is shown to be more accu-
rate and time-saving for deployment in nonhospital situations to synthesize a
S12-lead ECG from a RL-set ECG recording. This is promising for real cardiac care.
G. The authors in Ref. [6] have presented a novel, enhanced method for deriving
12-lead ECGs from a pseudoorthogonal 3-lead subset. Designers investigated
how well the 12-lead ECG is reconstructed using both generic and patient-specific
nonlinear reconstruction methods that are based on the use of artificial neural
networks (ANNs) [6].
FIGURE 7.1
Proposed remote health-monitoring scenario [1].
Pervasive Computing 181
FIGURE 7.2
Functionalities of the remote health-monitoring scenario.
2
R = 1 −
∑ Derived(sample k) − Measured(sample k) 2
× 100 (7.1)
∑ Measured(sample k) 2
rx =
∑ (Measured sample i) × (Derived sample i)
1/2 , (7.2)
(∑ (Measured sample i) × (Derived sample i) )
2 2
bx =
∑ (Measured sample i) × (Derived sample i) (7.3)
∑ (Measured sample i) 2
182
TABLE 7.1
Mean R 2, rx, and bx [16,17] Values of Various Categories for the Transformation of R3L to S12 System
V1 V3 V4 V5 V6 Average
R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R2
BB 90.40 0.947 0.899 95.78 0.979 0.975 87.39 0.937 0.913 88.47 0.944 0.919 92.41 0.962 0.952 90.89
HC 94.69 0.973 0.952 96.52 0.983 0.973 91.83 0.959 0.939 93.53 0.968 0.957 94.69 0.973 0.967 94.25
HY 97.44 0.987 0.979 96.23 0.981 0.968 87.45 0.934 0.897 89.90 0.945 0.899 95.74 0.978 0.957 93.35
MI 94.34 0.971 0.947 95.92 0.979 0.959 89.52 0.945 0.895 89.22 0.944 0.889 91.52 0.956 0.911 92.10
VA 93.73 0.967 0.942 94.25 0.970 0.945 89.97 0.948 0.906 91.89 0.959 0.921 93.78 0.968 0.934 92.72
ND 90.92 0.949 0.913 93.54 0.967 0.931 83.72 0.910 0.838 84.61 0.916 0.847 87.28 0.920 0.876 88.01
Note: Average value shown has been taken over the reconstructed precordial leads. It should be noted that the rest of the leads, namely, I, II, III, aVR, aVL, and aVF,
are obtained with approximately [1].
Health Monitoring Systems
Pervasive Computing 183
Table 7.1 presents mean R 2, correlation (rx), and regression (bx) values of various
categories of the working PTBDB [16,17].
The proximity effect can be observed from the table. Leads in close proximity
of the basis lead have better reconstruction compared to those away from it; this
is evident from the R2 values of leads V1 and V3, which are higher than other pre-
cordial leads owing to their close proximity to V2, one of the chosen basis leads in
this investigation. The lowest values can be found for lead V4.
R3L system was formed using leads I, II and one of the six precordial leads, that
is, V1–V6 resulting in a total of six such combinations and along with FV system
they all were used to reconstruct S12 system.
Table 7.2 presents the mean values of evaluation metrics for INCARTDB. It is
seen that reconstruction results in the case of S12 system are superior to those of
ML12 system. This difference is probably because of the electrode positions in
ML12, which are on the torso and not on arms and legs as in S12 system.
Table 7.3 provides the mean values of PT coefficients for PTBDB (healthy control
[HC] and unhealthy [UH]) and INCARTDB [1].
N.B. Patients in PTBDB were divided in healthy control (HC) and unhealthy
(UH) subjects [1].
Figure 7.3a,b corresponds to patients with mean R 2 values of 94.28% (HC) and
90.06% (UH), respectively (mean taken over 5 reconstructed missing precordial
TABLE 7.2
Lead-Wise Mean R 2, Correlation (rx), and Regression (bx)
Values of Recordings in INCARTDB [1]
INCARTDB
R 2 rx bx
V1 86.38 0.936 0.923
V3 86.91 0.940 0.926
V4 83.61 0.922 0.901
V5 83.74 0.921 0.911
V6 78.11 0.893 0.872
Average 83.75 0.922 0.907
Minimum 78.11 0.893 0.872
Note: INCARTDB, Institute of Cardiological Technics 12-Lead
Arrhythmia Database.
TABLE 7.3
Mean Values of Personalized Transformation Coefficients Obtained [1]
PTBDB [1]
INCARTDB [1]
Healthy Control Unhealthy
V1 −0.6767 −0.0561 0.4926 −0.6250 −0.0965 0.5006 −0.7450 −0.1431 0.5434
V3 0.7297 0.3298 0.8506 0.4449 0.3588 0.9637 −0.2267 0.5070 1.1181
V4 1.4101 0.5072 0.4149 0.9036 0.5792 0.5915 0.1140 0.7191 0.5214
V5 1.5883 0.4610 0.0314 1.0042 0.5933 0.1774 0.7173 0.6890 −0.0455
V6 1.1729 0.3618 −0.1173 0.7330 0.4710 −0.0442 0.6792 0.6080 −0.3074
Note: INCARTDB, Institute of Cardiological Technics 12-Lead Arrhythmia Database.
184 Health Monitoring Systems
FIGURE 7.3
The comparative study between original (blue) and derived (red) leads of the subjects from PTBDB and
INCARTDB. (a and b) The comparison of subjects from HC and UH category, respectively, with the mean R 2
values near to actual means provided in Table 7.1. (c) The corresponding mean case from INCARTDB. (d–f)
Minimum case of mean R 2 values for the subjects from HC, UH, and INCARTDB, respectively. The correspond-
ing R 2 values of the reconstructed lead have been provided with the figure. (Image appears in color in eBook
version of this book.)
Pervasive Computing 185
leads), which are nearly equal to the mean values given in Table 7.2. Figure 7.3c is
a similar mean case subject from INCARTDB with mean R 2 value being 84.97%.
Similarly, Figure 7.3d–f provides the reconstruction results of subjects with
minimum mean R 2 values from HC, UH, and INCARTDB, respectively. The range
of R 2 values for reconstructed leads shown in Figure 7.3 is from 5.512% to 99.52%.
It should be noted that R 2 values of 80% and above can be considered to have
high diagnostic value and R 2 values of 90% and above can be considered to be
an accurate retrace of the original 80% (51 × 5) value and 66% leads had more
than 90% R2 value. For the reconstruction of 340 (68 × 5) precordial leads of ML12
system, about 73.82% had more than 80% R 2 value and 50% leads had more than
90% R2 value. In Figure 7.3e,f, relative smoothing and stabilization of probably
badly positioned leads V4 and V5 can be seen. This is one of the advantages of
lead reconstruction as badly acquired leads can be reconstructed from the well-
acquired basis leads.
In this section of study, authors have attempted to address the requirements
of cardiologists and technological constraints encountered in telemonitoring
applications [1]. A personalized reconstruction methodology for reconstruction
of missing precordial leads of S12 and ML12 systems have been proposed and
the results show that using this methodology reliable and accurate reconstruc-
tion is possible. R3L system used in this paper requires no change in the stan-
dard electrode positions of S12 or ML12 system and hence it is a practical option
for clinical usage. Reconstruction of missing precordial leads of S12 outperforms
ML12 system. Here, we have presented an in-depth analysis of reusability of
transformation coefficients and analyzed the reconstruction results when the
same coefficients are used for reconstruction. It has been inferred that if care is
taken while placement of electrodes is at the standard positions, then reconstruc-
tion results are less likely to vary over time [1].
B. The methodology described in summary of the study cited in Ref. [2] is to recon-
struct the eight independent leads of S12 system from FV’s leads, that is, X, Y, and
Z. Brief description of methodology is as follows: first, raw ECG and VCG leads
are processed in a preprocessing module followed by coefficient generation using
HVP theory and LS fit method.
The coefficients generated are then used to reconstruct the leads from Frank’s
leads using HVP theory.
A wavelet-based preprocessing module has been proposed. The preprocess-
ing module consists of two steps, namely, removal of BW, and second, denois-
ing, as shown in Figure 7.4 [2]. Denoising has been performed by using three
variants, as shown in Figure 7.4, of wavelet transforms, namely, discrete wavelet
transform, undecimated wavelet transform [18], and translation invariant wavelet
transform [19]. BW has been removed using DWT [9]. The preprocessing module
consists of four different approaches, all of which consisted of two steps. The first
step involves the removal of BW using DWT following the procedure stated in
Zhang [19] and is same in all the approaches, while the second step is denoising
and performed in the second, third, and fourth approach.
On comparing the proposed PT with the previously proposed state-of-the-art
transformations, namely, AT and DT [17,20], with the help of Table 7.4, it is found
186 Health Monitoring Systems
FIGURE 7.4
An overview of the complete methodology.
that there is a significant mean improvement of 12.06% over AT and 20.73% over
DT for HC subjects, while for UH subjects it was found to be 20.67% and 32.33%
over AT and DT, respectively.
Figures 7.5 and 7.6 provide an insight into the correspondence between the qual-
ity of reconstruction and R2 values. With careful observations, it was found that
the R2 values of 80% or more can be considered to have high diagnostic value and
reconstructed leads with an R 2 value of 90% or more, thereby producing a com-
plete retrace of the originally measured signal.
In this work, designers [2] have proposed a personalized lead reconstruction
methodology to reconstruct S12 system from FV leads. A wavelet-based prepro-
cessing module has been introduced to remove BW and noise. Three variants
of wavelet transform have been evaluated, and it has been found that transla-
tion invariant wavelet transform (TIWT) outperforms others, however not with
significant differences. The proposed methodology was compared with the state-
of-the-art DT and AT.
C. Figure 7.7 shows summary of the proposed methodology. Here, authors envis-
age [3] a scenario where S12-lead ECG is being acquired in a remote or hospital-
based environment. Generally, in a remote health-monitoring scenario, these leads
are needed to be transmitted to nearby state-of-the-art health center for diagnosis,
storage, and updating of patient’s health record. At the transmission end, the con-
ventional S12 acquisition system is used to capture the ECG. Eight independent
leads of the S12 system are then passed through the lead component (LC) system
formation module. Using LC and S12 system together, the transformation coef-
ficients are obtained by employing LS fit method. Then, LC leads along with the
TABLE 7.4
Mean R2, rx, and bx Values Obtained on Applying the Proposed PT (Personalized Transformation), AT (Affine Transform), and DT (Dower Transform)
on HC and UH Subjects [2]
A1 A2 A3 A4 AT DT
Pervasive Computing
R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx
Healthy Control
I 92.89 0.963 0.926 93.01 0.963 0.927 93.05 0.964 0.928 93.00 0.963 0.927 76.58 0.899 0.812 66.91 0.893 0.808
II 97.36 0.987 0.975 97.43 0.987 0.976 97.46 0.987 0.976 97.38 0.987 0.975 93.84 0.976 0.927 92.28 0.973 1.028
V1 95.70 0.978 0.956 95.75 0.978 0.957 95.77 0.979 0.957 95.77 0.979 0.957 81.76 0.942 0.908 73.46 0.910 0.858
V2 93.90 0.969 0.943 93.94 0.969 0.944 93.95 0.969 0.944 93.96 0.969 0.943 75.79 0.921 0.929 63.87 0.806 0.651
V3 96.46 0.982 0.966 96.48 0.982 0.966 96.49 0.982 0.966 96.63 0.983 0.965 83.10 0.927 0.897 57.55 0.827 0.880
V4 97.42 0.987 0.974 97.44 0.987 0.974 97.46 0.987 0.974 97.65 0.988 0.976 82.34 0.937 0.925 72.56 0.921 0.972
V5 98.94 0.995 0.991 98.95 0.995 0.991 98.96 0.995 0.991 98.96 0.995 0.991 89.71 0.962 0.963 89.27 0.953 0.891
V6 98.98 0.995 0.991 99.00 0.995 0.991 99.01 0.995 0.991 99.00 0.995 0.991 92.70 0.970 0.997 90.58 0.958 0.844
Average 96.46 0.982 0.965 96.50 0.982 0.966 96.52 0.982 0.966 96.54 0.982 0.966 84.48 0.942 0.920 75.81 0.905 0.867
Unhealthy
I 90.04 0.948 0.909 90.34 0.950 0.913 90.39 0.950 0.913 90.40 0.950 0.913 48.32 0.866 0.884 60.23 0.867 0.681
II 92.79 0.963 0.936 92.99 0.964 0.939 93.04 0.964 0.939 93.07 0.964 0.939 85.54 0.932 0.894 78.64 0.921 0.999
V1 92.83 0.963 0.936 92.94 0.964 0.937 92.96 0.964 0.938 92.97 0.963 0.938 73.58 0.923 0.912 60.42 0.865 0.745
V2 91.06 0.953 0.916 91.14 0.953 0.917 91.13 0.953 0.917 91.16 0.953 0.918 67.18 0.893 0.867 55.62 0.807 0.612
V3 93.44 0.966 0.939 93.49 0.967 0.941 93.50 0.967 0.941 93.51 0.967 0.941 71.46 0.905 0.909 47.86 0.772 0.661
V4 94.51 0.972 0.951 94.56 0.972 0.952 94.57 0.972 0.952 94.57 0.972 0.952 72.67 0.909 0.890 46.56 0.779 0.711
V5 96.08 0.980 0.965 96.14 0.980 0.965 96.15 0.980 0.966 96.15 0.980 0.966 80.46 0.914 0.877 70.06 0.872 0.818
V6 95.45 0.976 0.957 95.55 0.977 0.958 95.57 0.977 0.958 95.57 0.977 0.959 82.81 0.943 0.917 69.36 0.903 0.832
Average 93.28 0.965 0.939 93.39 0.966 0.940 93.41 0.966 0.940 93.42 0.966 0.941 72.75 0.911 0.894 61.09 0.848 0.757
Note: PT was performed using various preprocessing approaches denoted in the table by A1–A4. The values denoted in ‘bold’ are mean R 2 values taken over the eight
reconstructed independent leads of standard 12-lead system.
187
188 Health Monitoring Systems
FIGURE 7.5
A comparative study between the reconstructed (red) and original (blue) signal. One patient each has been
taken from HC and UH categories, and the reconstruction has been performed using AT and PT methodologies.
The patients chosen have mean R 2 values of 83.89% (HC) and 72.82% (UH), obtained using AT, which is close to
the overall mean using AT, i.e. 84.48% (HC) and 72.75% (UH) as can be seen from Table 7.4: (a) reconstruction of
HC subject using AT, (b) reconstruction of HC subject using PT, (c) reconstruction of UH subject using AT, and
(d) reconstruction of UH subject using PT. (Image appears in color in eBook version of this book.)
FIGURE 7.6
A comparative study between the reconstructed (red) and original (blue) signal. One patient each has been
taken from HC and UH categories, and the reconstruction has been performed using AT and PT methodologies.
The patients chosen have minimum mean R 2 values of 88.41% (HC) and 75.22% (UH), obtained using proposed
PT, and the corresponding values when AT was used on the same subjects were 85.94% (HC) and 61.72% (UH):
(a) reconstruction of HC subject using PT, (b) reconstruction of HC subject using AT, (c) reconstruction of UH
subject using PT, and (d) reconstruction of UH subject using AT. (Image appears in color in eBook version of
this book.)
FIGURE 7.7
A summarized version of the proposed methodology. (Adopted from Ref. [1] and modified.)
190
TABLE 7.5
Mean R 2, rx, and bx Values of the Transformation of LC to S12 (1) and S12S to S12 (2) Systems for Both TWADB (72 Patients) and PTBDB
(First Recording of 290 Patients) [3]
TWADB PTBDB
I II V1 V2 V3 V4 V5 V6 Average I II V1 V2 V3 V4 V5 V6 Average
(1) R2 93.24 91.72 96.19 97.33 98.77 98.52 98.11 97.07 96.37 93.32 93.25 95.33 96.76 97.46 96.96 97.17 94.77 95.63
rx 0.974 0.956 0.984 0.989 0.994 0.993 0.992 0.987 0.984 0.970 0.968 0.978 0.985 0.989 0.987 0.987 0.975 0.979
bx 0.969 0.951 0.987 0.996 0.997 0.994 0.992 0.994 0.985 0.956 0.969 0.974 0.986 0.993 0.995 0.987 0.964 0.978
(2) R2 100 100 85.25 100 96.17 88.93 82.51 79.71 91.57 100 100 88.04 100 90.34 77.25 73.99 75.46 88.14
rx 1 1 0.954 1 0.981 0.942 0.926 0.918 0.965 1 1 0.938 1 0.955 0.902 0.886 0.887 0.946
bx 1 1 0.958 1 0.969 0.928 0.931 0.935 0.965 1 1 0.918 1 0.947 0.890 0.876 0.877 0.939
Health Monitoring Systems
Pervasive Computing 191
FIGURE 7.8
The mean (circle with green inside) and the range (whiskers) of R 2 values for both LC to S12 (a-TWADB, b-PTBDB)
and S12S to S12 (c-TWADB, d-PTBDB) transformations [3].
Figure 7.9 shows the comparison between the originally measured (blue) and
reconstructed (red) signal. One subject is each from TWADB (Figure 7.9a,b) and
PTBDB (Figure 7.9c,d). (Image appears in color in eBook version of this book.) The
two subjects have mean R2 values of 91.69% (TWADB) and 88.11% (PTBDB), which is
very close to the overall mean values stated in Table 7.5 for S12S to S12 transforma-
tion and the corresponding values for LC to S12 transformation were 96.71% and
92.64%. The R2 values of respective leads have been indicated in the figure. The R2
values in Figure 7.9 range from 54.61% to 100% and provide insight into the corre-
spondence between the quality of reconstruction and R2 values. Table 7.6 presents
the RMSE values for the features extracted from eight independent leads of S12 sys-
tem between the originally measured and reconstructed signal. Thirteen different
features were extracted for both the methodologies. Mean improvement of over 45%
was observed in the proposed LC to S12 compared to S12S to S12 transformation.
From a wide range of R2 values in Figure 7.9 i.e. 54.61%–100%, we have found
that R2 value of 80% or above can be considered to have significant diagnostic
value and a value of 90% or above is practically retracing the original waveform.
For PTBDB and TWADB using the proposed methodology, approximately, 96% of
patients were found to have mean R2 value of 80% and above. Similarly, 91% and
93% of patients were found to have mean R2 value of 90% and above for PTBDB and
TWADB, respectively. For S12S to S12 transformation, the fraction of patients above
with mean R2 values ≥80% were 86% (PTBDB) and 92% (TWADB). The fraction of
patients with mean R2 values ≥90% were 76% (PTBDB) and 83% (TWADB) [3].
192 Health Monitoring Systems
FIGURE 7.9
A comparative study between the two systems (LC and S12S) for the mean case S12S patient one each from
TWADB and PTBDB. (a and b) Comparison of eight independent leadsi.e. I, II, V1–V6 for the TWADB and
PTBDB. (c and d) A comparison of a mean case patient from PTBDB. The reconstructed lead (red) has been plot-
ted over original lead (blue). Corresponding R 2 values have been shown on respective plots [3]. (Image appears
in color in eBook version of this book.)
D. Figure 7.10 shows the two possible ways in which a patient can be registered at
the health center. During the registration process, the transformation coefficients
are generated which can be eventually used to reconstruct the S12 system. The
first scenario shows the online registration process and second shows the offline
registration process. ‘Online’ means that the patient needs not to be present in the
hospital/health-center physically [4]. In this case, the RL or Frank leads can be cap-
tured and transmitted to the hospital/health-center for reconstruction of S12-leads.
‘Offline’ means that the patient is present in the hospital/heath-center physically.
In this case, S12-lead reconstruction is done at the hospital/health-center itself. The
aforementioned remote health-monitoring service can benefit both ambulatory
patients and the patients living in rural or remotely accessed areas. The following
section discusses the registration process in the context of RL system used.
1. R3L systems to S12 system [21–24]
If the reconstruction methodology being adopted is the transformation of
R3L systems to S12 system, then for coefficient generation the acquisition of
only S12 system is required. These coefficients can then be used to reconstruct
S12 system on eventual readings.
2. FV system to S12 system
If the reconstruction methodology being adopted is the transformation of
FV to S12 system, then for the coefficient generation a simultaneous acquisition
TABLE 7.6
Pervasive Computing
Mean Root Mean Square Error (RMSE) Values for Various Features Extracted Using TDMG [9] for the Reconstruction Methodology
LC to S12 S12S to S12
Sl. no Feature (unit) I II V1 V2 V3 V4 V5 V6 V1 V3 V4 V5 V6
1 P duration (ms) 9.755 13.18 13.43 10.12 7.306 9.51 9.632 7.755 16.86 12.37 18 18.82 19.63
2 P height (µV) 52.89 63.59 92.53 84.82 50.52 81.96 37.58 37.03 133.3 110.1 177.7 113.4 89.27
3 PR interval (ms) 11.39 11.55 15.27 12.65 6 7.225 9.020 9.102 19.59 11.47 12.98 12.61 17.06
4 PR segment (ms) 10.78 6.449 15.14 8.163 5.551 9.388 5.265 5.020 18.33 11.35 16.78 12 16.04
5 Q peak (µV) 19.58 14.33 47.73 60.85 54.27 52.51 26.20 16.27 77.22 111.6 353.4 62.59 22.27
6 QRS length (ms) 10.65 7.184 7.633 3.755 4 6.816 5.184 6.122 9.306 9.143 11.63 10.61 10.37
7 QT interval (ms) 16.08 15.8 26.98 7.551 18.61 10 5.469 8.531 30.61 21.59 20.33 11.10 18.65
8 R height (µV) 47.26 51.03 47.12 99.95 60.18 88.54 48.41 31.72 78.21 182.4 480.4 158.9 90.75
9 S peak (µV) 9.125 10.36 19.27 0.035 0.036 2.825 9.714 10.20 28.90 2.689 4.220 19.85 17.29
10 ST interval (ms) 9.184 8.857 24.25 7.143 6.612 6.122 4.367 7.796 31.59 7.225 15.06 11.76 18.16
11 ST segment (ms) 14.16 11.92 18.61 10.37 7.184 11.27 6.490 10.90 28.86 7.633 17.76 18.08 20.37
12 T duration (ms) 43.32 64.35 107.5 114.5 139.0 105.2 73.27 43.96 161.2 199 202.5 135.7 98.76
13 T height (µV) 22.18 32.95 55.05 58.62 71.18 53.84 37.51 22.51 82.53 101.9 103.7 69.50 50.57
Note: Mean was taken over all the 49 patients (please see Section IV D). The leads I, II, and V2 formed the basis leads of S12S, so the RMSE values for them were zero
and therefore have not been reported [3].
193
194 Health Monitoring Systems
FIGURE 7.10
Proposed remote health-monitoring scenarios: (1) when patient may not be physically present for registration
(online) and (2) when patient is available for registration [4].
TABLE 7.7
Denotations of R3L Systems and Various ECG Features Extracted Using TDMG Algorithm [4]
Basis Leads of RL ECG Features
(Reduced Lead) System Denotation (Represents Unit) Denotation
I, II, V1 I P duration (ms) 1
I, II, V2 II P height (mV) 2
I, II, V3 III PR interval (ms) 3
I, II, V4 IV PR segment (ms) 4
I, II, V5 V Q peak (mV) 5
I, II, V6 VI QRS length (ms) 6
Frank leads X, Y, and Z FV QT interval (ms) 7
R height (mV) 8
S peak (mV) 9
ST interval (ms) 10
ST segment (ms) 11
T duration (ms) 12
T height (mV) 13
BB I 100 1 1 83.36 0.91 0.872 79.93 0.9 0.865 80.54 0.91 0.886 87.64 0.938 0.909 92.59 0.963 0.945 93.07
II 90.4 0.947 0.899 100 1 1 95.78 0.979 0.975 87.39 0.937 0.913 88.47 0.944 0.919 92.41 0.962 0.952 95.6
III 87.05 0.929 0.864 95.07 0.975 0.942 100 1 1 94.16 0.971 0.965 89.36 0.949 0.93 92.25 0.962 0.952 95.89
IV 84.68 0.919 0.849 77.53 0.873 0.779 89.47 0.943 0.896 100 1 1 91.6 0.963 0.954 92.03 0.962 0.951 94
V 78.24 0.889 0.815 64.08 0.798 0.688 67.28 0.813 0.728 74.2 0.848 0.805 100 1 1 94.24 0.971 0.963 89.23
VI 83.18 0.911 0.832 71.52 0.847 0.757 73.5 0.867 0.804 76.13 0.885 0.839 91.15 0.954 0.913 100 1 1 90.69
HC I 100 1 1 94.57 0.972 0.947 93.51 0.967 0.935 93.14 0.965 0.936 95.29 0.976 0.957 96.82 0.984 0.972 97.76
II 95.79 0.978 0.959 100 1 1 97.3 0.986 0.975 94.29 0.971 0.948 95.54 0.978 0.959 97.01 0.985 0.974 98.31
III 92.17 0.958 0.924 95.55 0.978 0.961 100 1 1 96.77 0.984 0.97 96.11 0.98 0.963 96.81 0.984 0.972 98.1
IV 82.23 0.901 0.827 80.59 0.888 0.816 92.57 0.962 0.93 100 1 1 97.66 0.988 0.978 96.6 0.983 0.969 95.79
V 73.18 0.846 0.74 66.38 0.794 0.678 77.04 0.868 0.777 92.18 0.959 0.924 100 1 1 97.78 0.989 0.981 92.2
VI 78.49 0.878 0.795 74.56 0.857 0.764 79.04 0.885 0.801 87.66 0.935 0.883 96.92 0.984 0.969 100 1 1 93.04
HY I 100 1 1 96.15 0.98 0.962 90.83 0.953 0.921 85.62 0.925 0.883 89.13 0.939 0.893 95.39 0.976 0.953 96.41
II 97.44 0.987 0.979 100 1 1 96.23 0.981 0.968 87.45 0.934 0.897 89.9 0.945 0.899 95.74 0.978 0.957 97.22
III 95.58 0.978 0.961 97.3 0.987 0.972 100 1 1 93.52 0.966 0.949 91.61 0.955 0.918 95.98 0.979 0.958 97.82
IV 91.88 0.958 0.925 88.94 0.941 0.888 93.48 0.967 0.93 100 1 1 94.45 0.971 0.941 96.63 0.983 0.962 97.1
V 85.25 0.921 0.861 78.51 0.878 0.797 77.9 0.883 0.796 86.81 0.929 0.887 96.91 0.984 0.967 100 1 1 93.77
VI 84.73 0.916 0.855 77.05 0.861 0.778 70.86 0.83 0.721 74.65 0.842 0.76 90.32 0.947 0.906 100 1 1 91.45
MI I 100 1 1 92.1 0.958 0.923 87.73 0.935 0.88 84.07 0.914 0.844 87.56 0.934 0.875 91.19 0.954 0.907 95.2
II 94.34 0.971 0.947 100 1 1 95.92 0.979 0.959 89.52 0.945 0.895 89.22 0.944 0.889 91.52 0.956 0.911 96.69
III 89.82 0.946 0.907 95.35 0.976 0.959 100 1 1 95.55 0.977 0.954 91.49 0.956 0.912 91.64 0.956 0.913 96.96
IV 82.21 0.903 0.834 83.34 0.909 0.844 92.55 0.96 0.93 100 1 1 94.84 0.974 0.947 91.83 0.958 0.915 95.37
V 73.83 0.852 0.75 68.54 0.818 0.701 73.96 0.851 0.753 84.38 0.907 0.85 100 1 1 94.72 0.973 0.946 91.26
VI 73.93 0.851 0.752 64.98 0.786 0.666 65.35 0.796 0.67 70.35 0.826 0.714 89.99 0.947 0.905 100 1 1 88.69
(Continued)
195
196
TABLE 7.9
Visualizing Mean R 2, rx, and bx Values of Various Categories for the Transformation of FV System to S12 System [4]
BB HC HY MI VA ND
R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx R 2 rx bx
I 90.89 0.952 0.898 95.99 0.98 0.96 95.34 0.976 0.959 92.41 0.961 0.93 94.15 0.971 0.955 93.61 0.968 0.927
II 94.29 0.97 0.944 99.04 0.995 0.99 98.33 0.992 0.983 96.54 0.982 0.967 97.41 0.987 0.982 93.55 0.966 0.947
III 88.29 0.916 0.88 93.18 0.965 0.929 95.41 0.976 0.952 93.55 0.967 0.94 91.99 0.958 0.918 91.84 0.952 0.92
aVR 91.06 0.953 0.914 98.49 0.993 0.985 97.71 0.989 0.979 93.86 0.969 0.945 95.59 0.978 0.966 91.6 0.955 0.918
aVL 91.05 0.953 0.904 91.75 0.957 0.918 93.63 0.967 0.937 92.18 0.96 0.926 90.28 0.945 0.914 94.24 0.971 0.963
aVF 96.01 0.98 0.961 98.09 0.99 0.98 97.34 0.987 0.974 95.99 0.979 0.96 96.41 0.982 0.966 95.6 0.977 0.959
V1 91.12 0.954 0.897 96.41 0.982 0.966 97.29 0.986 0.977 94.15 0.97 0.949 94.83 0.974 0.953 92.07 0.959 0.928
V2 89.31 0.943 0.89 94.03 0.97 0.942 96.39 0.982 0.961 92.29 0.96 0.929 91.19 0.951 0.916 91.28 0.955 0.925
V3 94.44 0.973 0.953 96.9 0.984 0.968 96.91 0.985 0.971 94.56 0.972 0.95 95.2 0.976 0.959 94.72 0.973 0.953
V4 93.75 0.97 0.961 98.06 0.99 0.982 95.82 0.979 0.974 95.66 0.977 0.959 97.36 0.987 0.984 97.04 0.985 0.967
V5 97.79 0.989 0.976 99.27 0.996 0.993 96.19 0.98 0.964 97.53 0.988 0.975 98.8 0.994 0.991 96.64 0.983 0.964
V6 98.7 0.994 0.991 99.51 0.998 0.996 99.24 0.996 0.992 97.54 0.988 0.974 98.74 0.994 0.988 98.12 0.991 0.983
Average 93.06 0.9623 0.931 96.73 0.983 0.967 96.63 0.983 0.968 94.69 0.973 0.950 95.16 0.975 0.958 94.11 0.969 0.9436
197
198 Health Monitoring Systems
TABLE 7.10
Mean RMSE Values of the ECG Features Extracted Using TDMG Algorithm [9] for All the RL
Systems [4]
Sr. Feature (in Unit) I II III IV V VI FV
1 P duration (ms) 6.700 5.536 5.423 5.644 5.888 6.357 9.70
2 P height (mV) 55.47 40.04 37.30 51.00 82.70 86.57 60.24
3 PR interval (ms) 9.058 8.639 9.281 10.96 11.80 12.27 17.20
4 PR segment (ms) 8.982 8.567 8.595 9.36 10.68 11.45 15.81
5 Q peak (mV) 63.60 51.33 44.34 61.49 116.4 150.6 97.75
6 QRS length (ms) 5.997 4.710 4.355 4.966 7.407 8.527 9.40
7 QT interval (ms) 11.97 11.26 10.64 12.41 14.38 15.98 21.41
8 R height (mV) 79.96 65.48 54.85 65.47 102.3 133.1 92.78
9 S peak (mV) 14.25 12.87 16.67 20.79 42.10 59.59 29.73
10 ST interval (ms) 10.94 9.889 9.629 10.94 12.69 14.72 19.32
11 ST segment (ms) 10.45 9.542 9.316 10.96 13.36 15.00 18.49
12 T duration (ms) 13.33 11.85 11.03 12.04 12.21 14.88 21.16
13 T height (mV) 52.42 38.26 33.79 54.17 95.76 98.51 53.61
peak was measured in comparison to the features measuring the horizontal inter-
vals. Table 7.11 presents a comparison between FRM and SRM in the context of
remote healthcare monitoring applications. The aforementioned mean values and
the values used in Figure 7.11 were taken over the complete database (all 275 patients).
Figure 7.12 shows a comparative study between the reconstructed (red) and the
measured (blue) ECG signal; Figure 7.12a–c shows the reconstruction of lead set
1 using SRM, i.e. FV to S12 for the worst, 80% and the best case mean R 2 values.
Figure 7.12d–i shows the reconstruction of lead set 2 (V1–V6) for all the RL sys-
tems with the basis leads starting from V1 (top) to V6 and FV system (bottom) [4].
(Image appears in color in eBook version of this book.)
Figure 7.13 provides the box plot of lead-wise mean RMSE for 13 different fea-
tures extracted using TDMG over the complete database. Figure 7.13a–h presents
thirteen boxes numbered 1–13 each corresponding to a particular feature. The
correspondence between the labels in the horizontal axis and features has been
described in Table 7.7. Figure 7.13a–f follows from the reconstruction results of
RL systems for lead set 2 with basis lead following the order: a–V1, b–V2, c–V3,
TABLE 7.11
Comparison of FRM and SRM in Context of Remote Healthcare Applications [4]
R3L System FV System
1 Five electrodes system Eight electrodes system
2 Three leads Three leads
3 Inconsistent reconstruction of precordial leads Consistent reconstruction of precordial leads
4 Comparatively bad reconstruction of V5 and V6 Comparatively better reconstruction of V5 and V6
5 Leads I, II, III, aVR, aVL, and aVF are obtained Comparatively less accurate and information is
with approximately no information loss lost in the reconstructed signal
6 Not much change in already existing acquisition A different system is required which can acquire
system is required both ECG and VCG
7 Online and offline registration possible Online registration difficult
Pervasive Computing 199
FIGURE 7.11
The bar plot of lead-wise mean R2 values. (a) The RL to S12 (blue) versus FV to S12 for lead set 1 i.e. I, II, III, aVR, aVL,
and aVF. (b) A comparative study between all the seven reconstruction methodologies for reconstruction of precor-
dial leads (lead set 2): leftmost to sixth bar correspond to RL systems with basis lead in the order V1 (blue), V2 (green),
V3 (magenta), V4 (yellow), V5 (red), V6 (black), and the rightmost corresponds to FV (cyan) system, respectively.
(Image appears in color in eBook version of this book.)
FIGURE 7.12
(a–c) Reconstructed signal (red) overlapping the original signal (blue), obtained using SRM, of three different
patients with worst (71.3%), 80% and best case (99.61%) mean R 2 values for lead set 1. (d–i) A similar comparison
for lead set 2 for both FRM and SRM, where the first six boxes in all the subfigures correspond to FRM in the order
of R3L systems with leads V1–V6 as the precordial lead in the basis lead set and the last box corresponds to SRM.
(Image appears in color in eBook version of this book.)
200 Health Monitoring Systems
FIGURE 7.13
(a–h) A box plot of RMSE values [4]. Starting from left to right with R3L systems I–VI for lead set 2. The left
subfigure last row corresponds to lead set 2 and right subfigure corresponds to lead set 1 when S12 was recon-
structed from FV. The labels 1–13 on the horizontal axis correspond to the respective features extracted from
TDMG as mentioned in the text. For details about denotations, please refer Table 7.7 [4].
d–V4, e–V5, and f–V6 (all of these basis lead sets essentially contain leads I and II).
Figure 7.13g,h follows from the results of SRM for lead set 2 and lead set 1,
respectively [4].
E. Figure 7.14 shows the summary of the methodology for this investigation [10].
First, the databases are denoised using the preprocessing module followed by
PCA module for the construction of FV from S12 system. IDT and KT were also
used to reconstruct Frank’s leads. All the three sets of derived Frank leads were
then compared with actually recorded Frank leads. Finally, the S12 leads were
reconstructed from the derived FV leads, obtained using the proposed PCA-
based methodology, and were compared with the originally measured S12 leads.
The personalized reconstruction of S12 system from derived FV system further
includes a module for generation of transformation coefficient [10] (Tables 7.12
and 7.13).
Figure 7.15 shows the working principle of PCA. For a 2D case, PCA finds a lower
1D linear vector which when the dataset is projected produces lowest mean square
of the perpendicular distances from the points to the vector. This linear vector is
known as a principal component. For higher dimensions, a lower dimensional
plane or hyperplane represented by spanning vectors (principal components)
Pervasive Computing 201
FIGURE 7.14
A complete summary of the methodology followed in this study for reconstruction of Frank system from
standard 12-lead system and then using the derived Frank leads (DX, DY, and DZ) to reconstruct standard
12-leads using PT employing least-square fit method and heart-vector projection theory [10].
TABLE 7.12
Fractional Content of Heart Dipole Components in S12 Leads for PTB Database (PTBDB) [10]
12 Lead x/y x/z y/x y/z z/x z/y
I 7.338 10.06 0.136 1.370 0.099 0.73
II 0.164 8.155 6.112 49.85 0.123 0.02
III 0.589 1.296 1.699 2.202 0.771 0.45
AVR 0.129 0.245 7.767 1.904 4.079 0.52
AVL 0.797 3.560 1.255 4.468 0.281 0.22
AVF 0.355 1.583 2.821 4.465 0.632 0.22
V1 0.677 2.079 1.477 3.071 0.481 0.32
V2 2.773 3.352 0.361 1.209 0.298 0.82
V3 1.239 8.521 0.807 6.876 0.117 0.14
V4 0.834 17.23 1.199 20.67 0.058 0.04
V5 0.445 5.094 2.254 11.48 0.196 0.08
V6 0.996 4.884 1.004 4.905 0.205 0.20
is searched. When PCA was applied on a subset (I, V5, V6) of S12 system, the
first principal component obtained was found to have 98.69% resemblance with
the originally measured X lead of FV system, as shown on the right hand side of
Figure 7.15.
The derived Frank leads, obtained using three different methodologies, namely,
the proposed PCA-based method, IDT and KT have been compared independently
with the originally recorded Frank lead using the evaluation metrics in Table 7.14.
202 Health Monitoring Systems
TABLE 7.13
Fractional Content of Heart Dipole Components in S12 Leads for CSE Database (CSEDB) [10]
x/y x/z y/x y/z z/x z/y
I 7.207 13.79 0.139 1.914 0.072 0.523
II 0.315 2.829 3.178 8.990 0.354 0.111
III 0.540 2.927 1.850 5.417 0.342 0.185
aVR 1.141 126.5 0.877 110.9 0.008 0.009
aVL 1.082 7.522 0.924 6.953 0.133 0.14
aVF 0.149 0.833 6.722 5.594 1.202 0.19
V1 2.637 0.592 0.379 0.224 1.691 4.45
V2 0.295 0.086 3.385 0.290 11.65 3.43
V3 3.301 0.574 0.303 0.174 1.744 5.75
V4 81.77 1.401 0.012 0.017 0.714 58.36
V5 6.204 3.472 0.161 0.560 0.288 1.78
V6 3.155 14.89 0.317 4.718 0.067 0.22
FIGURE 7.15
Effect of principle component analysis on a data set and when applied on 3-lead subset of standard 12-lead
system. The subset in the figure includes I, V5, and V6. The resulting first principal component is shown as a
continuous plot in green whose resemblance to originally measured Frank’s X lead is 98.69%.
TABLE 7.14
Number of Subjects (in %) with Various Values of Reconstruction Accuracy of Frank System from
Standard 12-Lead System for Both Physikalisch-Technische Bundesanstalt database and CSE
database Using Our Proposed Methodology (PCA-Based), Inverse Dower Transform (IDT), and
Kors Transform (KT) [10]
No. of Patients in CSEDB (in %) No. of Patients in PTBDB (in %)
Mean R Values
2 PCA-Based IDT KT PCA-Based IDT KT
>0% 100% 98.8% 99.6% 97.63% 73.04% 56.47%
>50% 90.4% 94% 97.2% 73.95% 47.91% 47.18%
>80% 68% 66.8% 75.2% 40.98% 30.05% 34.97%
>90% 48.8% 40% 55.6% 19.85% 11.66% 19.31%
Overall mean R2 value 81.6% 80.93% 85.52% 65.77% 34.15% 26.89%
Overall mean correlation coefficient 0.8289 0.6708 0.6344 0.9080 0.9046 0.9276
Note: CSEDB, CSE database; PTBDB, Physikalisch-Technische Bundesanstalt database.
Pervasive Computing 203
TABLE 7.15
Fraction (in %) of Subjects in Physikalisch-Technische Bundesanstalt database with
Various Reconstruction Accuracy Values for Reconstruction of Frank System from
Standard 12-Lead System for Healthy Control (HC), Unhealthy (UH) for 290 Records, and
Remaining Records [10]
Mean R 2 Values Healthy Control: 52 Unhealthy: 238 Remaining Records
>0% 100% 99.2% 96.14%
>50% 92.31% 92.44% 58.69%
>80% 82.69% 67.65% 17.76%
>90% 63.46% 47.06% 2.703%
Overall mean R2 value 86.63% 80.90% 52.84%
Overall mean correlation 0.933 0.904 0.764
TABLE 7.16
Mean R 2 and Correlation Coefficient Values for the Reconstruction of Standard 12-Lead System
from Derived Frank Leads, Derived Using PCA-Based Methodology and Its Comparison with
the Reconstruction Result Using Originally Measured Frank Leads [10]
PTB CSEDB
Derive Frank Leads Original Frank Leads Derive Frank Leads Original Frank Leads
Leads R 2 (%) CC (r x) R 2 (%) CC (r x) R 2 (%) CC (r x) R 2 (%) CC (r x)
I 48.85 0.527 46.39 0.494 92.06 0.9646 89.91 0.950
II 97.57 0.987 92.21 0.959 98.36 0.9928 95.29 0.977
V1 87.54 0.930 92.91 0.961 92.45 0.9609 91.61 0.961
V2 97.32 0.986 83.87 0.912 97.67 0.9884 87.41 0.954
V3 96.56 0.983 85.73 0.920 96.28 0.9840 92.86 0.967
V4 94.82 0.972 88.34 0.935 93.49 0.9676 95.41 0.977
V5 97.25 0.986 90.42 0.947 97.15 0.9871 97.46 0.988
V6 97.31 0.987 95.39 0.975 97.54 0.9912 97.46 0.988
Mean 89.65 0.9198 84.41 0.8878 95.62 0.9795 93.43 0.970
CC, correlation coefficient.
204 Health Monitoring Systems
FIGURE 7.16
A comparative study between original (in blue) and derived (in red) signals of Frank system when constructed
from Standard 12-lead system. (a, d, and g) Construction using our proposed PCA-based method for the sub-
jects, which had mean, median, and maximum R 2 value in PTBDB. (b, e, and h) Reconstruction for inverse
Dower transform. (c, f, and i) Reconstruction for Kors transform for the same subjects. (Image appears in color
in eBook version of this book.)
FIGURE 7.17
A set of 10s ECG signal of record patient 001.
120,000 images as a training set to train the CNNs and for validation and 30,000
images as a testing set to evaluate the proposed method. The aim of this transfor-
mation is not only to satisfy the input requirements but also to obtain more details
of the input signal.
Authors computed the gradient of the input signal, then repeat and arrange
them as Figure 7.18 (1 presents no. 1 original signal and 1′ presents the gradient of
no. 1 signal).
Figure 7.19 is the structure schematic of the proposed method with CNN.
Table 7.17 shows the diagnostic classes of the remaining 268 subjects.
As shown in Table 7.18, the CNN-based method shows a better reconstruc-
tion result, compared to the ANN-committees-based method and the multiple-
regression-based method.
FIGURE 7.18
The morphology of the input signal.
206 Health Monitoring Systems
FIGURE 7.19
The schematic of the structure of the proposed method.
TABLE 7.17
The Diagnostic Classes of PTB Diagnostic ECG Database [5]
Class to Diagnose Total Number of Subjects
Myocardial infarction 148
Cardiomyopathy/Heart failure 18
Bundle branch block 15
Dysrhythmia 14
Myocardial hypertrophy 7
Valvular heart disease 6
Myocarditis 4
Miscellaneous 4
Healthy controls 52
Figure 7.20 is the line chart of the results. The results show that the proposed
method performs better in the reconstruction of lead V1, V4, V5, and V6.
A 12-lead ECG is one of the most important cardiac diagnostic tools. The need
for synthesis of 12-lead ECG from fewer leads increases dramatically as portable
devices provide often limited electrodes. The reduction of running time during
synthesis is important, considering the large amount of signal and the real-time
request. In this chapter, authors investigated the CNN method in detail and apply
it to the reconstruction of an ECG signal. Compared to linear regression and ANN,
this methodology shows not only better similarity between the reconstructed
and the original ECGs but also less time consumption. Authors also believe that
the generic synthesis method can be further modified by using a higher volume
training database. Authors vowed to continue to improve the effectiveness of the
generic approach and provide better support for more patient types [5].
G. To synthesize the missing (V1, V3, V4, V5, and V6) ECG signals from the recorded
(I, II, and V2) 12-lead ECG subset, we use an ensemble of N multilayer feed-
forward ANNs trained by means of a supervised back-propagation algorithm.
Pervasive Computing 207
TABLE 7.18
Correlation Coefficients (rx) between the Original ECG Signal and the
Reconstructed ECG Signal Obtained by the below Three Methods [5]
Methods Lead R (%)
CNN-based methodology III, aVR ,aVL, aVF 100
V1 94.40
V3 94.48
V4 93.80
V5 94.65
V6 97.44
ANN-committees-based III, aVR ,aVL, aVF 100
methodology V1 91.40
V3 95.66
V4 89.74
V5 94.54
V6 97.15
Multiple-regression-based III, aVR ,aVL, aVF 100
methodology V1 91.42
V3 96.51
V4 89.20
V5 93.01
V6 96.58
ANN, artificial neural networks.
FIGURE 7.20
The line chart of similarity obtained by three methods.
Each individual ANN consists of one input layer with three input neurons (one
for each recorded signal), one output layer with five output neurons (one for each
derived signal), h = 1 hidden layer and n = 15 neurons per hidden layer, as shown
in Figure 7.21.
Table 7.19 displays the rms values and the correlation coefficients between the first
and second Cardiette ECGs of DS2, after removal of the 5% extreme values (N = 35) [6].
208 Health Monitoring Systems
FIGURE 7.21
The architecture of the ANN used to synthesize the five missing V1, V3, V4, V5, and V6 leads (output layer) of
the 12-lead ECG using a 3-lead ECG (I, II, and V2) as input layer, h (typically 1 or 2) hidden layers and n neurons
per hidden layer [6].
TABLE 7.19
RMS (in µV) and Correlation Coefficients (rx) Among
the Original I, II, and V2 Leads of the First and the
Second Standard 12-Lead ECGs Recordings for Every
Patient of Dataset DS2 (N = 35) [6]
Lead RMS (±SD) r (±SD)
I 60 (±30) 0.93 (±0.09)
II 73 (±32) 0.85 (±0.18)
V2 125 (±58) 0.95 (±0.07)
V1 82 (±44) 0.95 (±0.08)
V3 135 (±57) 0.95 (±0.04)
V4 136 (±56) 0.92 (±0.08)
V5 139 (±59) 0.90 (±0.13)
V6 100 (±41) 0.91 (±0.10)
This study [6] investigates the accuracy of the reconstruction of the 12-lead ECG
from a RL set using generic and patient-specific ANNs. The results suggest that
ANN represents a rather interesting and very promising approach to improve
the current 12-lead ECG synthesis method based on linear transforms [27,28],
especially for the patient-specific approach. The differences between the original
and the reconstructed ECGs were mainly ascribable to electrode displacements
or to hour-to-hour changes in the ECG and not on the synthesis methodology
(Figure 7.22).
Pervasive Computing 209
FIGURE 7.22
The reconstruction of a standard 12-lead ECG out of the three-lead PEM ECG. The synthesis of the five m issing
V1, V3, V4, V5, and V6 leads is obtained by averaging the outputs of a committee of 50 neural networks [6].
7.4 Conclusion
A 12-lead ECG is one of the most important cardiac diagnostic tools. The need for s ynthesis
of 12-lead ECG from fewer leads increases dramatically as portable devices provide often
limited electrodes. In this chapter, we have explored and have reported various studies,
which provided solutions to the aforementioned problems.
The first remedy proposed a personalized reconstruction methodology for reconstruc-
tion of missing precordial leads of S12 and ML12 systems. Second solution proposed a
robust and accurate method for the reconstruction of S12-lead system from FV system
using PT matrices targeting personalized remote health-monitoring applications. Third
strategy proposed a personalized R3L system formation methodology, which employs
PCA, thereby reducing redundancy and increasing SNR ratio, hence making it suitable
for wireless transmission. Forth, a technical methodology to the medical practitioners
210 Health Monitoring Systems
has been proposed for selection of RL systems suitable for personalized remote health-
monitoring applications. Subsequently, a novel S12-lead ECG reconstruction methodol-
ogy is also proposed which is shown to be more reliable than the state-of-the-art lead
reconstruction methodologies. Subsequently, VCG-based substitution to complement S12
system in diagnosis of CVDs has been proposed. This is followed by CNN-based method,
which proved to be more accurate and time-saving for deployment in nonhospital situa-
tions to synthesize a S12-lead ECG from a RL-set ECG recording. All these solutions have
been summarized in this study along with their promising results. Every methodology is
still in process of further exploration for better results.
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8
Trusted Digital Solutions and
Cybersecurity in Healthcare
I. E. Lamprinos
Intracom S.A. Telecom Solutions
CONTENTS
8.1 Introduction: Background and Driving Forces.............................................................. 213
8.1.1 The Notion of Digitization in Healthcare........................................................... 213
8.1.2 The Notion of Trust................................................................................................ 214
8.1.3 Patient Role in Decision Making.......................................................................... 215
8.1.4 Patient Data Protection Framework..................................................................... 216
8.2 Trusted Digital Solutions in Healthcare.......................................................................... 217
8.2.1 Digital Solutions for Physical Activity Monitoring........................................... 217
8.2.2 Digital Solutions for Personalized Medicine...................................................... 218
8.2.2.1 Person-Centric Decision Support Systems........................................... 218
8.2.2.2 The Role of Machine Learning and Artificial Intelligence................ 220
8.2.2.3 Indicative Application Domains............................................................ 221
8.2.3 Personal Health Records........................................................................................ 221
8.3 Cybersecurity in Healthcare.............................................................................................222
8.3.1 The Needs................................................................................................................222
8.3.2 Technology Trends Related to Cybersecurity in Healthcare........................ 223
8.3.2.1 Homomorphic Encryption......................................................................223
8.3.2.2 Blockchain Applied in Cyber-Security Solutions for Healthcare
Applications���������������������������������������������������������������������������������������������223
8.3.2.3 Sandboxing............................................................................................... 224
8.3.3 Service Provision Trends Related to Cybersecurity in Healthcare................. 224
8.4 Conclusion...........................................................................................................................225
References......................................................................................................................................225
In this context, it does not come as a surprise that in Europe, for example, healthcare
market comprises one of the core sectors that are set to realize the Digital Single Market
notion [1]. There are several good reasons for this: the incorporation of Information and
Communications Technologies (ICT) in almost every aspect of healthcare delivery, the
digitization and automation of data exchange, the breakthrough of artificial intelligence
and big data analytics, the enormous penetration of consumer-oriented wearable medical-
grade and lifestyle-related sensing and control devices, all these are profoundly changing
the way (and the dynamics) healthcare services are provided.
FIGURE 8.1
Privacy has gained significant interest among end-users with the onset of sensors and
devices in various walks of life. The issue of the General Data Protection Regulation [4]
by the European Union (EU) is a characteristic action towards this direction (this topic is
further analyzed in Section 8.1.4). While the issue of privacy is very broad and deep, key
aspects of privacy that characterize a digital solution are the mechanisms put in place for
blocking de-anonymization of data while at the same time guaranteeing data utility.
Compliance pertains to legal aspects related to digital solutions and particularly with
data protection laws. To accomplish this need, digital solutions need to embed mecha-
nisms for guaranteeing liability for data, system integrity, and security breaches, while
being transparent as for the applicable data sovereignty laws.
Last but not least dimension of the trust notion is security. This dimension pertains
mostly to the technical framework that ensures data protection against malicious attacks.
This framework includes mechanisms such as end-to-end encryption of the communi-
cated data and strict rules for control, audit, back up, update and upgrade of the software
and hardware components of the digital solution. This particular issue is further analyzed
from a technical perspective in Section 8.3.
outcomes and assist the patient in selecting a therapy that is best matched to the patient’s
values. This role is similar to the informed model in that the provider is responsible for
providing the necessary facts about the disease state and the available therapeutic options.
Also, the provider has the responsibility to help the patients discover their own values
and understand how these relate to the decision. It is different in that the provider must
also assume the role often played by a decision analyst, such as in business consultations,
where the provider’s task is not only to educate the patients but to guide them through
clarifications. The proportion of collaborative role is believed to be 50%–60% [6]. Like the
informed group, these patients tend to be in a younger age group and have a strong edu-
cational background.
Finally, in the deliberative model of clinical decision-making, the healthcare provider
abandons objectivity. The provider’s goal is to influence the patient’s beliefs about the like-
lihood of outcomes and values so that they reflect what the provider feels are the patient’s
best interests. The provider functions in the role of friend or a teacher in that the provider
may suggest to the patient which decision they think is the best for the patient. Further, the
provider may attempt to persuade the patient to change values the provider believes harm-
ful or change mistaken beliefs about the likelihood of outcomes if the provider believes
this to be the best in the interest of the patient. This differs from a collaborative model,
where the provider primarily seeks to discover patients’ values and from the paternalistic
model, in that authority is not delegated to the physician. For an action to occur, both the
patient and the provider must believe that the chosen path is in the patients’ best interest.
Studies show that the number of patients desiring a deliberative role ranges from 10% to
20% [6]. These patients tend to be in a younger age group, highly educated, and female
gender.
Lamprinos et al. [10] present an ICT system for accurately monitoring and promoting
the physical activity of elderly in both structured contexts and in daily life at home for
both personal (primary prevention) and professional use (secondary prevention and reha-
bilitation) (Figure 8.2). The system consists of five major components: body-worn sensors
(IMUs, HR monitor) and a mobile unit are used to acquire information; innovative infor-
mation processing technology is used to extract the relevant parameters of physical activ-
ity; motivational user interfaces provide instant feedback and guidance during exercising
and encourage elderly to improve their level of physical activity and learn associated good
practices; and finally, a personal health record (PHR) with web and smart-TV interfaces
enable management, sharing and reviewing of collected activity data and facilitate health-
care professionals in the follow-up of personalized training plans.
In the commercial sector, an increasing number of providers of physical activity moni-
toring hardware and software solutions provide open APIs for the integration of their
artifacts with third-party service providers. For example, the health and sports tracker,
Runkeeper, provides an API to save, store, read, and modify personal health information
of a user in a cloud-based service environment. FitBit is a provider of tracking sensors
and apps for activity, health, nutrition, and sleep monitoring. MapMyRun also provides a
health service API called MapMyHealth and collaborates with various providers of sports
equipment such as Nike, Polar, Garmin, CycleOps, or Wahoo.
FIGURE 8.2
An indicative platform for out-of-hospital rehabilitation and physical activity monitoring.
Trusted Digital Solutions and Cybersecurity 219
life. These data, often rich with vital information, are scattered all over the place and often
remain underutilized.
By integrating carefully selected data sources into a single platform, these data, and
eventually the information derived from it can be used more effectively and in conjunction
with established medical knowledge to help patients better self-manage their disease. In
fact, the combination of dynamically collected data with established knowledge, properly
co-processed by an intelligent decision support system (DSS), forms an ideal framework
for wellness and disease management (Figure 8.3).
Health promotion and disease self-management can be served and promoted by lever-
aging several different sources of data (patient specific, disease specific, ambient envi-
ronmental data, etc.) originating from personal health systems (i.e. observations of daily
living, lifestyle, and behavioral data), knowledge sources (e.g., clinical guidelines, predic-
tive models), and other sources (biological, therapeutic, environmental, or occupational
exposure, etc.).
All these data and knowledge sources feed DSSs that are used either directly by the
patients or indirectly via their supervising medical team, to raise awareness and empower
the patients to participate in various aspects of health management (wellbeing and preven-
tion, disease monitoring and management).
Such DSSs provide timely information and recommendations for disease self-
management, improve patients’ interaction with their healthcare professionals, and even
FIGURE 8.3
Leveraging health, environmental, and administrative data and medical knowledge in decision support
systems.
220 Health Monitoring Systems
foresee an adverse event for high-risk individuals. Often, the recommendations are related
to medication and aim at establishing personal changes in lifestyle (e.g., increasing physi-
cal activity, lowering caloric intake).
FIGURE 8.4
High-level process flow related to machine learning in predictive modeling.
Trusted Digital Solutions and Cybersecurity 221
decisions are less likely to change healthcare provider’s behavior and disease management
outcomes than those that translate risk into a decision recommendation [13].
Predictive models can be used to identify high-risk individual cases and transmit anno-
tated data back to the healthcare provider, triggering updates to their clinical assessment
[14]. If properly validated in several different populations, predictive models can also
form the basis for patient-oriented decision support systems. Such models are particularly
attractive, as they can optimize user-friendliness and may be introduced quickly and effi-
ciently in disease self-management solutions.
by individuals themselves. The common goal of a PHR is to provide access to and man-
agement of personal health data and eventually support personal health management and
enable better decision making. A PHR provides a single, detailed, and comprehensive
track record of a person’s health status and healthcare activities. It facilitates informed care
decisions. Additionally, a PHR can help people to prepare for appointments, facilitates care
in emergencies, and helps track health changes.
According to the American Health Information Management Association and the
American Medical Informatics Association, a set of basic principles when selecting and
using a PHR should be applied [17]. In detail, every person is ultimately responsible for
making decisions about his or her health and should have access to his or her complete
health information. This should ideally be consolidated in a comprehensive record. The
information in the PHR should be understandable to the individual, accurate, reliable,
and complete. The integration and secure communication of PHRs with EHRs of provid-
ers allows data to be shared between a consumer and his or her healthcare team. People
should have control over how their PHR information is accessed, used, and disclosed to
concerned parties. All secondary uses of PHR data must be disclosed to the consumer, with
an option to opt-out, except as required by law. The operator of a PHR must be accountable
to the individual for unauthorized use or disclosure of personal health information.
A PHR should contain any information relevant to an individual’s health. For example,
a PHR should contain information related to personal identification, including name and
birth date, people to contact with in case of emergency, names, addresses, and phone num-
bers of physicians, dentists, and specialists, health insurance information, a list and dates
of significant illnesses and surgical procedures, current medications and dosages, immu-
nizations and their dates, test and physical examination results, allergies or sensitivities to
drugs or materials, health-related information of the family members, nutrition and physi-
cal activity logs, and educational material.
8.3 Cybersecurity in Healthcare
8.3.1 The Needs
The incorporation of ICT in healthcare and the digitization of data collection and exchange
transform the way healthcare services are provided. Health service providers collect, ana-
lyze, store, and exchange large amounts of data collected by wearable, portable, or station-
ary medical and wellness devices or imported by the patients and their caregivers into IT
systems.
Being characterized by openness and broad accessibility, this IT infrastructure also
inherits various cyber risks and vulnerabilities. For example, the universal proliferation
of consumer electronic equipments such as smartphones and wearables in health services
brings substantial risks due to the engagement of an audience with low digital literacy
and significant ignorance to the dangers of the digitally connected world. It is not unusual
that such portable devices, being used to manage a personal health record and collect and
locally store vital signs measurements, are secured by a four numbers password that can
be “cracked” in less than 20 s [18].
The situation is not significantly more promising when it comes to corporate systems
of hospital and care centers. These entities offer services the availability of which is of
Trusted Digital Solutions and Cybersecurity 223
paramount importance. Such services can be divided into two major types: healthcare
services and administrative services [19]. The first type ensures continuity of care for
the patients, the disruption of which may have a significant impact on their health sta-
tus. The administrative services are dedicated to the smooth hospital workflow. Systems
handling work orders, medicine inventories, prescriptions, bills, or appointments are
part of these services. Their unavailability is, however, less critical as long as their down-
time remains of short duration. In this context, healthcare service providers are primary
targets of cyber-attacks, mainly related to data theft, denial-of-service, and ransomware.
A retrospective observational study of all available reported data breaches in the United
States from 2013 to 2017 revealed 1,512 data breaches that affected circa 154 million
patient records [20].
Following the above analysis, it is obvious that there is a tremendous need for novel
solutions that can bring trust and security in the IT systems used in healthcare services.
Indicative such solutions are being described in the subsequent sections.
In this context, a single centralized log management and auditing system is not feasible,
both for technical reasons (as it introduces a single point of failure) and for organizational
and possibly legal reasons (each entity must have a copy of the logs under its direct control
and each entity should not be forced to trust others’ management procedures).
A blockchain-based approach can successfully overcome such limitations. Each entity
has its own log system that concentrates and stores all the relevant descriptive information
of the operations performed by users that are relevant to a healthcare service. Each opera-
tion that is performed by users of the system and results in an exchange of data is logged
by all participating stakeholders into a timestamped and digitally signed block.
Many of the immediate challenges present in healthcare cybersecurity will not be met
by blockchain technology [24]. For instance, blockchain can do little against attackers
attempting to steal patient data, phishers looking for credentials or authorization to trans-
fer money or steal financial documents, insiders looking at the records of patients outside
of their care, as all of these data must somehow be accessible and readable within the ser-
vice provision physical environment.
However, blockchain may address a threat that is rapidly approaching: integrity-based
attacks. In these attacks, malicious insiders or external actors modify data, in such a way
that is not trackable, leading to major patient safety and institutional trust concerns. Such
an intervention may be related, for example, to the integrity of information related to a
patient’s history of drug allergy. Blockchain technology is an excellent tool to address such
a challenge, enabling an immutable record of changes that can be retroactively examined
to see precisely what was changed, when it was changed and who changed it.
8.3.2.3 Sandboxing
The concept of sandboxing pertains to the case where a predefined and tightly controlled
environment is being set up in which software applications run. The operating system
gives the application just enough access to perform activities that it deems “safe” and noth-
ing more. Also, the application is only given access to a predefined area of computer and
memory access that is segregated and unique to its owner [25].
Sandboxing is a highly effective means of detecting previously unknown threats.
Operating at key locations in the security fabric, a sandbox provides an isolated, secure
environment to examine unrecognized code. If it determines it’s a threat, it automatically
propagates the threat information throughout the fabric, immunizing the entire network
against further damage [26].
Sandboxing is a promising technique to be applied against medical devices hijack. This
is particularly true for legacy medical devices running on outdated operating systems for
which the security is no longer supported, which renders them good candidates as entry
points for cyber-attacks.
Despite the main advantages of sandboxing, this technique suffers from two important
drawbacks [27]: first, sandboxing is quite time- and resource-intensive, and, second, sand-
boxing can be evaded. For example, a threat might be programmed to remain dormant
until a future date, so that during sandboxing it appears benign. Another effective evasive
technique is to make the malware able to detect whether it is in a virtual environment and
to remain dormant until it finds itself in a real desktop or other device.
and storage onto the internet, provided that security and data ownership concerns are
addressed. Towards this direction, the emerging security as a service (SecaaS) model
offers a novel security solution for cloud services’ users. This model encompasses a variety
of advantages such as authentication as a service (AaaS), encryption as a service (ENCaaS),
as well as dedicated virtual firewalls and web application firewalls to the cloud infra-
structure costumers. It also assists healthcare organizations in strengthening their virtual
private clouds with controls applicable to their business domain.
8.4 Conclusion
In this chapter, we presented major driving forces behind digitization in healthcare.
We also explored the state of play regarding trust, analyzing its key dimensions as well as
the patients’ data protection framework. Cyber-security is a must in all digital solutions.
With this in mind, we presented multidisciplinary technologies and solutions (e.g., homo-
morphic encryption, blockchain-based cyber-security solutions, and sandboxing) that are
capable to assure data privacy, security, and protection of healthcare-related applications
and services, such as physical activity monitoring and personal health records. All these
aspects are relevant and timely taking into account the transformation of our world into a
digitally driven environment of products, processes, and services.
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and cybersecurity: National trends in data breaches of protected health information, JAMIA
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ing. [Last accessed 09/09/2019].
9
Novel e-Health and m-Health Services
I. E. Lamprinos
Intracom S.A. Telecom Solutions
CONTENTS
9.1 Introduction: Background and Driving Forces.............................................................. 227
9.2 Technology Advances behind Novel e-Health and m-Health Services...................... 229
9.2.1 The Role of Big Data and Cloud Computing...................................................... 229
9.2.2 The Role of Internet of Things in Healthcare..................................................... 230
9.2.3 The Role of Blockchain in Health Information Sharing.................................... 231
9.3 Novel e-Health and m-Health Services........................................................................... 233
9.3.1 The Trajectory of a Disease: From Risk Assessment to Early Intervention
and Patient Empowerment.................................................................................... 233
9.3.1.1 Risk Assessment Phase........................................................................... 233
9.3.1.2 Early Intervention....................................................................................234
9.3.1.3 Patient Empowerment.............................................................................234
9.3.2 Trends in m-Health Applications and Services.................................................. 235
9.3.2.1 Consumer Mobile Applications for Personal Health Monitoring.... 236
9.3.2.2 Consumer Mobile Applications for Telemonitoring........................... 237
9.3.2.3 Certified Mobile Medical Apps.............................................................. 238
9.3.2.4 Best Practices for Building an m-Health App...................................... 238
9.4 Conclusion........................................................................................................................... 240
References...................................................................................................................................... 240
227
228 Health Monitoring Systems
With increasing life expectancy and changing lifestyles, the disease patterns are also
changing; for instance, chronic diseases become much more common. Almost 86% of
US healthcare spending in 2010 was for patients with at least one chronic condition [1];
whereas in Europe, recent figures raise it to 70%–80% [2]. These numbers signify a great
need to reduce costs, for example, by seeking for solutions that reduce the number of hos-
pital visits, that comprise more cost-efficient care alternatives, and that reduce the number
of acute hospitalizations due to complications.
Apart from the cost, the quality of healthcare services is an equally crucial factor. The
accumulated experience in large and complex systems such as that of healthcare delivery
indicates that in many cases the interests of individuals may be secondary to inherent
issues of the system (e.g., administrative issues) or to the system inertia (e.g., inadequate
services that have always been delivered that way). One of the ways to overcome this issue
is by organizing healthcare services around the needs of patients rather than the needs of
the healthcare delivery system.
In the context described above, e-Health can play an important role in rendering the
healthcare delivery system more citizen-centered; it can improve the access to clinical
and specialized services, bringing these services to citizens rather than moving people
to them. Additionally, it can shift part of the responsibility of care to the citizens’ hands,
particularly for wellness and disease prevention.
In line with the above-described socio-economic context, there are various different
entry points for the e-Health business domain, giving emphasis on either cost containment
or health promotion or both. A typical one when focusing on the cost aspect is the provi-
sion of health services by decentralized service providers. The information technology
(IT) systems used in such services support a tiered multilayer architecture (aligned with
the primary, secondary and tertiary care notions). While supporting timely access to the
specialists’ medical advice, such systems enhance rural healthcare delivery by integrating
low cost, scalable technological components into existing infrastructures, while favoring
the continuity of healthcare provision by supporting the integration of patients’ medical
data to globally and instantly accessed Electronic Health Records.
Offering citizen/patient-centered health services is another entry point which supports
remote health status monitoring of those with limited access, notably people living in rural
regions, citizen empowerment to manage their own health status and conduct indepen-
dent living, personalized care and treatment plans, preventative lifestyle, and continuity
of care (from prevention to rehabilitation).
Other attractive use cases include home-based or mobile patient monitoring with ICT
systems that offer various functions. Such systems may:
Finally, another significant group of e-Health services relates to personal health management
and wellness services that focus on preventive healthcare. Typical services of this family
include optimal nutrition and weight management, physical activity and exercise monitor-
ing, stress management, personalized medical care, and pregnancy and maternity calendar.
In line with the above, in the subsequent sections, we present the most promising
e-Health and m-Health applications (apps) and services, after first having presented an
overview of the most significant advances in technology that favors the setup and delivery
of those services.
FIGURE 9.1
Framework for big data analytics in healthcare.
cost-effective manner. Cloud computing not only reduces costs, but also makes a wide
array of applications available to companies and researchers as it closes the gap between
generation, management and processing of data. Here is an example: patient genomic data
analysis allows one-step association of biomarkers with therapies and enables the detec-
tion of new actionable biomarkers, or clinical trials compatible with patients, thereof sav-
ing time and money and increasing treatment success [5]. Classic research laboratories
do not possess sufficient storage and computational resources for processing omics data.
Laboratory-hosted servers require investments in informatics support for configuring and
using software. Such servers are not only expensive to setup and maintain, but do not
meet the dynamic requirements of different workflows for processing omics data, leading
to either extravagant or sub-optimal servers [6].
The above trend for novel cloud computing-based big data analytics services is favored
by several initiatives that support the public availability of data through open datasets.
Indicatively, we refer to the European Union (EU) Open Data portal [7] or the European
Data portal [8]. This is a major step forward towards enabling the development of data-
driven innovative personalized health solutions.
environment closer to the service receiver and enables a more efficient management of the
financial cost of those services.
The application of IoT in the healthcare domain is boundless. Indicative applications are
the following:
• Smart indoor infrastructures enable citizens with special health profiles (e.g., elderly,
chronic patients) to live in their familiar environment in an assisted living man-
ner, prolonging their independence and enhancing their quality of life. Wearable
or implantable sensors with wireless transmitters ranging from blood pressure and
heart rate monitors to advanced devices capable of monitoring specialized implants,
such as pacemakers, electronic wristbands, or advanced hearing aids, facilitate the
monitoring of crucial vital signs and favor ubiquitous patient monitoring.
• A broad range of IoT applications can be found also within the hospital environ-
ment. For example, IoT is a promising technology for medical asset tracking and
management. In combination with real time geolocation systems it facilitates
the management of medical devices, supplies and biological material therefore
conducting on preventing medical errors and improving patient care and safety
inside hospitals. IoT is also a valuable enabling technology for rendering hospital
beds into “speaking beds” that detect and communicate the presence of a patient
or her attempt to get up. Such beds can also adapt to ensure appropriate pressure
and support applied to the patient without the manual intervention of nurses.
• Several use cases are based on the convergence of IoT with robotics. Among the
many robots that are already in use today there are well-known examples of medi-
cal robots in surgery (precision surgery or tele-surgery), robots that are used for
rehabilitation, robots which carry out tasks such as medication delivery, food
delivery, and delivery of supplies in general. All these robots comprise internet-
connected devices that are able to collect, process, and exchange data.
FIGURE 9.2
Leveraging IoT platforms and connected devices in e-Health and m-Health applications.
with data protection regulation. Particularly for the latter, an extensive discussion over
the impact of the EU General Data Protection Regulation (GDPR) [10] on the adoption of
blockchain technologies in healthcare applications is ongoing.
GDPR governs the use and protection of personal data collected from or about any EU
citizen. In GDPR, personal data are defined very broadly and include any information
relating to an identified or identifiable natural person. Under current EU legal interpreta-
tions, this includes encrypted or hashed personal data as well as public cryptographic
keys that can be tied to an individual. One of the foundational aspects of GDPR is the fact
that citizens have more control over their personal information and are given the right to
erase, that is, the right to have all their personal data permanently deleted. This comes in
contrast with blockchain’s structural element of immutability. In other words, since data
that is stored on the blockchain, including personal data, can’t be deleted, there is no way
to exercise the right to erasure that people are granted under GDPR [11].
Blockchain is not designed to be GDPR-compatible. Or rather, GDPR is not blockchain-
compatible the way it is written today [11]. Several ideas are proposed, for example,
rendering the solution of making personal data permanently inaccessible equivalent to the
right to erasure; or moving sensitive personal data “off chain.” The latter would mean that
personal data are moved outside of the blockchain and only a reference to the data and a
hash of the data are stored on the blockchain. This would allow the removal (erasure) of
the personal data without breaking the blockchain. However, this approach defeats many
of the benefits of DLT such as security and resilience through redundancy [5].
Anyway, e-Health includes several application domains other than the Electronic
Health Records (EHRs) and similar applications where blockchain technology could play
a significant role. An indicative such domain is the one that intersects healthcare with
logistics: provenance-based blockchains can track the manufacturing and distribution of
medication across the supply chain, ensuring medications are properly stored and han-
dled, and that counterfeit drugs do not enter the market [12].
Novel e-Health and m-Health Services 233
9.3.1.2 Early Intervention
Early intervention in the disease onset and progression process to prevent serious effects
is a critical aspect of healthcare service delivery. Additionally, as the trajectory of chronic
diseases is often cyclical, such an approach offers multiple interception opportunities to
prevent serious decline. The role of the citizen in this model of disease prevention and
management is pivotal. In fact, several clinical situations can be prevented or better moni-
tored and managed with active participation of the patient. This in turn highlights the
importance of delivering e-Health and m-Health solutions that raise individual’s aware-
ness and empower the patient to participate in the management of his or her health.
Towards this direction the use of ICT tools and e-Health services is of paramount impor-
tance. Such solutions are typically based on a multi-layered architecture including com-
ponents for data collection, analysis, visualization, and action. The data collection layer is
based on a sensors’ data fusion approach towards the collection of physiological, physical,
cognitive, environmental, and social data related to the monitored individual. Measuring
physiological and activity-based parameters remotely and continuously via wearable or
ambient sensors has the potential to revolutionize our ability to predict and pre-empt
harmful changes in a disease trajectory.
The collected data are fed to a decision support engine that analyses it and produces
new knowledge. An important aspect of this part of the chain is the development of meth-
ods for real-time identification of behavioral and physiological patterns since early detec-
tion and communication of alerts to both patients and providers can prompt help-seeking
behavior and deployment of just-in-time interventions that may prevent relapse episodes
and effectively alter the disease progression [15].
The above architecture pertains to a closed loop built around the citizen. Different forms
of this loop exist, mostly depending on the action step: some interventions render the
knowledge extraction into recommendations that may be prompted either to the moni-
tored individuals or their health caregivers; others use this knowledge to support highly
personalized tools for the individuals.
9.3.1.3 Patient Empowerment
Patient empowerment interventions aim at involving patients to a greater extent in their own
healthcare and disease management cycle. A concept often applied in patient empowerment
is that of ICT-enabled self-management pathways. This comprises a cyclical process pro-
posed by Lorig et al. [16] that includes medical consultations followed by self-management
goal setting, then self-management actions, then feedback collection, and finally evaluation
and readjustment of the self-management process. Self-management can be further decom-
posed into information collection, decision making, and action-taking steps (Figure 9.3).
In line with this concept, Lamprinos et al. [17] propose a modular ICT framework for
the establishment of a personalized yet evidence-based patient guidance strategy, leverag-
ing various sources of personal health data and clinical knowledge. This approach was
applied in diabetes self-management, using an ICT-based self-management framework, as
a means to seek lifestyle and behavioral changes that facilitate disease management. This
framework is composed of four phases:
FIGURE 9.3
Framework for self-management applications.
b. the phase of goals’ definition or modification, where the patient breaks down the
physician’s recommendations into short-term self-management goals, stored in
her/his individual Personal Health Record (PHR);
c. the phase of actions’ specification, where the patient breaks down the defined
goals into small, achievable portions; and
d. the phase of observations of daily living (ODLs) recording, where the patient
collects and records ODLs in alignment with the planned activities using a web or
mobile application (Figure 9.4).
The loop closes with an evaluation and feedback step where the patient is given an indi-
cation on how realistic the planning was as well as hints for planning adaptation and
rationalization.
FIGURE 9.4
Mobile applications for collection of ODLs by diabetic patients.
Departing from the mobile devices’ evolution, m-Health nowadays encompasses a much
broader concept, capitalizing the tremendous evolution in key enabling technologies. This
evolution is based for example on the increased accuracy of sensors and measurement meth-
ods, on the injection of internet connectivity to miniature-measuring devices and sensors,
on the ability to interface various different devices and fuse data from different sources,
and to the increased user friendliness due to wearing comfort and invisibility of the devices
(being either wearables, such as smart watches and textiles, or implantable or ingestible).
The above evolution at the technical foundations’ plane enabled to flourish a market-
place with numerous applications and services, related to personal health monitoring and
healthcare delivery via certified medical apps. In the subsequent sections we analyze these
two dimensions.
Overall, one of the major drivers of the increasing impact that consumer mobile applica-
tions for telemonitoring can have is that of the cost-saving potential they create. Taking as
an example diabetes mellitus, the direct costs per patient in countries like Germany, not
including the cost of long-term care (nursing insurance), are between EUR 4,000 and EUR
5,000 per year [24]. In the same research, the indirect costs, which include loss of produc-
tivity and early retirement, are calculated to more than EUR 5,000 per patient per year. In
the US, the total cost of diagnosed diabetes is expected to rise to $336 billion by 2034 [25].
FIGURE 9.5
Rendering an m-Health app attractive to potential users.
of gamification for patients using health applications, there is a growing interest in going
beyond simply improving usability of an application, by motivating the user to incorpo-
rate the application into her everyday routine and reap the intrinsic benefits of using it.
Gamification seeks to improve user engagement, help solve problems, learn, and help com-
plete tasks. From the health perspective, gamification techniques might help the patient man-
age their general health, their chronic disease(s) and perhaps improve their health condition.
Integration with social networks provides patients with significant benefits. Patients
with similar disease can interact with each other and share experiences. The idea is that the
mobile app serves as a bridge to social networks to communicate news and relevant infor-
mation related to the monitored disease. However, the use of social networking involves
privacy and security implications and may have controversial impact on the subjects. So,
while the feature of bridging disease management mobile app with social networks is an
appealing feature for many users, prior consent of them should be mandatory, as well as
the option to disconnect the link between the apps whenever desired.
Personalization is a very important feature when it comes to digital artifacts that sup-
port our life nowadays. The one-size-fits-all model is a guarantee for failure. Chronic
patients who decide to use a mobile app to support their disease management are actu-
ally users with very diverse characteristics (digital and health literacy, usage preferences,
experiences, and competences in disease self-management). A dynamic combination of
offered functionalities as well as a variation in the timing and ordering of how they are
applied (i.e. user driven customization) is a key factor for success. Personalization is a key
dimension also when considering access to health-related information material. Holtz and
Lauckner [28] indicate that although Internet health information is growing rapidly, the
240 Health Monitoring Systems
average person lacks the skills for finding and using the health information strategically
for his/her benefit. This indicates the need for personalized information, leveraging user’s
profile, desires, and actual health status.
Finally, the design of the presentation layer of the applications is a key success factor. Older
people comprise a segment of population who can benefit only from accessible interfaces.
They have some overlapping needs with disabled people in general, due to age-related
impairments affecting vision, hearing, physical, and cognitive abilities. Therefore, user inter-
faces that are accessible to disabled users are also more accessible to older users. However,
an effective interface for the older people is achieved not only by applying the generic web
accessibility guidelines, but also by implementing accessibility aids specifically addressed to
their special needs. When designing user interfaces for older people, usability is a major con-
cern. Apart from making them accessible and multimodal, for the certain benefit of enabling
access to a broader public, perhaps more important than that is simply make them usable.
Achieving usability needs some special design considerations when the target audience are
older people. The decrease in cognitive capabilities, especially the short-term memory, is the
biggest obstacle for using an interface that requires exploratory learning and building con-
ceptual models. While other age-related impairments (vision, hearing, and physical ability)
can be addressed by accessibility or multimodality features, the difficulties derived from
intellectual decline can only be overcome with an adaptive design of the user interface.
9.4 Conclusion
In this chapter, we presented a compact yet inclusive set of novel e-Health and m-Health
apps and services. We briefly showed that the evolution is healthcare delivery is propelled
by the enormous advances in key enabling technologies and infrastructure, such as big data
and cloud computing, IoT, and blockchain. This evolution will contribute to the efficient
management of health and wellbeing, while empowering the participation of citizens, and
facilitate the transformation of health and care services to more digitized, person-centered
and community-based care models, thereby enabling better access to healthcare and the
sustainability of health and care systems.
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10
Activity Monitoring of Elderly Patients
Dwaipayan Biswas
IMEC
CONTENTS
10.1 Introduction......................................................................................................................... 243
10.2 Activity and Sensing.......................................................................................................... 244
10.2.1 Activities to Be Monitored..................................................................................... 244
10.2.2 Sensing Techniques................................................................................................ 244
10.2.3 Challenges of HAR................................................................................................. 247
10.3 HAR Processing Overview............................................................................................... 248
10.3.1 Data Acquisition and Pre-Processing.................................................................. 249
10.3.2 Segmentation........................................................................................................... 249
10.3.3 Feature Engineering and Classification.............................................................. 249
10.3.4 Deep Learning......................................................................................................... 251
10.3.5 Performance Evaluation......................................................................................... 253
10.4 Case Study...........................................................................................................................254
10.4.1 Experimental Protocol and Data Acquisition.....................................................254
10.4.2 Arm Movement Classification.............................................................................. 256
10.4.2.1 Feature Engineering and Clustering..................................................... 256
10.4.2.2 Deep Learning – CNN............................................................................ 258
10.5 Conclusion........................................................................................................................... 259
References...................................................................................................................................... 259
10.1 Introduction
Human activity detection and classification is a key component for remote health monitoring
applications. Remote health monitoring systems aided by technological advancements
have enabled continuous monitoring of inhabitants within the home environment, in
effect cutting down hospitalization and care expenses [1]. The developments in microelec-
tromechanical systems (MEMS) technology along with wireless sensor networks (WSN)
and Internet of Things (IoT) have proliferated the miniaturization of wearable sensors
capable of pervasive long-term monitoring [2]. Wearable sensors have become popular
means of providing accurate and reliable information on people’s activities and behaviors.
They also measure physiological parameters (e.g., heart rate, blood pressure, etc.) which
are combined with mobility information to infer the state of critical patients during home
rehabilitation [3].
Human movement typically entails a variety of periodic activities including w alking,
running, stair ascent, and stair descent. Monitoring of movements performed in an
243
244 Health Monitoring Systems
ambulant environment, termed as ‘activities of daily living (ADL)’, for example, brushing,
drinking, cooking, bathing, etc., has also gained prominence [4,5]. ADL monitoring helps
determine the activity levels of elderly inhabitants and ascertain motor activities especially
for subjects with physical impairments. Typical modalities for activity monitoring can be
categorized into – vision [6] and wearable sensor-based monitoring [7,8]. Although more
accurate, camera-based sensing systems are intrusive and involve the processing/storage
of large amounts of data [9]. In comparison with the former, time-series data generated by
low-cost wearable sensors provide an opportunity for reliable and pervasive monitoring.
Smart homes that provide intelligent systems for assistance in the subject’s living environ-
ment, also referred as ambient assisted living (AAL), are developed by combining wear-
able sensors with ambient sensors – such as motion detectors on doors, radio-frequency
identification tags on objects of daily use, sensitized cutlery and acoustic sensors to moni-
tor activities pertaining to food intake, pressure sensors proximal to the bed to monitor
sleep durations, etc. [10–12]. Information collected by wearable sensors is augmented by
the ambient sensors and distributed throughout the home to provide holistic feedback of
the activities performed and living behavior of the subject aimed toward improved health
management [13].
10.2.1 Activities to Be Monitored
It is imperative to comprehend the granularity inherent in human behavior; correspond-
ingly, movements can be categorized as (but not limited to) – (a) gestures, (b) actions, or
(c) interactions. Gestures represent elementary components comprising a holistic move-
ment, for example, raising an arm, moving a finger, or nodding. Actions, on the other
hand, comprise multiple gestures leading to a meaningful activity, for example, drinking a
glass of water (involves grabbing/holding an object, raising the arm, and using the mouth)
[8,21]. Finally, human–object/human–human interactions take place to complete a dedi-
cated set of tasks, for example, making coffee or preparing breakfast [22,23].
10.2.2 Sensing Techniques
Vision-based activity recognition uses video sequences or digitized visual data to moni-
tor a subject’s movement in a designated area [9,24]. Computer vision techniques are used
to analyze visual observations. Popular camera-enabled gaming consoles and Microsoft
Activity Monitoring of Elderly Patients 245
Kinect systems have been used in the field of rehabilitation. However, their surveillance
is intrusive on user privacy and restricted within a specific zone [25], and the processing
of the data involves complex image processing algorithms [26]. Moreover, vision sensors
are affected by the variability of light leading to a decrease in performance due to visual
obstructions [27]. Monitoring ADL in ambulant environment has necessitated a shift in par-
adigm toward mobile and wearable sensor-based monitoring. They provide a comparative
advantage over vision sensors in terms of unobtrusiveness, low cost, and ease of deploy-
ment, enabling pervasive monitoring [23,28,29]. Moreover, mobile phones have become part
of daily life and hence can double up for monitoring applications. Alternatively, the use of
social networks [30] that exploit appropriate users’ information from multiple sources to
understand user behavior and interest has also been proposed recently. An illustration of
a mobile phone attached to the user’s arm is shown in Figure 10.1.
Sensors, the key elements of wearable and mobile monitoring systems, are used to mea-
sure the physiological parameters of interest accurately and reliably over long durations.
Sensors used for HAR generate time-series data reflecting physiological parameters or
state changes which are further statistically analyzed for recognizing/inferring the under-
lying activity. Inertial measurement units (IMUs) comprising accelerometers and gyro-
scopes are the most commonly used sensors for HAR [32]. IMUs are generally augmented
by magnetometers to derive orientation information with respect to earth’s magnetic field.
However, magnetometers are susceptible to electromagnetic interference from ferromag-
netic materials, which could be present in ambulant home environment [33]. IMUs help
measure acceleration, distance, rate of rotation, time, etc. which act as health indicators for
gait, posture, tremor, and spasticity, aiding clinical assessment [34]. In addition to IMUs,
smartphones provide access to a wide range of built-in sensors such as Bluetooth, Wi-Fi,
microphones, pedometer [36], and proximity and light sensors [35]. These sensors can be
exploited to infer coarse-grained and context-aware activity recognition, user location,
and social interaction between users. For example, the pedometer in Samsung Galaxy
smartphones allows step counts and, correspondingly, heart rate monitoring [37–39].
Other sensors such as barometers, thermometers, air humidity, etc. have also been applied
in assisted living and wellness monitoring of elderly citizens [36].
FIGURE 10.1
Smartphone attached to the elbow joint for detecting activities [31].
246 Health Monitoring Systems
We take an in-depth look at a MEMS accelerometer and gyroscope given their popular-
ity in HAR. In general, MEMS-based transducers generate resistive/capacitive variations
when excited by external forces, for example, compression, pressure, acceleration, etc. The
change in geometry of the piezoresistive material or the change in distance between two
capacitor plates as an effect of external force is transduced to an electrical signal obtained
at the front end of the sensor device. An accelerometer is the most popularly used sensor
in HAR, which measures force rather than acceleration. This is achieved by measuring
the displacement of an internal proof mass and using Newton’s second law of motion
(F = ma). It is not necessary to know the value of the proof mass since this is accounted
for during the calibration process (and it is assumed that it remains constant); and thus
a calibrated MEMS accelerometer produces an output that is directly proportional to
the acceleration experienced by it. Since an accelerometer responds to force, it is always
subjected to gravity which implies that gravitational acceleration (g) can be used as the
reference value during the calibration of accelerometers. During the absence of external
forces acting on the accelerometer (e.g., when stationary), analyzing the recorded value of
gravitational acceleration as experienced by the accelerometer helps determine orientation
information for postural tracking [40]. Calibrating the accelerometer with the use of g as a
reference standard is a very simple process. The accelerometer is placed on a flat surface
with its sensing axis either aligned, opposed, or orthogonal to the direction of g, causing
it to experience accelerations equal to +1 g, −1 g, or 0 g, respectively. Hence, a simple three-
point calibration procedure implemented without additional or specialist equipment is
adequate if the accelerometer exhibits a linear response to acceleration. However, the three
calibration points that define the upper, lower, and midrange values of a measurand can be
deceptive since both a linear or sine function can be fitted to them with high correlation.
It is therefore highly recommended that, where practical, calibrations are performed with
more than three reference values [40].
A MEMS gyroscope is used to measure the rate of rotation (°/s) without a fixed point
of reference. It works on the principle of a tuning fork and comprises a pair of identical
masses (m) that are driven to oscillate with equal amplitude but in opposite directions,
as shown in Figure 10.2. The positive X, Y, and Z directions are shown, and the negative
direction for each axis represents the opposite direction. If one of the masses moves in
FIGURE 10.2
Principle of operation for MEMS vibrating gyroscope [32].
Activity Monitoring of Elderly Patients 247
the positive X-axis direction with velocity Vx and an angular rotation Ωz applied about the
Z-axis, then the mass will experience an ‘FCoriolis’ force in the direction of the arrow due to
the Coriolis effect. Correspondingly, the other mass moving in the negative X-axis direc-
tion with the same velocity (Vx) will also experience a Coriolis force of the same magnitude
but in the opposite direction [32]. These two forces can be measured by sensing mecha-
nisms built into the MEMS structure (e.g., strain change measured with piezoresistor or
deflection measured by change in capacitance). Since the gyroscope measures the angular
rate of rotation, it requires a source of rotation to excite the device. In relevant literature,
turntables (rotating at predefined speeds) are generally used for calibrating a gyroscope
[41]. Each axis of the gyroscope can be calibrated by rotating the sensor through 360° about
that axis, integrating the total response obtained over the time taken (angle × rate) and
dividing the result by 360 (rate). Rotations can be performed both clockwise and coun-
terclockwise, and corresponding measurements are also averaged when the gyroscope is
stationary. The calibration coefficients thus determined are used to calibrate the raw sen-
sor data before any further processing.
10.2.3 Challenges of HAR
The challenges related to HAR – experimental setup for data collection, choice of sensor,
sensor placement, and data analysis techniques depending on the application scenario –
have been discussed in detail in this section [20]. Sensor data representing physical activities
demonstrate a wide range of variability across individuals. Variability is inherent within
the same activity morphology performed by different individuals. Furthermore, variability
could also be exhibited when the same individual repeats the same activity over time due
to fatigue and environmental factors. Hence, an activity recognition system needs to be
robust enough to incorporate these variabilities. There can be two approaches to building a
successful HAR model. First, a generalized/subject-independent methodology built using
data from more than one subject; however, it could be susceptible to inter-subject variabil-
ity. This can be mitigated by using a large number of data samples pertaining to each sub-
ject, formulating a second subject-dependent/personalized model approach. This model is
based on the data from each individual subject. A personalized model necessitates collect-
ing a large dataset from an individual, incorporating maximal variability [42]. Generally,
for health monitoring applications, formulating a personalized model is beneficial, demon-
strating the difference in levels of impairment depending on the stage of rehabilitation [43].
Data segmentation is a further challenge in HAR systems for real-time monitoring appli-
cations based on continuously streaming data; this data needs to be segmented depending
on the relevance of activities to be monitored. Data pertaining to activities out of inter-
est are referred as null class and difficult to model since it is representative of a wide
range of activities. It can be identified if the corresponding signal morphology is uncor-
related/different from the activity being monitored. Class imbalance is another problem
especially for data collected over long durations where the monitored activities have dis-
similar occurrences, which can be mitigated by generating augmented data or adopting
techniques such as oversampling/interpolation to balance out the different class (activi-
ties) representations. Activities performed in ambulant environment also suffer from the
unavailability of ground truth annotation required for verifying the activity recognition/
detection systems [29]. This is particularly relevant to wearable sensor data as opposed to
vision data.
Activities are generally performed in the following situations: (a) controlled
environment – for example, in the laboratory, where the annotations of the activities with
248 Health Monitoring Systems
minimal variability are known, (b) semi-naturalistic environment – the subject performs
activities as they would normally do in daily life while another person annotates the activ-
ities through visual inspection, and lastly, (c) naturalistic environment – the subject per-
forms activities voluntarily, and no annotations for actions performed are available [20].
Corresponding to the activities, designing an experimental protocol for data collection
presents another challenge in HAR. Although there are publicly available datasets for a
varied range of activities [44–49], for example, walking, running, different gestures, etc., it
is important to acquire custom data for specific-use cases.
Sensor characteristics present a significant challenge for long-term monitoring, for
example, hardware failures, sensor drifts, and errors in the software aimed at stream-
ing or capturing the data, leading to erroneous situations. External factors such as tem-
perature, pressure, and change in position due to loose straps (used to attach sensors) can
require frequent calibration, thereby affecting recorded data. Energy efficiency is one of
the last notable challenges. Remote monitoring systems often transmit the data acquired
from wearable sensors to a remote station (server/back-office service platform) wirelessly,
where the signals are analyzed. Besides incurring energy loss, continuous data transmis-
sion along with key processing steps – such as analog to digital conversion, quantization,
filtering, and microcontroller operation – would affect the longevity of battery-operated
sensors. Hence, for long-term continuous monitoring using wireless body area networks
comprising heterogeneous sensors, it is important to select low-complexity data analysis
techniques that can be carried out on the sensor node itself, thereby negating continuous
data transmission. Therefore, it is imperative to perform low-power, on-node processing
for applications requiring real-time operation [50] while for applications supporting long-
term behavioral analysis offline data processing may be sufficient [51].
Performance
Classification
Evaluation Automatic feature extraction
and Deep Learning
FIGURE 10.3
Overview of data processing for HAR.
Activity Monitoring of Elderly Patients 249
10.3.2 Segmentation
The pre-processed signal is segmented or windowed to contain information on the activ-
ity of interest. It is challenging to determine the boundaries indicating the start and stop
time of the activities to be monitored during ADL. Sliding windows are popularly used,
where a fixed window size representing a time duration is used to extract segments of
a signal [53]. A trade-off is required on the window size since a smaller window causes
missing out on relevant activity while a longer window could cause an overlap between
successive activities influencing classification. Hence, dynamic windows based on a data-
driven or probabilistic approach could lead to optimal solutions, however, incur high com-
putational complexity [8].
space for successful classification. The extracted features are typically pre-processed to
remove outliers and normalized (e.g., zero mean and unit variance also known as stan-
dardization). An outlier, as the name suggests, refers to a point/sample in the feature vec-
tor that lies away from the mean. For normally distributed data, three standard deviations
from the mean is usually considered as the threshold. Normalization of the features is
further essential to avoid the effect of feature values lying in different numeric ranges on
the cost function of the classifier. The normalized features are ranked, where a higher-
ranked feature has more discriminatory ability between competing classes as compared to
a lower-ranked feature in the respective feature space [20]. Some common techniques like
Fisher discriminant ratio and Bhattacharya distance are used to quantify the discrimina-
tory ability of features between two equi-probable classes [55]. Similarly, scatter matrices
[56], ReliefF algorithm, and clamping technique are some of the popular alternatives for
multiple-class problems. Principal component analysis [57] or Karhunen–Loeve transform
is also a popular dimensionality reduction technique used in HAR. Principal compo-
nent analysis can be used directly on the raw data or extracted features to transform the
data into a small number of uncorrelated variables referred as principal components. The
extraction, ranking, and selection of features, commonly referred as feature engineering, is
entirely dependent on the type of activities, number of sensors, and application scenarios.
The classification phase helps map extracted/selected features into sets of activities
(classes) using machine learning or pattern recognition methods [29]. A wide range of
classifiers are used; the primary selection factor being accuracy, complexity, and the fea-
sibility of real-time execution [19,23]. There can be two broad approaches undertaken for
the classification task: (a) supervised learning – the training dataset comprises extracted/
selected feature vectors with prior information on the class label associated with them; (b)
unsupervised learning – the number of classes could either be known or unknown, and
a class label is assigned to each training set. Activities performed in a controlled or semi-
naturalistic environment with annotations available for each activity are best suited for
supervised learning techniques [20]. Whereas, activities performed in a completely natu-
ralistic/ambulant environment with no annotations available are best suited for unsuper-
vised training methodology. There could or could not be information available regarding
the type/number of activities performed. In such scenarios, clustering-based unsuper-
vised learning has been effectively used to determine the classes in HAR [58].
The classification mechanisms can be broadly categorized as:
c) Template based – exploits the similarity between the test dataset and pre-computed
class (activity) templates. Dynamic time warping is a popularly used method for
assessing the similarity between two time series with varying window sizes [60].
The classification process is commonly involved with cross-validation during the estima-
tion of model parameters (or training) to judge its robustness. The available data is split
into training and testing datasets (e.g., in a ratio of 80:20 or 70:30). The training dataset is
used to train the classifier or formulate the model. The model parameters thus estimated
are used to validate the effectiveness of the model on unseen test dataset. Cross-validation
or resampling is used to split the training dataset into k partitions/folds, where k − 1 folds
are used to formulate the model or train the classifier, and the remaining one fold is used to
test the model. Hence, all segments are iteratively trained and tested upon, thereby formu-
lating a robust model. The k-fold partitioning could be further extended to leave one data
sample out of testing, and the rest of the data for training, resulting in an exhaustive ‘leave-
one-sample-out’ cross-validation technique. The error computed over each iteration is aver-
aged to estimate the overall error. The cross-validation technique (partitioning mechanism)
adopted depends on the size of the available dataset. A large partition helps achieving near
accurate estimation of the classifier’s performance, however, is limited by a longer computa-
tion time; whereas smaller number of folds is a sacrifice on the performance of the classifier.
Common practices involve using ten partitions of the data which are iterated over ten times,
yielding ten runs of tenfold cross-validation to estimate a robust trained model [20,55].
10.3.4 Deep Learning
The translation invariance of human activity data involves different people perform-
ing the same kind of activity in a different manner or even the same person perform-
ing the same activity in different ways over multiple repetitions that causes a fragment
of activity to manifest at different points in time [61]. The use of hand-crafted features
has a high impact on the highly temporally correlated data structure; hence, a method
that addresses this strong bias in feature engineering is needed, especially on the back-
drop of high volume of data generated by smart wearable platforms (e.g., mobile phones).
Expert-driven, hand-engineered features, although representative of the activity and the
application scenario, are limited in their ability to faithfully represent the salient charac-
teristics of complex activities and involve time-consuming feature selection procedures.
In effect, the whole process of feature engineering involves extensive heuristic knowledge
to extract and select appropriate features for HAR. Deep learning, alternatively known as
deep neural network, is a new branch in machine learning that allows automatic feature
representation from raw data, capturing local dependencies and scale invariance. With
the current influx of unlabeled sensor data from IoT, crowdsourcing, and cyber-physical
systems, implementing an HAR system with manual feature extraction and classification
could be extremely challenging and incur a high design time and effort [19].
Deep learning methods are particularly useful where a large amount of data needs to
be processed and can be broadly classified into restricted Boltzmann machine [62], auto-
encoder [63], convolutional neural network (CNN), and recurrent neural networks (RNN)
[64], as shown in Figure 10.4. Nweke et al. provide a detailed description of these methods
[19]. The taxonomy of deep learning popularly used for HAR includes CNN and RNN.
CNNs are characterized by an initial layer of convolutional filters (a set of weights which
slides over the input), followed by non-linearity (activation function – rectified linear units),
sub-sampling (pooling), and a fully connected layer which realizes the classification.
252 Health Monitoring Systems
FIGURE 10.4
Different algorithms of deep neural networks [19].
The stacking of multiple convolutional layers helps achieve automatic feature extraction,
where downstream layers capture more complex or differentiating features [65,66]. This
integrates information from different filters and levels of abstraction. RNNs are an effec-
tive choice to analyze time-series data for inferring sequential/time-variant information
since they incorporate contextual information from past inputs and are robust to localized
distortions in the input sequence along time. A bottleneck for deep CNN structures (with
large network size) is the vanishing gradient problem, wherein information from previous
computations is rapidly attenuated with progression through the data flow. RNNs applied
to long sequential data suffer similarly since every time step has the same weight, and
consequently, the contribution of an input in the hidden state is subjected to exponential
decay. Long short-term memory (LSTM), a variant of RNN, uses a memory block inspired
by a computer memory cell, where context-dependent input, output, and forget gates con-
trol what is written, read, and kept in the cell in each time step [67]. Hence, it becomes con-
venient for the network to store a given input over many time steps, helping LSTM layers
capture temporal correlations, a key feature of inertial-sensor-data-representing activities.
Hence, CNN and LSTM present a data-driven approach to learning discriminative fea-
tures from raw sensor data to infer complex, sequential, and contextual information in a
hierarchical manner.
Deep neural network performance is highly influenced by the hyper-parameters
selected for modeling, which involve architectural parameters and training parameters.
Architectural parameters include the number of layers stacked together (e.g., convolution
or LSTM layers), number of filters/kernels in each layer, size of each filter, the stride rate
(during convolution of the input data and the filter), and type of pooling used (e.g., max
pooling, average pooling, etc.), all of which are key toward model formulation. The train-
ing parameters include the learning rate, type of backpropagation algorithm, activation
function, loss function, dropout, and number of epochs. It is cumbersome to manually iter-
ate through all these parameters to determine the optimal selection; hence, a grid search
cross-validation technique [68] is generally used that works through multiple combina-
tions of parameters and helps arrive at the optimal selection.
Recent studies have indicated superior results of deep learning over conventional hand-
crafted features for HAR [69,70]. CNN outperformed other state-of-the-art data mining
techniques in HAR for the benchmark dataset collected from 30 volunteer subjects [71],
achieving an overall performance of 94.79% on the test set with raw sensor data and 95.75%
Activity Monitoring of Elderly Patients 253
FIGURE 10.5
An illustrative confusion matrix with three classes.
with additional information on temporal FFT of the HAR data set. Lastly, LSTM was suc-
cessful used to mode the sequence of time-correlated samples of activity pertaining to
inertial sensor data of ADL tasks [69,72].
10.3.5 Performance Evaluation
The performance of an HAR system can be evaluated in terms of correct classification
through true positive (TP) and true negative (TN); and false detection of activities that did
not occur, that is, false negative (FN) and false positive (FP). These measures are used to
estimate a few well-known matrices, which are explained as follows:
Further, normalized confusion matrices could be used for unbalanced datasets having a
significant amount of difference in the number of ground truth annotations of the activ-
ity classes [73]. An illustration of a confusion matrix for multiple classes is shown in
Figure 10.5. Here, only the accuracy might not be a true evaluation of the classifier due
to possible dissimilar classification rates of different classes affecting the overall perfor-
mance measure. The sensitivity Si of the i-th class is a measure of the number of correct
predictions with respect to the total number of observations in that class [74], as shown in
Equation 10.1:
N ii
Si = × 100 (10.1)
fi
254 Health Monitoring Systems
fi = ∑C
j =1
ij (10.2)
where i = 1…c, and c is the total number of classes. The diagonal and off-diagonal elements
of the confusion matrix represent correctly classified and misclassified patterns, respec-
tively. Cij represents the number of times the patterns are predicted to be in class j when
they really belong to class i.
Figure 10.5 demonstrates a near-accurate classification since all left-to-right diago-
nal elements approach unity and all off-diagonal elements approach zero. The sensi-
0.9
tivity of class A can be computed as SA = = 90% and the overall accuracy as
(0.9 + 0.9 + 0.98) (0.9 + 1.0)
Accuracy = = 92.67% [20].
3
10.4 Case Study
In this section, we present a short use case scenario on recognizing arm movements
performed by stroke survivors during ADL. Cerebrovascular accident, more popularly
referred to as stroke, ranks second to heart diseases [75,76]. Stroke is a medical emergency
since it causes some degree of physical disability and potentially leads to death. The effect
of stroke is different for each individual based on the degree and region of the brain that
suffers damage and can result in partial paralysis, impaired vision, memory loss, and
speech problems [77,78]. Such aftereffects pose a socio-economic challenge in terms of loss
of lives, disability among the survivors, and expenses incurred toward their rehabilitation.
Physiotherapy is a popular treatment to restore the motor functionality of stroke survivors.
Rehabilitation therapies in clinical settings have also relied on functional electrical stimula-
tion, virtual reality [79], and constraint-induced movement therapy [80,81]. Although these
approaches are successful in achieving motor recovery in patients, their use is restricted
to clinical settings, requiring unnecessary patient transfer to the clinic. Physical activity
has traditionally been monitored by questionnaires, citing their usefulness in covering
large subject groups and cost-effectiveness. These questionnaires can be completed by the
patients themselves on a daily basis at home, thereby helping to formulate a patient activity
log which is monitored periodically by respective clinicians. In addition, therapists perform
a wide range of tests to assess the patient’s motor ability within a clinical environment –
the Wolf motor function test [82], box and block test [83], and the nine-hole peg test [84].
These tests involve a subjective scoring system to quantify the performance of the sub-
jects. However, subjective measures of physical activity may overestimate activity levels
compared to assessments made by objective measures. Hence, sensor-based telemedicine
modalities provide an alternative toward objective measurement for patient monitoring.
A specialized telemedicine application for stroke rehabilitation called ‘Telestroke’ has been
in practice with major success in delivering ‘around-the-clock’ specialist clinical evaluation
of stroke survivors in home settings, thereby also reducing costly human intervention [1].
objective was to detect the occurrence of these specific arm movements in an ambulant
environment using minimal number of sensors. Enumerating occurrences of particular
arm movements (e.g., prescribed exercises) during daily activities over time could be par-
ticularly useful in monitoring rehabilitation progress in neurodegenerative diseases such
as stroke or cerebral palsy. The reported exploration focuses on three elementary arm
movements: (a) Task A – reach and retrieve (extension and flexion of the forearm), (b) Task
B – lift arm (rotation of the forearm about the elbow), and (c) Task C – rotate arm (rotation
of the wrist about the long axis of the forearm). In principle, the three chosen movements
constitute a significant proportion of the complex movements performed with the upper
limb during ADL, besides resembling tasks 8, 9, and 15 of the streamlined Wolf motor
function test. Here, a summarized view has been presented with respect to the c ontextual
details presented in Section 10.3. For a detailed description of the methodology refer to [22].
Data were collected in two settings from four stroke survivors (age range 45–73, both
sexes, both left and right arm dominant) within a treatment center at the Brandenburg
Klinik, Germany. Firstly, in a controlled laboratory environment where, for patients 1 and
4, 80 trials of Task A and 40 trials each of Tasks B and C; and for patients 2 and 3, 40 trials
of Task A and 20 trials each of Tasks B and C are considered. It is interesting to note that
there is a higher number of trials for Task A in comparison with Tasks B and C; this is in
line with the fact that Task A is performed more frequently in daily lives. Secondly, in a
semi-naturalistic environment, that is, in the kitchen proximal to the laboratory. A cus-
tomized activity list (Table 10.1), emulating the process of ‘making tea’, a common ADL,
with repeated occurrences of Tasks A, B, and C, was performed by the subjects [8]. The
activity list in the experimental protocol comprises 20 individual actions including 10
occurrences of Task A, and 5 each of Task B and C. Data was collected for 5 days from
TABLE 10.1
Use Case Activity List – ‘Making Tea’
Activity Sequence Task
1. Fetch cup from desk A
2. Place cup on kitchen surface A
3. Fetch kettle A
4. Pour out extra water from kettle C
5. Put kettle onto charging point A
6. Reach out for the power switch on the wall A
7. Drink a glass of water while waiting for kettle to boil B
8. Reach out to switch off the kettle A
9. Pour hot water from the kettle in to cup C
10. Fetch milk from the shelf A
11. Pour milk into cup D
12. Put the bottle of milk back on shelf A
13. Fetch cup from kitchen surface A
14. Have a sip and taste the drink B
15. Have another sip while walking back to the desk B
16. Unlock drawer C
17. Retrieve biscuits from drawer A
18. Eat a biscuit B
19. Lock drawer C
20. Have a drink B
256 Health Monitoring Systems
each of the four stroke survivors, where two repetitions of ‘making tea’ were performed
by the subjects each day. Therefore, the semi-naturalistic dataset comprises 10 repetitions
(2 repetitions/day), adding up to 200 arm movements (100 Task A, 50 each of Task B and
C). Shimmer 9DoF wireless kinematic sensor module containing mutually orthogonal
tri-axial accelerometer, magnetometer, and gyroscope was used for sensing. We use a tri-
axial accelerometer (range ± 1.5 g) on the impaired arm, proximal to the wrist, chosen in
view of its sensitivity to arm movements compared to other potential positions like the
elbow or sternum [42]. Data is sampled at 50 Hz, deemed sufficient for human upper limb
movement, and streamed along with timestamp to a host computer using the Bluetooth
wireless standard. The data pertaining to each task was segmented in accordance with
annotations from an accompanying researcher during the experiment. The participants
gave their kind consent for the experiments and were encouraged to perform the move-
ments naturally without restricting physical factors such as height/distance/position of
tables/chairs/working surface with respect to the subject’s position and pace of perform-
ing the designated task. This was done to ensure a wide range of variability within the
kinematic data that aided the development of a robust activity classification mechanism.
TABLE 10.2
Sensitivity and Accuracy for Arm Movement Recognition Using Clustering for
Stroke Survivors
Sensitivities (%)
Subjects Features A B C Overall Accuracy (%)
S1 19 80 90 100 88
S2 19 90 20 100 75
S3 21 95 100 20 78
S4 8 10 80 60 40
258 Health Monitoring Systems
profiling of the individual patient with respect to their movement quality. Movements
performed in an ambulant environment (e.g., ADL) can be associated with the proximal
cluster centroid using minimum distance classifier. It is important that the methodology
adapts to changing movement patterns of the patients with time, reflective of their motor
functionality. The patient’s training data can be collected periodically, and the cluster cen-
troids and the associated features (new selected feature set) can be recomputed to reflect
the changing movement patterns. This information (new cluster centroid and feature set)
will be subject-specific due to the inter-subject variability in movement profiles, varia-
tion in the rehabilitation profile, and the associated functional ability of each subject. This
information can be further used by the minimum distance classifier to recognize move-
ments performed in daily life. This methodology can be implemented for online detection
of arm movements. For the training phase, the key steps of cluster formation and feature
selection (being computationally intensive) can be performed in an offline mode when
requested by the clinician, depending on the rehabilitation progress. Online detection
can be used to compute only the required features and the distance to the pre-computed
cluster centroids in near real-time, thereby providing an energy-efficient solution toward
operation of wearable sensors for long durations [50].
TABLE 10.3
Results for Arm Movement Recognition Using CNN for Stroke Survivors
Subjects Accuracy Precision Recall F1-Score
S1 95.42 95.23 94.92 95.18
S2 98.89 99.82 99.68 99.52
S3 98.34 97.52 98.75 98.24
S4 98.91 99.46 99.8 99.27
Average 97.89 98.01 98.29 98.05
mini-batch size of 15. After each training epoch, model performance is evaluated on the
testing set and an early stopping criteria is set to halt the training process when there is
no decrement in training error during the 100 epochs. The classification results for each
subject are shown in Table 10.3.
In this study, ten trials of the semi-naturalistic ‘making-tea’ dataset is used both for
training and testing. Hence, a direct comparison, although not fair, presents the advantage
of deep learning over the feature-engineering-based approach. Table 10.3 demonstrates
that the second approach outperforms the previous method based on k-means clustering
(Table 10.2). While the former approach involves feature extraction and computation of
the minimum distance, the second approach involves convolution operations. Hence, a
trade-off between the complexity and accuracy needs to be analyzed before adopting an
approach for a given application.
10.5 Conclusion
In this chapter, HAR using wearable inertial sensors has been discussed, addressing the
key areas of processing sensor data and inferring activities performed in the ambulant
environment. Activity monitoring has primarily gained importance with the advent of
wearable sensors and development of telemedicine technologies for monitoring elderly
patients. Feature-engineering-based classification techniques that have dominated the
literature and the more recently used deep learning techniques have been presented. In
addition, a brief case study on arm activity recognition of stroke survivors while they
perform archetypal ADL has been included. The data processing details and results are
demonstrated for both approaches on the same dataset which could help potential readers
understand the context of the methodology with respect to the application.
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11
Blood Pressure Monitoring
Rajarshi Gupta
University of Calcutta
CONTENTS
11.1 Introduction......................................................................................................................... 265
11.2 Oscillometric NIBP Measurement Techniques.............................................................. 266
11.3 Plethysmography-Based NIBP Measurement Technique............................................. 270
11.4 Cuffless BP Monitoring Techniques Using Pulse Transit Time (PTT)........................ 272
11.5 NIBP Measurement Methods Based on PPG Only........................................................ 275
11.6 Conclusions.......................................................................................................................... 276
References...................................................................................................................................... 276
11.1 Introduction
Arterial pressure is defined as the hydrostatic pressure exerted by the circulating blood
over the arteries as a result of the pumping action of the heart. Systolic pressure (SBP) is the
highest pressure in a cardiac systole (ventricular contraction), while diastolic pressure
(DBP) refers to the lowest (ventricular relaxation) one. Mean arterial pressure (MAP) is
the algebraic difference between the SBP and DBP determined by dividing the area under
the BP curve of one cardiac cycle by its period [1]. Pulse pressure (PP) is the algebraic
difference between SBP and DBP. Periodic BP measurements are essential for cardiovas-
cular patients, especially those with hypertension. Under normal activity of a patient,
non-invasive and automatic electrical type BP monitors have become popular since many
years due to their portability, quick response, and moderate accuracy level. The ambula-
tory blood pressure (BP) measurement devices are lightweight (often less than 300 g) can
be worn for 24 h by a patient, and can be used to record SBP, DBP, and MAP at regular
intervals, with each record estimated over several cardiac periods (normally less than 30 s).
These can be operated by the patients themselves and do not require any ‘clinical skill’.
Worldwide, there are few apex professional bodies that provide recommendations for
standardization of automatic BP monitoring devices. These include the Association for
Advancement of Medical Instrumentation (AAMI), British Hypertension Society (BHI),
and European Society for Hypertension. The first protocol by the American National
Standards Institute (ANSI)/AAMI SP10 was published in 1987 and subsequently updated
in 1993 and 2002. BHS published a protocol, which was again updated in 1993. The lat-
est ANS/AAMI standard was published in 2009 and revised in 2013 (ISO 81060-2:2013)
together with the International Organization for Standardization (ISO) [2]. To validate an
automated BP monitor with auscultatory reference sphygmomanometer, both the BHS
265
266 Health Monitoring Systems
Power supply
Pressure Air pump control Opto-
sensor isolator
Air valve control
Micro-
controller Display Unit
Air Cuff
with ADC
LP Filter HP Filter Gain
amplifier
FIGURE 11.1
Schematic diagram of a complete oscillometric BP monitor.
Blood Pressure Monitoring 267
the heart. Normally, the initial target pressure in oscillometry is 180 mmHg, which com-
pletely occludes the blood flow in the brachial artery. Figure 11.2a shows the arm cross
section for oscillometry setup. During cuff inflation, the pressure (pc) is applied through
the soft tissue to compress the artery against the bone, gradually decreasing the lumen
area to flat. From this pressure level, the cuff is slowly deflated either by a linear or step
deflation valve to a value below the DBP. During this period, the cuff oscillations due to
the gradual release of blood through the brachial artery are measured by a pressure sen-
sor embedded within the cuff. Initially, the microcontroller provides the control for air
cuff inflations, followed by the bleed valve control. With the gradual deflation of the cuff,
the composite signal consisting of pulsations due to artery and cuff pressure is picked
up by a peizoresistive sensor. The conditioned signal is filtered, amplified, and fed to
the analog-to-digital converter of the microcontroller. The algorithms embedded in the
microcontroller extract the oscillometric waveform envelope (OMWE) from the acquired
data; determine the SBP, DBP, and MAP; and display these values through the liquid crys-
tal display (LCD) unit [6].
A typical oscillometric cuff pressure waveform, also called the cuff deflation curve
(CDC), shows pressure pulsations induced by the artery superimposed on the slowly
decreasing deflated curve pressure, as shown in Figure 11.2b. From this, the oscillometric
waveform (OMW), shown in Figure 11.2c, is extracted by a band-pass filter, with cutoff
frequency between 0.3 and 20 Hz [7]. This eliminates the low-frequency component of
the deflating cuff pressure and the high-frequency components that occasionally creep in
due to hand movements, muscle contraction, etc. Another technique called detrending [8]
involves subtracting a line of best fit (which represents the decreasing cuff pressure dur-
ing deflation) from the CDC to generate the OMW. This requires locating the beginning of
each pulse on the CDC and joining these points through a smooth curve.
The next job is to extract the OMWE. It can be extracted from the OMW by subtracting
the amplitudes of the peaks from the corresponding trough and plotting them against
time, as shown in Figure 11.2d. There are two ways to calculate the SBP, DBP, and MAP
from the OMWE. The first algorithm is the maximum amplitude algorithm (MAA) [9],
which determines MAP as the peak of the OMW. It also fixes the SBP and DBP as two
fixed ratios of the MAP. As described in Ref. [10], the model predicted the SBP and DBP
as 0.593 and 0.717, respectively, of the MAP. These are marked in Figure 11.2d as Ars and
Ard, respectively. However, it has been found that although the MAP is accurately deter-
mined by MAA, the ratios of SBP and DBP are sensitive to the variation of BP waveform,
PP, and age. Hence, the MAA is unable to provide accurate SBP and DBP. The second algo-
rithm which estimates the SBP and DBP is maximum/minimum slope algorithm (MMSA)
[10,11]. This technique estimates the SBP and DBP as the CP at which the first derivative of
the OMWE, when plotted against the time, reaches its maximum and minimum, respec-
tively. As shown in Figure 11.2e, when this derivative is plotted against the cuff pressure,
it reaches a maximum positive value when cuff pressure equals DBP. The minimum nega-
tive value was found to occur at SBP, while at zero differential oscillations correspond to
MAP. In Ref. [12], a trusted boundary around the SBP and DBP is proposed to estimate
the trustworthiness of the estimated BPs, either through MAA or MMSA. The authors
propose a parameter, Ratio2, which provides an expected range of SBP and DBP values.
To overcome the inaccuracies that arise due to the linear and fixed coefficient relation
between the SBP, DBP, and OMWE, neural network (NN)-based approaches were p roposed
by some researchers [13,14]. Other advantages of NN is that it is free from noise sensitiv-
ity. A set of features using the height, derivative, and width of the cuff pressure oscilla-
tions were extracted to form envelopes and fed to a feed-forward NN [15]. The technique
268 Health Monitoring Systems
(b)
(c)
(a)
(d)
(e)
Diff osc./ diff
Pressure
(volts/ mmHg)
FIGURE 11.2
Oscillometric principle: (a) physical setup; (b) CDC; (c) OMW; (d and e) determination of SBP and DBP from
OMWE using MAA and MMSA algorithms.
showed a very good agreement with actual BP, with a gain of 0.89 mmHg over MAA tech-
nique. A comparison of performance between different training algorithms of NN in terms
of estimation error, training time, iterations, etc. were studied in Ref. [16] and compared
with the MAA technique. The different training algorithms studied were steepest descent
(with variable learning rate and momentum, resilient back propagation), quasi-Newton
(Broyden–Fletcher–Goldfarb–Shanno, one step secant, Levenberg–Marquardt), and conju-
gate gradient (Fletcher–Reeves update, Polak–Ribiére update, Powell–Beale restart, scaled
conjugate gradient). It was found that NN outperforms the MAA in terms of estimation
error, with best results obtained using the steepest-descent resilient back propagation
(SD-RBP) algorithm. The problems with these approaches are twofold. First, redundant
OMWE data which degrades the NN performance in terms of generalization. Second, a
larger network size that requires a large training data set. A natural extension of NN appli-
cation for BP estimation is extracting features from the OMWE and feeding to NN for
Blood Pressure Monitoring 269
reduced computational load. In Ref. [17], the OMWE was modeled using a combination of
two Gaussian functions and some features were extracted by minimizing the model error
to feed a feed-forward NN. The method yielded a MAE and SDE of 23.33 and 28.68 mmHg
respectively for SBP and 4.98 and 3.04 mmHg for DBP, respectively, over MAA technique.
There are some attempts to apply the oscillometric pulse morphology analysis for BP
estimation [18,19]. Although these methods are not driven by definite theoretical or physi-
ological background, some of them could achieve reasonable accuracy. In Ref. [19], the
cardiovascular features like augmentation index, reflection index, and stiffness index,
ΔT/T ratio (where, T is the pulse duration and the ΔT is the systolic peak to diastolic peak
duration) were computed from the OMW during the cuff deflation process. The local max-
ima or minima of these features were estimated. The MAP was estimated as average of the
selected pressure corresponding to maximum and minimum values. From the clusters of
the features plot, the pulse corresponding to MAP and the regions of pulses correspond-
ing to SBP and DBP were identified. The SBP and DBP were then calculated as the cuff
pressure by averaging a group of pulses in SBP and DBP regions.
The last category of oscillometric NIBP measurement includes model-based techniques that
utilize any of the following approaches: arterial BP pulse waveform model, transmural and
cuff pressure model, cuff-pressure volume model, or arterial pressure-area model. However,
a common limitation of many of these approaches is the dependence on the empirical coeffi-
cients, which may vary under different conditions like age, vascular compliance, and nervous
system. In Ref. [20], the arterial BP and intracranial BP are modeled as harmonically related
sinusoids modulated by respiration. The model parameters are determined using standard
MATLAB® functions. In Ref. [21], the observation state-space model is considered as the func-
tion of respiratory rate, heart rate, and low frequency signal and use extended Kalman filter
for state estimation. The arterial pressure-area model [22] considers the mechanics of stretch-
ing and collapsing the arterial wall with positive and negative transmural pressure, which is
the differential pressure on the arterial wall due to cuff and arterial BP.
There have been many studies on the errors, repeatability, reproducibility, and calibra-
tion in oscillometric BP measurements [23]. The prime factors those limit the accuracy are:
(a) the motion of the arm that introduces pressure oscillation noise; (b) cardiac diseases
like arrhythmia; (c) variability due to the use of manufacturers’ cuff. Some other sources
of error are undesired external pressure applied on the cuff, tremor, or cardiovascular
abnormalities during the measurement period. The commercial oscillometric BP devices
with several implementations differ in their characteristics due to: (a) different cutoff fre-
quencies for filtering pressure waveform, (b) different methods for averaging pulse size,
(c) different methods and coefficients for extracting SBP and DBP from OMWE, and (d)
different methods for controlling the cuff pressure during inflation and deflation. Due to
many such factors, different manufacturers’ devices achieved good accuracy over a certain
group of patients. The research on repeatability addressed within-device and between-
device repeatability [24]. Several simulators have been developed for the calibration of
oscillometric BP devices; however, their accuracy in representing the OMW for different
age groups and cardiovascular patients has been limited.
An extension of an oscillometric-type BP monitor implementing a smartphone is
described in Ref. [25]. Here, the arm cuff is replaced by the index finger of the human sub-
ject, acting as the actuator. A photoplethysmogram (PPG) and force sensor are integrated
on the backside of a smartphone. A visual guide is provided on the phone screen to alter
the finger pressure on the transverse palmar arch artery. The oscillations due to pulsatile
blood flow through the artery are sensed, and the oscillogram data is collected by the
phone software. From this data, SBP, MAP, and DBP are computed similar to oscillometry.
270 Health Monitoring Systems
FIGURE 11.3
Schematic arrangement of vascular unloading technique.
Blood Pressure Monitoring 271
pc a
b c
SP
MAP
(a)
(b)
SP
MAP
FIGURE 11.4
(a) Determination of SP and MAP from the pulse amplitude and cuff pressure applied; (b) Validation of the SP
and MAP from the playback-recorded cuff-pressure-sensed pulse amplitude.
272 Health Monitoring Systems
A start pulse is generated, following which the cuff pressure (pc) is gradually increased
from a preset value (20/30 mmHg) to an upper pressure limit (200/240 mmHg). On the
detection of pulse peak at higher level, the pc is further increased till pulse waveform
completely disappears (point ‘a’ at Figure 11.4). From this point, a decrease and then an
increase of pc is repeated till the pulse waveform appears (point ‘b’) and then disappears
(point ‘c’). This is to ascertain that pulse waveforms are of sufficient shape and amplitude
to estimate the SP. From point ‘c’, the pc is gradually decreased at a rate of 2–4 mmHg per
heartbeat till the pulsations reappear at point ‘d’. This instant is considered the systolic end
point, and the pc at this is instant gives the SP value. With the further gradual decrease of
pc, the pulse amplitude gradually increases and, after point ‘e’, gradually decreases again.
The pc at the point ‘e’, maximum pulse amplitude, gives the MAP. If the discrimination
between point ‘d’ and ‘e’ becomes difficult due to hand movement, the current measure-
ment cycle is stopped, and pc is set to the initial preset value. Normally, each measurement
cycle is completed within 30 s. The DBP is calculated tentatively with the formula: DP
w= (3 × (MAP-SP)/2.
The personal computer is used offline for data validation. For this, an additional elec-
trocardiogram (ECG) measurement is performed to ascertain the pulse waveform reliabil-
ity. The pulse, ECG, and pc data stored in the RAM are transferred to the computer for a
‘playback’ mode analysis. The graphical representation in Figure 11.4b shows the SP, MAP
in the pc, and plethysmograph waveform. The SP, MAP, and pc are marked in the pulse
waveform.
FIGURE 11.5
BP waves amplified with increasing distance from heart.
amplitude of BP. The well-known Moens–Korteweg equation [37] shows the PWV as a
function of vessel and fluid parameters:
E⋅h
PWV = (11.1)
2ρ ⋅ r
where E: elastic modulus of the vessel, ρ: blood density in the vessel, h: wall thickness, and
r: the inner radius of the vessel wall.
PAT is defined as the time interval between the peaks of R wave in an ECG signal and a
fiducial point on a different peripheral pulse waveform indicating the pulse wave arrival
within the same cardiac cycle. If changes in PWV are negligible in short-time interval, PAT
(T) is inversely proportional to PWV per unit length of vessel and expressed as [38]:
L ρ ⋅ ∆V
T= (11.2)
PWV V ⋅ ∆P
where L: the approximate length of the artery from heart to peripheral site, ΔV: change
in blood volume corresponding to ΔP, the difference between SBP and DBP, and V: blood
volume per unit length. PP is shown to be:
ρ ⋅ ∆V
PP = SBP − DBP = (11.3)
V ⋅T2
2
SBP = SBP0 − (T − T0 )
γ T0
(11.4)
T
DBP = SBP − ( PP0 ) ⋅
T0
where suffix ‘0’ indicates base values of the respective parameters [38], and γ is a coefficient
representing vessel characteristics.
ECG, PPG, and impedance cardiography (ICG) are the three established techniques used
for PPT/PAT estimation. The fiducial points detected on the ECG, PPG, and/or ICG wave-
forms for PAT and PTT measurements are indicated in Figure 11.6. The Q wave of ECG
provides the most accurate measure of the start of ventricular systole. However, sometimes
Q point is either difficult to detect, or not prominent, or buried under artifacts. Hence for
274 Health Monitoring Systems
R-peak
ECG
PEP
ICG
B
P: Point of maximum slope
A
M at systolic phase of PPG.
PTT M: Tangent to P.
N: Tangent to minimum point
of pulse foot.
PPG
PAT P
D: Intersection of M and N.
N
D
Time
FIGURE 11.6
Definition of PTT and PAT.
all practical purposes, R peak, which is detected more easily can be taken as the start of
a ventricular systole. The arrival of pulse wave at a peripheral site is marked by the PPG
foot, which is also minimally disturbed by wave reflection. This point also indicates dia-
stolic minimum time. The most accepted definition for PAT/PTT calculation is the point of
intersection (D) between tangents at maximum slope (point P) and pulse minimum point,
as shown in Figure 11.6. The ICG waveform represents the time derivative of the thoracic
impedance (Z). The B point (Figure 11.6) in ICG waveform represents the onset of cardiac
ejection and is detected by zero crossing before reaching to the maximum amplitude. The
time duration between the R peak and B point represents the pre-ejection period (PEP).
It is worth mentioning that the duration between the Q wave of ECG and B point in ICG
could provide more precise PEP measurement. PTT is defined as the time between the
aortic valve opening (B point on ICG) and a fiducial point on PPG. PAT is the sum of PEP
and PTT.
There are a few measurement challenges for PAT/PTT estimation for NIBP mea-
surement. The choice of PPG sensor modes among transmission and reflection type is
important. Experimentally, the transmission-type PPG provides best estimation of PAT,
operating in infrared range. The contact pressure for transmission-type PPG sensor
should be near to mean BP to get optimal pulse amplitude. The problem with reflection-
mode PPG is that, since the contact pressure is almost zero, the pulse amplitude is
diminished. ICG is normally measured near the thorax, to measure the proximal wave-
form. However, it requires additional current (1–5 mA, 2–100 Hz) injection into the blood
and, hence, is not very suitable for long-term BP measurements. Thus, many realizations
for ambulatory BP measurement use ECG–PPG combination using PAT, instead of PTT
[37–39]. Many other researchers considered PAT and PTT measures as same [40,41]. The
second challenge is unreliable measurements due to artifacts; especially PPG since it
Blood Pressure Monitoring 275
11.6 Conclusions
The number of devices and systems used for continuous BP measurement have been
gradually increasing in the last decade. There has been much research on creating wearable
and ambient assisted living environment around the patients to enable them to perform
their normal activities. Low power sensing, intelligent computing, and short-range com-
munication techniques have played a key role toward this approach. An important applica-
tion has been the use of smartphones, most common personal gadgets. Even a moderately
priced smartphone comes with useful in-built sensors, a moderate-level processor, and
communication interfaces that can be exploited for NIBP measurements. A prime research
using this has been the introduction of image PPG (IPPG) to use smartphone cameras,
either for still images of facial segment or video streaming [57,58]. Another approach is
the use of smart textiles and body sensor networks in conjunction for wearable sensing
of physiological signals, particularly ECG and PPG in the context of BP measurements
[59]. Nevertheless, most of the reported works have been validated on limited population
groups with few postural activities. There are still ample research opportunities in the
area of NIBP measurement in the coming years.
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Blood Pressure Monitoring 279
CONTENTS
12.1 Introduction......................................................................................................................... 281
12.2 Telecardiology in the Management of Acute Coronary Syndromes........................... 283
12.3 Telecardiology in the Management of Chronic Heart Failure..................................... 285
12.4 Telecardiology in the Management of Cardiac Rehabilitation (CR) Program........... 287
12.5 Telecardiology in the Diagnosis and Management of Hypertension......................... 289
12.6 Telecardiology for Arrhythmias and Cardiovascular Implantable Electronic
Devices (CIED).................................................................................................................... 290
12.7 Impact of Telecardiology on Health Costs...................................................................... 293
12.8 Conclusions.......................................................................................................................... 294
Funding......................................................................................................................................... 294
Competing Interests..................................................................................................................... 295
Acknowledgments....................................................................................................................... 295
References...................................................................................................................................... 295
12.1 Introduction
Telemedicine is the ability to provide interactive healthcare utilizing modern technol-
ogy and telecommunications [1]. The terms “telemedicine” or “remote healthcare” and
“e-Health” encompass both “telemonitoring” and telephone support. With telemonitoring,
patients transmit data on their vital signs for real-time monitoring via a communication
link or by store and forward systems. With telephone support, healthcare providers sup-
port patients or caregivers via the standard telephone system, which may involve monitor-
ing of vital signs reported by patients. Methods involving standard telephone are relatively
old-fashioned and present several disadvantages. It is important to imagine new methods,
such as mobile phone communication [2], to enter a more nomadic era whereby monitoring
is not just home confined.
On the other hand, telemedicine allows patients to visit with physicians live over video
for immediate care or capture video/still images and patient data are stored and sent to
physicians for diagnosis and follow-up treatment at a later time. Whether a patient lives
in the center of London or Rome or deep in Sahara, telemedicine is an invaluable tool
in healthcare. Instead of traveling to the nearest specialist, which depending where the
patient lives could be anywhere between a 45-min drive and an 18-h car ride up sanded
roads, the patient’s service provider connects the patient directly to any medical specialist
281
282 Health Monitoring Systems
via telemedicine. The specialist then may hear the medical history and current condition
directly from the patient instead of reading a written account dictated by the first health
provider. There might be medical peripherals (such as electrocardiographic, echocardio-
graphic signals, or nasopharyngoscope probes) that might be handled by the first-line
health provider to allow the specialist receiving important direct elements for diagno-
sis. There might be direct questions from the specialist and immediate replies from the
patient. At the end of the teleconsultation, the specialist can diagnose and recommend
treatment immediately.
In general, the existing remote healthcare or telemedicine systems can be classified in
three broad categories: real-time telemedicine, asynchronous telemedicine, and home care.
The real-time telemedicine is the most common type of remote healthcare approach. Like
the example above, live video allows the provider, patient, and specialist to communicate
together to achieve the optimal outcome for the patient. It might be used in outpatient
specialty consultation, for physician supervision of non-MD first-line health providers. It
requires large bandwidths (>256 kB), although new technologies allow to overcome this
technical constraint [3]. Asynchronous telemedicine is used when both the clinician and
health service provider are not available or not required at the same time. The provider’s
voice or text dictation on the patient’s history, current affliction including pictures and/or
video, radiology images, electro-, and echocardiograms are attached for diagnosis. This
record is either emailed or placed on a server for the clinician’s access to follow up with
his/her diagnosis and treatment plan. On the other hand, it is with home care technolo-
gies that the future approaches. When a patient is in the hospital and he/she is placed
under general observation after a surgery or other medical procedure, the hospital is usu-
ally losing a valuable bed and the patient would rather not be there as well. Home health
allows the remote observation and care of a patient. Home health equipment consists of
vital signs capture and video conferencing capabilities; patient stats can be reviewed and
alarms set from the hospital nurse’s station, depending on the specific home health device.
There are benefits in the telemedicine system for both the spoke sites and the patients.
The formers receive education from the providers and the specialists, in particular. There
is a better health outcome for the patients around the spoke site. The community around
the spoke site enhances confidence in the local healthcare since they know that there are
opportunities of continuing medical education for the local health providers via the tele-
medicine system.
There are important advantages for the patients as well. The loved ones remain in their
community with close family support and cost savings from not having to travel exten-
sively. When urgent care is needed, there is the possibility to look for immediate consulta-
tion. Confidentiality is increased since the patients receive consultation from the specialist
without anyone else knowing apart from the general practitioner (GP) with whom the
patient looks for tele-health. Finally, early diagnosis prior to escalated medical episodes
is more frequently obtained, and in urgent situations the patient is more adequately
stabilized prior to transport. All these have economic consequences: if patients remain
in their communities, they can save money and no expenses will occur looking for medi-
cal advice in the absence of telemedicine. In general, the implementation of telemedicine
systems was shown to substantially reduce health costs [4].
Among the vast range of medical disciplines in which telemedicine has been success-
fully applied (i.e., psychiatry, dermatology, radiology, neurology, ophthalmology, oto-
laryngology, rheumatology, pulmonary, urology, wound care, obstetrics, pediatrics and
neonatology, pathology, emergency medicine, and trauma), telecardiology is certainly
one of the most highly developed technology [5]. Within cardiology, a wide variety of
Wireless Telecardiology 283
both noninvasive and invasive medical signals are available for telehealthcare. Most are
recorded manually by the patients via a device (e.g., systolic and diastolic blood pres-
sure, pulse, 3-lead electrocardiogram (ECG), heart rate variability, body-weight, oxygen
saturation, blood glucose, natriuretic peptides). Others obtained from invasive devices are
recorded automatically (e.g., impedance, incidence of arrhythmias, pulmonary, and left
atrial pressure) [6–8]. In addition to the provision of care to patients with heart disease,
it has a vital role in educating these patients on the nature of their conditions, improving
their compliance to medical therapy, and guiding them in practicing healthy life habits.
The benefit of telecardiology in rural communities is especially important because of its
capability of overcoming the obstacle of the large distances that would have to be covered
in order to access medical assistance. As such, hazardous and even unnecessary trans-
portation of critically ill patients for the purpose of diagnosis can be avoided by remote
expert counseling. Finally, patients can receive second opinion and physicians can consult
experts, capabilities that have proven to have a beneficial effect on both patient survival
and recovery.
Telecardiology is used in a wide range of cardiovascular diseases (i.e., acute coronary
syndromes (ACS), chronic heart failure, hypertension, etc.) and in various settings (i.e., pre-
hospital, in-hospital, and after hospital discharge telecardiology) with beneficial results.
Therapy based
on cardiologist’s
feedback
Chest pain
reporting
Ambulance ECG
Ambulance analysis by
service doctor
FIGURE 12.1
The set-up for prehospital ECG diagnosis in ACS. The ECG is recorded in the ambulance and sent wirelessly to
a primary PCI center. The ECG is interpreted by the on-call cardiologist and, after talking to the patient and/
or paramedic, a decision is made to either redirect the patient for primary PCI (in case of STEMI) or send the
patient to the nearest local hospital for further diagnosis and therapy. ACS, acute coronary syndromes; ECG,
electrocardiogram; PCI, percutaneous coronary intervention; STEMI, ST-elevation myocardial infarction.
feasible either from a moving ambulance or from remote places. The agreement between
tele- and standard-ECG concerning alterations of the ST segment is usually very good [21].
ECG-teleconsultation allows faster triage, shortens door-to-needle time (just slightly ear-
lier than time-to-balloon), and reduces in-hospital delays in STEMI patients compared
with a prospective control group [9,10,15,16]. Transmission of a pre-hospital 12-lead ECG
directly to the attending cardiologist’s mobile telephone decreased door-to-PCI (coronary
angioplasty) time by more than 1 h when patients were transported directly to PCI centers,
bypassing local hospitals [15].
In a large cohort of patients, Brunetti et al. demonstrated that telecardiology evaluation
and direct referral for primary PCI enable STEMI patients living far from a PCI center
and in rural areas to achieve a system delay comparable with patients living in close
vicinity of a PCI center. The routine use of prehospital ECG diagnosis and field triage
was evaluated in a larger region comprising both urban and rural surroundings [22]. The
authors found that pre-hospital diagnosis and triage reduces the importance of distance
to primary PCI center, with a difference of only 9 minutes between patients belonging to
urban versus rural hospital, but with a median distance from the PCI center of 10 km for
the former versus 40 km for the latter. Moreover, the study showed a decreased mortality
in patients with prehospital diagnosis and triage. A recent Dutch study further empha-
sized the importance and value of prehospital diagnosis for patients living far from a
PCI center [23]. Telemetry may also enable the use of prehospital thrombolytic treatment
in STEMI patients, thus reducing the call to treatment times in non-PCI capable settings.
However, this benefit must be balanced against the very small proportion of eligible
patients identified as suitable for prehospital thrombolysis [24].
In addition, ECG-teleconsultation screening may lower the rates of false negative
diagnosis in the case of STEMI with atypical presentation [17,18]. Lastly, the pre-hospital
diagnosis of STEMI has been associated with other favorable outcomes such as a reduction
of the infarct size, a lesser impairment of the ejection fraction, a shorter length of stay, and
Wireless Telecardiology 285
a reduction in early and late mortality [25,26]. Current Acute Cardiac Care Association of
the European Society of Cardiology guidelines on the pre-hospital management of chest
pain and dyspnea, therefore, mandate pre-hospital ECG by telemedicine in the absence of
a physician skilled in ECG interpretation [22].
In conclusion, telecardiology in ACS enables a more widespread access to rapid reper-
fusion therapy (by either pharmacological or mechanical intervention), thereby reducing
treatment delay, morbidity, and mortality [27].
Telemedicine Architecture
Professional
ECG
Network
Chronic Illness
Gateway Clinical
Feedback
Pulse Patient
Onward
Blood pressure transmission
Biomedical
data Telemedicine
acquisition service centre
Body weight
Blood glucose
Ambulance
service
FIGURE 12.2
Telemedicine architecture in heart failure. Multiple non-invasive devices collect the patient’s clinical data that
are transmitted to a telemedicine service center, where trained healthcare professionals may refer red alerts to
a doctor or, if needed, ask for emergency service.
onitoring in heart failure (TIM-HF); and (c) better effectiveness after transition-heart fail-
m
ure (BEAT-HF) do not support those findings [50–52] and conclude that, when compared
with usual care, telecardiology is not associated with a significant reduction of cardiovas-
cular death and hospitalization or an improvement in health-related quality of life.
However, in a recent meta-analysis and systematic review of 39 randomized clinical
trials (11,758 patients), remote patient monitoring was associated with a reduction of
20% in all-cause mortality and of 37% in hospitalizations for HF, as well as to shorten
the HF-related length of hospital stay [53]. In a budget impact analysis, the adoption of
a telemedicine-based strategy entailed a progressive and linear increase in costs saved.
In an other meta-analysis including 6,000 patients from 25 trials, case management type
interventions led by a HF specialist nurse reduce HF-related readmissions after 12 months
of follow-up, all-cause readmissions and all-cause mortality [54].
In the above-mentioned studies, telehealthcare was based on non-invasive medical
data, but other studies have investigated the management of HF patients using data from
implantable devices [55,56]. The efficacy of an implantable monitor lies in the fact that it
continuously measures and stores hemodynamic information of HF patients that can be
reviewed remotely. An increase in pulmonary vascular congestion is reflected by increased
impedance [57], thus intra-thoracic impedance monitoring may be a valuable tool in the
management of HF in patients, such as those with an implantable cardioverter defibril-
lator (ICD) or a cardiac resynchronization therapy device (CRT) [58]. This is supported
by a study that found significantly greater sensitivity to predict worsening HF events for
intra-thoracic impedance monitoring compared to acute weight increase [59]. Likewise,
left ventricular filling pressures and pulmonary artery pressures are correlated with clini-
cal congestion, functional limitation, and poor prognosis in HF patients [60]. Since the
intracardiac and pulmonary artery pressures increase several days to weeks before the
onset of clinical symptoms of congestion, close monitoring may provide early warning
and assist appropriate management, including changes in medication. Abraham et al. [61]
used a wireless implantable hemodynamic monitoring system to measure pulmonary
artery pressure and reported that the use of the system significantly reduced the duration
of hospital stay and the rate of hospital admissions related to HF, the latter by 30%. In a
study that monitored left atrial pressure, this monitoring was associated with the reduced
risk of acute decompensation and death [62].
In conclusion, the majority of evidences show that telemonitoring in HF not only reduces
costs and increases the revenue but also saves lives and prevents complications by also
improving patient safety and quality of care [54]. Nevertheless, additional randomized
clinical trials using third- or fourth-generation telemedical systems that combine nonin-
vasive and invasive variables with daily monitoring and management are needed to fully
clarify the value of telehealthcare in cardiology.
on telerehabilitation is rather scarce and mainly focuses on low-risk patients [64,65]. These
studies have shown favorable effects resulting from telemonitored CR.
Telemedicine-based rehabilitation after cardiac surgery has been demonstrated to be
feasible and safe. Scalvini et al. [66] demonstrated that patients who underwent cardiac
surgery (EuroSCORE 0–10) followed a 1-month home rehabilitation program supervised
by a nurse-tutor and a physiotherapist, and they had a significant increase in the 6-min
walking test distance at the end of the program compared to the baseline (404 vs 307 m,
p<0.001). When two models of assistance (telecardiology versus usual care) for patients
discharged after ACS were compared in the assessment of angina, telecardiology slightly
reduced hospital readmissions (44% vs 56%) in a short-term follow-up [67]. Giallauria
et al. assessed the efficacy of telecardiology in improving the effects of CR during three
sessions weekly for 8 weeks in post-MI patients with reduced ejection fraction. Physical
capacity and exercise duration improvement were comparable in patients trained in an
out-patient center and at home with ECG monitoring, but patients who trained at home
without telemonitoring failed to obtain favorable effects [68]. In a large population who
survived hospitalization after sustaining an acute myocardial infarction, subjects followed
by telemedicine support had significantly higher survival rates at 1 year compared to usual
care (4.4% vs 9.7%; p<0.0001) [69]. The direct 12-week comparison of a conventional and a
telemedicine approach showed the suitability of telemedicine for delivering cardiac reha-
bilitation for risk factor modification and exercise monitoring to patients who otherwise
would not have access to it [70].
Studies wholly dedicated to home-based telerehabilitation of HF patients showed the
high impact of this intervention on health status. One of the first studies to assess telere-
habilitation in HF patients specifically was that of Smart et al. who described a 35-week
home-based CR model initiated 16 weeks after a hospital-based exercise training program.
Patients were provided with heart rate monitors and exercise diaries. They were also
offered scheduled telephone and e-mail consultations. This study showed that home reha-
bilitation based on heart rate monitoring maintained the oxygen consumption improve-
ment achieved during previous rehabilitation in hospital settings, only in patients with HF
adherent to the program [71]. Two randomized studies evaluated telerehabilitation in HF
patients. The first one showed that an 8-week home-based telerehabilitation program pro-
vided improvements in physical capacity and quality of life similar to that of a standard
outpatient rehabilitation program [72]. The second one demonstrated that a home-based
telemonitored Nordic walking training program was well accepted, safe, and effective
among HF patients, including those with implanted devices [73].
Telerehabilitation may also serve as a helpful strategy for reducing depression that
are common in HF patients and that work as a barrier for exercise training. Allowing a
continuous monitoring and management of patients in a familiar environment and as
part of the everyday way of life, telerehabilitation may enhance exercise adherence and
ultimately health-related quality of life [74]. Moreover, telerehabilitated patients might
receive a persistent psychological telesupport and teleassistance from the telemonitoring
team (nurse, physician, physiotherapist) [71,73]. In the Personal Decision Support System
for Heart Failure Management (HeartMan) study, wireless monitoring of patients’ physi-
cal condition and psychological state is integrated by a decision support system to allow
personalized lifestyle advice [48]. This system might increase adherence and efficacy of
physical exercise both supporting physical activity by a psychological support and adapt-
ing the strength of exercise to the current physical condition of the patient (based on
health wireless telemonitoring). The trial is still ongoing and results are expected within
the end of 2019.
Wireless Telecardiology 289
communication technologies) where they are reviewed by the referring physician for treat-
ment adjustments.
BPT is a well-established practice that has been shown to improve patient adherence to
treatment regimens and to achieve target BP levels. It has the potential for the improvement
of hypertension control and associated healthcare outcomes, as documented in numerous
meta-analyses [79,82–84]. In 7,037 hypertensive patients enrolled in 23 selected high-quality
randomized controlled studies, a regular BPT at home was associated with a significantly
larger reduction in both office and ambulatory BP as compared with usual care [85], with a
significantly larger proportion of BPT patients achieving office BP normalization. Another
interesting feature about telecardiology is its ability to provide measurements of BP with-
out provoking the “white coat” effect [86]. Finally, BPT has been demonstrated to reduce
the total cost of hypertension care compared with usual care [87].
Web-based telemedicine platforms may also be used to provide home or 24-h ambulatory
BP monitoring through community pharmacies, extending the screening for hypertension
and providing a quick, accurate, and professional feedback and adjustment of care plans
in treated hypertensive patients, with the support and supervision of the general practitio-
ner or the specialist [88]. The synergy between BPT and pharmacist case management of
hypertensive patients may facilitate high BP screening and detection. Furthermore, add-
ing web-based pharmacist care to BPT and web-provided education on lifestyle may be
particularly effective for improving BP control in treated hypertensives. The effectiveness
of such a telemedicine-based approach for hypertension management has been shown in
a number of randomized or observational studies [89–91].
In conclusion, current evidence suggests that telecardiology, and in particular BPT, may
be useful in hypertensive patients needing a tighter BP control (such as those at high-risk).
Moreover, BPT may support doctors for a closer and continuous follow-up of hypertensive
patients as well as in situations that require the monitoring of multiple vital signs. The dif-
fusion of telecardiology solutions among the general public will help patients’ caregivers
to improve the quality and the effectiveness of the delivered care.
Implantable Satellite
cardioverter Connectivity
defibrillator (ICD)
Telemedicine Doctor
Phone or internet service centre
connectivity
FIGURE 12.3
Remote monitoring system applied to CIEDs. A device equipped with a micro-antenna automatically
communicates with a transmitter located close to the patient. Data are then received and analyzed in a tele-
medicine service center by trained staff and then, if necessary, referred to a dedicated cardiologist.
time off work. Moreover, they require space and dedicated staff in the outpatient clinic
because of the workload associated with every visit.
Remote monitoring and remote follow-up by the use of telecardiology offers an alter-
native management strategy [94]. Preferably, the system should be able to automatically
transmit data stored in the device to the outpatient clinic using the wireless global system
for mobile communication (GSM) network or a landline. Automatic wireless data trans-
mission requires a pace-maker or ICD/CRT equipped with a micro-antenna for commu-
nication with a transmitter located close to the patient as shown in Figure 12.3. This setup
diminishes the need for patient compliance and increases the frequency of data trans-
missions. Several companies provide systems for automatic wireless data transmission.
Almost all data stored in the device’s memory (e.g., battery voltage, lead characteristics,
arrhythmias, alerts) can be transmitted. The alerts may be based on a change in device
performance (battery status, lead impedance), programming (disabling of ventricular
fibrillation therapy, insufficient safety margins for sensing or capture), or medical data
(arrhythmias, indication of lung fluid accumulation). Data are usually transmitted to a
central database, where they are processed and made available to the physician on a secure
webpage. Additional notifications by e-mail, SMS, fax, or phone messages may be valuable
when critical data are available for consultation. Daily monitoring allows transmission of
data with any predefined alerts to a physician. Telemonitoring also reduces the amount of
follow-up visits for checking the device’s parameters (e.g., battery status, leads impedance,
etc.). Several reports showed home-monitoring as a safe alternative to conventional care
and significantly lowered the number of ambulatory visits [95,96], increasing efficiency for
healthcare providers, and improving quality of care for patients [97].
The safety and clinical benefits conferred by telehealthcare in patients with implantable
devices have been validated in several studies [95,98,99]. In the COMPAS trial [95], long-
term remote monitoring of pace-maker users was a safe substitute for conventional follow-
up, decreased the number of ambulatory visits, and enabled early detection of important
clinical and device-related adverse events. Another study showed that half of the regu-
larly scheduled visits could be avoided by the use of remote monitoring, without impair-
ing patient safety [98]. Additionally, only 6% of patients undergo device reprogramming
292 Health Monitoring Systems
or admittance to hospital during clinic visits, and thus 94% of these visits might as
well be executed by remote monitoring [99]. Remote monitoring could diagnose 99% of
arrhythmia- or device-related problems in patients with an ICD, when combined with a
clinical follow-up. In addition to fewer scheduled clinic visits, unscheduled visits follow-
ing an ICD shock might be prevented by remote follow-up; after such an event, the patient
may perform manual upload of data, transmitted to the healthcare provider for imme-
diate determination of whether the shock was appropriate or not, and whether device
reprogramming or medical modifications are needed [100]. Finally, previous observations
showed that remote monitoring reduces the time to event recognition and diagnosis and
the time from the event to a clinical decision for the individual patient [55,101].
Remote monitoring has also been showed to have a beneficial impact on survival. As
identified in the long-term outcome after ICD and CRT implantation and influence of
remote device follow-up (ALTITUDE) survival study [102], a 50% higher survival rate was
found in patients with ICD and CRT-D devices followed with remote monitoring than in
those followed only with in-person visits. The survival benefit did not appear to be related
to patient age, gender, device type, or how long the device had been implanted. Also,
no differences were found in survival rate related to a patient’s economic or educational
status. Although the ALTITUDE Survival Study included only patients with ICDs and
CRT-Ds, similar results were replicated in a study designed to include pace-maker and
CRT pace-makers in addition to ICD and CRT-D. Interestingly, Varma et al. [103] discov-
ered that survival rate improvement correlated with adherence to remote monitoring pre-
scription. The reported 50% higher survival rate was seen in patients who demonstrated
75% or more adherence to the remote monitoring protocol. However, the group that par-
ticipated in remote monitoring with a protocol adherence rate less than 75% also gained a
survival benefit when compared with those study participants who did not participate in
remote monitoring. Similarly, a reduction in all-cause mortality rate greater than 50% was
demonstrated by a group of remotely monitored ICD or CRT-D patients with chronic HF
on optimal medical therapy when compared with the control group that received standard
in-office follow-up [104]. Anyway, to date it is not known with certainty whether remote
monitoring independently increases survival rate or whether it is associated with other
factors that increase survival rate, such as more compliant patients and health care provid-
ers who practice the most up-to-date optimal medical care.
Remote monitoring is associated with a reduction in inappropriate ICD shocks. In the
Effectiveness and Cost of ICD follow-up Schedule with Telecardiology (ECOST) trial, the
number of inappropriate shocks in the remotely followed group was 52% lower than that
in the control group. Remote monitoring expedites provider notification of ICD shocks,
which in turn enables a CIED follow-up clinic to assess the rhythm together with the
appropriateness and effectiveness of the shock. As needed, a clinic visit could then take
place with the goal of preventing further inappropriate shocks through device reprogram-
ming or prescription of other therapies aimed at management of arrhythmias and other
comorbidities [105]. This is of particular interest because the reduction in ICD shocks plays
an important role in the physical and psychological well-being of patients with CIEDs and
it is well documented that ICD shocks are linked to incidence of anxiety, depression, and
sometimes traumatic syndromes in patients having an implanted defibrillator [106], as
well as to an increase in mortality [107].
Remote monitoring may finally be a valuable tool to assist in the diagnosis of arrhyth-
mias or ECG abnormalities [108,109]. Symptoms secondary to arrhythmias, such as pal-
pitations and syncope, can be documented on ECG tracings, but many ECG changes are
transient or paroxysmal, and the search for corroboratory evidence of these arrhythmias
Wireless Telecardiology 293
can be lengthy and problematic and missed even by long-term Holter ECG recordings
[110]. The detection of these arrhythmias has crucial therapeutic implications, such as
the provision of anti-arrhythmic and anticoagulation treatment for high-risk atrial fibril-
lation (AF) patients, permanent pace-makers for patients suffering from high-degree
atrioventricular nodal block, ablation for patients suffering from recurrent supraven-
tricular tachycardia, and others. The diagnostic yield increases substantially with the
use of patient-activated short-term ECG recordings [110]. The use of a remote device that
enables real-time cardiac arrhythmic monitoring and able to provide a specific diagnosis
and recommendations of action has been demonstrated to raise the rate of diagnosis and
the efficacy of treatment [111]. In addition, it enabled patients to undergo dose titration
or change of their medication in the outpatient setting, thus reducing the rate of hospi-
talization [112].
AF remote detection and management deserve a particular mention in this setting.
Remote monitoring is advantageous in patients with paroxysmal AF because alerts could
identify the start-point of the arrhythmia, resulting in an unscheduled follow-up either in
office or by phone, and appropriate interventions (e.g., antiarrhythmic medication, antico-
agulation or antiplatelet medication, external cardioversion, device reprogramming) [113].
AF may trigger inappropriate therapy or promote loss of effective cardiac resynchroniza-
tion and may places patients at risk for stroke, especially if they are not receiving antico-
agulation therapy, or for HF decompensation. Almost all modern CIEDs have AF detection
criteria that will trigger a remote alert when the CIED and/or the alert notification criteria
in the remote monitoring web portal are programmed to do so. Timely access to arrhyth-
mia data, including electrogram, rate, onset, and duration of episodes, enables clinicians to
assess the appropriateness of anticoagulation therapy and rate control in accordance with
evidence-based AF management guidelines.
referrals in 65.8% of cases, with an estimated savings for the NHS of about £300,000 [121].
Finally, self-monitoring with self-titration of anti-hypertensives and telemonitoring of BP
measurements not only was shown to reduce blood pressure, compared with usual care,
but also to represent a cost-effective use of healthcare resources [122].
Opposite, a comprehensive review of the literature suggests that there is a lack of con-
crete evidence to assess the economic impact of telecardiology [123]. Comparing cost-
effectiveness of telemonitoring versus usual care in patients with HF in the TEHAF study,
there were no significant differences in annual costs per patient between groups [124]. In
another study, telecardiology was not cost effective after recent discharge in patients with
HF, with a similar gain in terms of quality adjusted life years by patients using telehealth
in addition to usual care or usual care only [125].
In conclusion, although there is a lack of concrete evidence to assess the economic impact
of telecardiology, mainly due to different technologies used (first vs second generation
telecardiology tools, namely telephone follow-up versus modern communication technol-
ogies, Web 2.0 tools, or even artificial intelligence) and settings (rural versus urban, HF
versus rehabilitation, etc.), the majority of the recent evidence shows some potential cost
reduction associated with the implementation of telecardiology supporting good agree-
ment with some clinical hopes and/or expectations expressed earlier [126].
12.8 Conclusions
By adapting to the population scale the potential of technology and intervening at the level
of primary and secondary prevention, wireless telecardiology could become an essential
tool for delivery of effective health care in patients with a broad range of cardiovascular
diseases, in turn reducing the cardiovascular disease burden in a society-wide manner
and alleviating the tremendous pressure (and costs) the national health services are under.
The next-generation of remote telecardiology systems should aim to identify those indi-
viduals (in primary prevention) or patients (in secondary or tertiary care) who may need
clinical attention in short-term even before the symptoms are manifested and thereby
allowing the clinicians to move towards a proactive care pathway. Finally, telecardiology
data will help clinicians to study the characteristic trends of physiological parameters in a
person-centric way, targeting diagnosis and treatment on the patient and no more on the
disease.
Funding
The research described in this paper partially was carried out in the Chiron project
(https://artemis-ia.eu/project/17-chiron.html), which was co-funded by the ARTEMIS
Joint Undertaking (grant agreement # 2009-1-100228) and by national authorities, and
partially in the HeartMan project (http://cordis.europa.eu/project/rcn/199014_en.html),
which received funding from the European Union’s Horizon 2020 research and innovation
program under grant agreement No 689660. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Wireless Telecardiology 295
Competing Interests
The authors have declared that no competing interests exist.
Acknowledgments
The consortium of the HeartMan project, which partially funded this research, consists
of Jožef Stefan Institute, Sapienza University, Ghent University, Italian National Research
Council, ATOS Spain SA, SenLab, KU Leuven, MEGA Electronics Ltd., and European
Heart Network.
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13
Diabetes Monitoring System
Saptarshi Das
University of Exeter
CONTENTS
13.1 Measurements, Model Estimation, and Control of Diabetes....................................... 303
13.2 Minimal Models..................................................................................................................304
13.3 Maximal Models.................................................................................................................309
13.4 Diabetes Monitoring Signals and Controls..................................................................... 310
13.5 Non-Invasive Diabetes Monitoring.................................................................................. 311
13.6 Recent Research Trends in Diabetes Monitoring and Control..................................... 312
13.7 Conclusion........................................................................................................................... 315
References...................................................................................................................................... 315
303
304 Health Monitoring Systems
13.2 Minimal Models
Minimal models describe the key functionality rather than the detailed substrate/hormone
interactions. For large detailed models, it is usually difficult to estimate all model param-
eters from in-vivo dynamic data; whereas minimal models are much simpler. The desirable
features of a minimal model include:
• Physiologically motivated
• Parameter estimation with good precision is possible through a single dynamic
response of the system.
• The model parameters should vary within physiologically plausible ranges.
• The whole system dynamics can be described with a minimum number of
parameters.
The glucose system is divided into many parts of a compartmental model [1]:
dG(t)
= − a1G(t) − a2 I (t) + J (t)
dt
(13.1)
dI (t)
= a3G(t) − a4 I (t)
dt
The minimal model assumes that glucose kinetics can be described by one compartment
and both the remote insulin controls, the net hepatic glucose balance (NHGB) and periph-
eral glucose disposal:
dQ1 (t)
= NHGB (Q(t), I ′(t)) − Rd (Q(t), I ′(t)) + D ⋅ δ (t), Q(0) = Qb
dt
dI ′(t)
= − k3 ⋅ I ′(t) + k2 ⋅ [ I (t) − I b ] , I ′(0) = 0 (13.2)
dt
Q(t)
G(t) =
V
where Rd is the rate of glucose disappearance from the peripheral tissues, which is
given as:
dQ(t)
= − p1 + X (t) ⋅ Q(t) + p1 ⋅ Qb + D ⋅ δ (t), Q(0) = Qb
dt
dX (t)
= − p2 ⋅ X (t) + p3 ⋅ [ I (t) − I b ] , X (0) = 0 (13.5)
dt
Q(t)
G(t) =
V
X (t) = ( k 4 + k6 ) ⋅ I ′(t)
p1 = k1 + k5
p2 = k 3 (13.6)
p3 = k 2 ⋅ ( k 4 + k 6 )
p4 = NHGB 0 − Rd0 = p1 ⋅ Qb .
p3
SIIVGTT =
p2
⋅V (dL/kg/min per µU/mL ) , (13.7)
where SIIVGTT is a steady-state measure which means it does not account for how fast or slow
the insulin action takes place.
Another mass balance equation is used by describing the rate of appearance of glucose
into plasma (Ra) as:
dQ(t)
= − p1 + X (t) ⋅ Q(t) + p1 ⋅ Qb + Ra(t , α ) Q(0) = Qb (13.8)
dt
dX L (t)
= − p2 ⋅ X L (t) + p3L [ I (t) − I b ] , X L (0) = 0, (13.10)
dt
where
p2: rate constant describing the dynamics of insulin action on glucose production,
p3L : scale factor governing the amplitude of hepatic insulin action.
dX 1 (t)
= − p2L ⋅ X 1 (t) + p3L ⋅ [ I (t) − I b ] , X 1 (0) = 0
dt
(13.12)
dX L (t)
= − p2L ⋅ X L (t) + p3L ⋅ X 1 (t), X L (0) = 0
dt
and
dG(t) dG(t)
kGR ⋅ , if ≥0
dt dt
X Der (t) = (13.13)
dG(t)
0, if <0
dt
EGP can be calculated using the endogenous glucose concentration (Gend ) which indicates
the compartment of total glucose concentration measured in the plasma due to glucose
production. The quantity Gend is related to EGP by the integral equation:
t
Gend (t) =
∫ h(t, τ ) ⋅ EGP(τ ) ⋅ dτ + G ⋅ h(t, 0)
0
b (13.14)
308 Health Monitoring Systems
where h(t , τ ) is the time-varying impulse response of glucose system given by tracer
minimal model, and Gb is the basal glucose.
The whole-body-to-tissue model usually employs tracer elements and is described as the
following ODEs:
dCc (t)
= K1Cp (t) − ( k2 + k3 ) Cc (t) + k 4Ce (t), Cc (0) = 0
dt
dCe (t)
= k3Cc (t) − ( k 4 + k5 ) Ce (t), Ce (0) = 0 (13.15)
dt
dCm (t)
= k5Ce (t), Cm (0) = 0
dt
and
One of these models can calculate the fractional uptake of the FDG and K [mL/mL/
min] as:
K1 k3 k5
K= (13.17)
k2 k 4 + k2 k5 + k3 k5
As described before, during the intravenous glucose tolerance test, the basal insulin
secretion model is given by the pancreatic secretion rate (SR) as:
dF
= − m ⋅ F(t) + Y (G, t), F(0) = F0 (13.20)
dt
with F0 as the amount of insulin released immediately after the glucose stimulus and
Y (G, t) as the provision of new insulin which depends on the glucose level:
dY (G, t) 1
= − ⋅ [Y (G, t) − Y (G, ∞)] , Y (0) = 0. (13.21)
dt T
13.3 Maximal Models
Compared to the minimal models, the maximal models are more detailed or fine-grained,
non-linear, and higher-order, with a large number of parameters to estimate. Usually such
models are not estimable without running large experimental investigations. However,
they have been widely used for simulation to check model validity. A healthy state simula-
tor, described in Ref. [1], has been usedon 204 non-diabetic subjects with simulation models
of plasma glucose, plasma insulin, EGP, glucose rate of appearance, glucose utilization,
insulin secretion, etc. Maximal models have also been used for pre-diabetes simulator,
type 2 diabetes simulator, and type 1 diabetes simulator with unit process models and
forcing functions for the liver, gastrointestinal tract, muscle and adipose tissue, beta cell,
etc. In-silico subject simulation has been reported in Ref. [1] with a feedback controller
and simulated insulin pump for type 1 diabetes. For insulin secretion, the following mass
balance equation is used for the intermediate pool (I):
∞
dI (t)
dt
= M( g , t) − r ⋅ I (t) − p + ⋅ I (t) + p − ⋅
∫ 0
h( g , t) ⋅ dg (13.22)
dh( g , t)
= p + ⋅ I (t) ⋅ j( g ) − p − ⋅ h( g , t) − f + ⋅ h( g , t) ⋅θ (G − g ) (13.23)
dt
Here, θ (G − g ) is the heaviside step function which takes the value of unity for G > g or
zero and I is the total intermediate pool. The primary flux p + I distributes among cells with
threshold g, described the time constant function j( g ).
310 Health Monitoring Systems
Using these inputs, the multivariable adaptive controller drives the insulin infusion pump
for the patient. The measurements used in this scheme are continuous glucose monitoring
sensor and wearable biometric sensors. The study in Ref. [2] with clinical experiments also
concluded that the multivariable approach provides better results than the single variable
version using only CGM measurement.
Meal detection has been further researched in Ref. [3] for 9 patients with type 1 diabetes
over 27 different main meals. The multivariable adaptive artificial pancreas system
includes a minimal model similar to Equation (13.1), followed by the unscented Kalman
Diabetes Monitoring System 311
filter for state estimation of the non-linear system. The CGM measurements are used for
nine subjects during breakfast, lunch, and dinner to validate the algorithm. A similar ana-
log PID-based glucose-control algorithm known as the Hill equation was implemented in
Ref. [4] using a beta-cell model comprising an ODE and the sigmoid function. For type 1
diabetes control using artificial pancreas, PID and sliding-mode-reference-conditioning-
based safety auxiliary feedback control has been implemented in Ref. [5] with enhanced
robustness and fault-tolerance properties.
A model predictive iterative learning control scheme has been proposed in Wang et al.
[6] for artificial pancreatic beta cells in type 1 diabetes. This involves a virtual patient
which uses an autoregressive exogenous model. The robustness of the MPC and iterative
learning control on repetitive and non-repetitive diets, robustness to subject variations,
and set-point updating have also been investigated.
For type 1 diabetic patients, an improved overnight safety scheme has been proposed
in Facchinetti et al. [7] for online failure detection of the glucose sensor and insulin pump
system. The two cases considered are CGM sensor failure and continuous subcutaneous
insulin infusion pump failure. The failure detection employs a Kalman predictor and
online prediction and alert module. The method was validated on in-silico data of 100 vir-
tual subjects and real type 1 diabetes mellitus data. The robustness of the failure detection
monitoring system against noise and the domain of validity has also been investigated on
these two databanks.
In the field of closed-loop control of diabetes, MPC has wide applicability over
conventional therapy in the development of artificial pancreas with various modules, for
example, data handling/filtering, state estimation/update, closed-loop control algorithm,
safety supervision algorithm, actuation, data-logging and outcome measures, etc. [8]. The
important issues for modular control are design flexibility, incremental testing, regula-
tory approval, and deployment. The safety supervision algorithm has various elements
like insulin request classifier, correction filter, hypoglycemia indicators, etc. This system is
based on a linear MPC with a linearized model of the non-linear insulin-glucose dynam-
ics. Moreover, the MPC works on the difference between the CGM signal and the patient’s
nominal blood glucose profile with improved individualization capability and has been
validated on in-silico experiments.
application control, etc. The embedded back-end implementation was tested against power
consumption, security, in-vitro testing, system stability, safety, in-vivo testing, etc.
Much of the future research in this domain is expected to have a few key functionalities –
unobtrusive sensing, modeling the onset and progress of diabetes mellitus, and user-
centered approach [10]. Zarkogianni et al. [10] reviewed various emerging technologies
for the management of diabetes mellitus. Among the commercially available devices, only
two use non-invasive methods like Raman spectroscopy (HG1-c by C8 Medisensors) and
thermal ultrasound and electromagnetic (GlucoTrack by Integrity Applications Ltd.). These
devices have been benchmarked against other invasive devices by Dexcom, Medtronic,
and Abbott with detailed comparison of the sensor lifetime, sensor warm-up time, fre-
quency of calibration, frequency of recording, accuracy, etc. In Ref. [10], several artificial
intelligence models for risk prediction and early diagnosis of type 2 diabetes have been
reviewed in, for example, fuzzy neural networks, adaptive neuro-fuzzy inference sys-
tem, support vector machine, linear discriminant analysis, adaptive network-based fuzzy
inference system. In addition, a number of experts and their performances are c ompared
using classification accuracy, sensitivity, specificity, etc. For long-term risk modeling,
several statistical models – Cox regression, Tobit survival regression, Cox proportional
hazard model and fractional polynomials, univariate and multivariate logistic regres-
sion, survival analysis, Weibull proportional hazard regression, Markov model, etc. – have
been used for different cohort sizes (55, >1,000, >5,000, >1.2 million, etc.). Moreover, several
glucose prediction models are compared in Ref. [10] based on artificial intelligence and
autoregressive models. Long-term implanted sensor/telemetry systems are studied in Ref.
[11] for glucose monitoring in diabetic patients. Raman spectra of blood has also been used
for glucose monitoring devices in Ref. [12].
FIGURE 13.1
Word-clouds of the titles of recent diabetes monitoring research papers. (Datasource: Scopus.)
FIGURE 13.2
Type of recent papers on diabetes monitoring system. (Datasource: Scopus.)
and such recent technological advancements take years to get assimilated into clinical
practice. Furthermore, there are more outliers in the papers between the years 2001 and
2013 as shown in Figure 13.4 which indicates that there are a few highly impactful papers
compared to the citation of average papers published in recent years. Next, the source
titles of the top 2,000 papers on this topic are explored in Figure 13.5. Most of the impact-
ful papers were published in journals like Diabetes Care (American Diabetes Association),
Diabetes Technology and Therapeutics (Liberto Pub.), Journal of Diabetes Science and
Technology (Sage), Diabetic Medicines (Wiley), Diabetologia, Diabetes (American Diabetes
Association), The Lancet, IEEE Transactions on Biomedical Engineering, etc. A more
focused study on mobile health technologies for diabetes mellitus ranging from 2011 to
2017 and using 3 databases like ScienceDirect, SpringerLink, and IEEEXplore have been
reported in Ref. [13].
With the advent of 5G and smart technologies, several personalized options have
opened to analyze big healthcare data on clouds [14]. The five goals of technology using 5G
314 Health Monitoring Systems
FIGURE 13.3
Indexed and author’s keywords for recent papers on diabetes monitoring system. (Datasource: Scopus.)
FIGURE 13.4
Year-wise citations of recent papers on diabetes monitoring system. (Datasource: Scopus.)
FIGURE 13.5
Source titles for recent works on diabetes monitoring system. (Datasource: Scopus.)
13.7 Conclusion
This chapter reviews the recent trends in diabetes monitoring using the top 2,000 cited
articles and reports text analytics results on past research activities and emerging trends
in this domain. It also reviews various diabetes monitoring and control models available
in the literature which are primarily divided into two categories – minimal and maximal
models. Although the citation trends suggest that fundamental research in this domain is
nearly saturated, there are many commercialization activities and patents issued recently
on this topic [15]. This provides an opportunity to grow new industries in this domain
which requires a good and affordable business model particularly for low and middle-
income countries of the developing world where diabetes is emerging almost at the scale
of a large epidemic.
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Index
317
318 Index
N R
Na–K pump, 21 Receiver operating characteristic (ROC), 112
Neural networks, 165–167, 178, 180, 207, 220, Reconstruction, 9, 60, 61, 69, 70, 74, 77–79, 81–83,
249, 312 87, 89–91, 177, 178, 180, 181, 183, 185,
Noise, 6, 20, 25–28, 32, 39, 40, 45–48, 60, 63, 72, 186, 188, 191, 192, 194, 198, 200, 203, 205,
74, 76, 78, 87, 88, 91, 110, 112, 149–152, 206, 208–210
154, 156, 158, 160, 178, 179, 181, 186, 200, Recurrent network, 166
249, 258, 267, 269, 311 Recurrent neural network (RNN), 251, 252
Normalized percent root mean square difference Regression, 111, 164, 168, 178, 179, 181, 183, 194,
(PRDN), 60, 61, 64, 79, 82–87, 91, 93 205–207, 220, 275, 312
ReliefF algorithm, 250
O Remote healthcare, 2–4, 134, 179, 198,
281, 282
Optimization, 83, 111, 167, 168, 290, 304
Respiration, 3, 24, 26, 35–37, 40, 49, 88, 94,
Orthogonal expansion, 74
136, 153, 269, 275
Oscillometric method, 266
Respiratory inductive plethysmography, 37
Oscillometric waveform/oscillometric
Respiratory rate, 4, 5, 7, 9, 111, 113, 269
waveform envelope, 267–269
Resting membrane potential (RMP), 21
Ottobock sensors, 149
Restricted Boltzmann machine, 251
P Run length encoder (RLE), 72