International Journal of Reconfigurable and Embedded Systems (IJRES)

Vol. 13, No. 2, July 2024, pp. 483~490

ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i2.pp483-490  483

Low-cost electrocardiogram monitoring system for elderly

people using LabVIEW

Ricardo Yauri1,2, Max Delgado2, Enzo Flores2, Oscar Llerena3

1
Facultad de Ingeniería, Universidad Tecnológica del Perú, Lima, Perú
2
Facultad de Ingeniería Electrónica y Eléctrica, Universidad Nacional Mayor de San Marcos, Lima, Perú
3
Faculty of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, South Korea

Article Info ABSTRACT

Article history: Cardiovascular diseases increase due to factors such as obesity, an
inadequate diet, and are a problem due to shortages of medical personnel and
Received Oct 1, 2023 hospitals. In this case, the implementation of technological solutions is
Revised Dec 15, 2023 presented as a necessity to prevent heart diseases. Various approaches are
Accepted Dec 26, 2023 used to design low-cost electrocardiogram (ECG) devices, from the use of
Bluetooth technology to facilitate data transmission, to the development of
wearable ECG devices that use artificial intelligence. The objective is to
Keywords: develop a monitoring system in LabVIEW to visualize the heart rhythms of
older adults in the city of Lima (Peru), focusing on ease of use and
Arduino adaptation to their needs, with the purpose of collaboration between health
Elderly professionals. A development approach is used that encompasses design,
Electrocardiogram implementation, and iterative testing, as well as practical evaluations and
LabVIEW pilot testing. As a result, the correct functioning of the ECG device was
Monitoring validated. Electronic components and electrodes were integrated into the
board to capture cardiac signals, energized with batteries and sending the
information to an interface in LabVIEW. In conclusion, a portable ECG
device has been developed that uses operational amplifiers (Op-amps) and
analog filters to reduce noise in cardiac measurements and an intuitive
interface in LabVIEW.
This is an open access article under the CC BY-SA license.

Corresponding Author:
Ricardo Yauri
Facultad de Ingeniería, Universidad Tecnológica del Perú
125 Natalio Sanchez Road, Santa Beatriz, Lima, Perú
Email: boncer99@gmail.com

1. INTRODUCTION
The growing population of older adults in Metropolitan Lima requires specialized and accessible
medical care to guarantee their well-being and quality of life. Cardiovascular diseases, such as stroke and
heart disease, are the leading cause of death globally, especially in low-resource countries, accounting for
more than 75% of global deaths [1], [2]. In this context, continuous monitoring of heart rhythms is presented
as a vital tool for the detection of possible arrhythmias [3].
Cardiovascular diseases, driven by obesity, atherosclerosis, and poor diet, represent one of the
leading causes of mortality worldwide due to their effects on metabolism and cardiovascular health [4]. In the
case of hospitals in Peru, they face significant problems due to deficiencies in internal management,
insufficient acquisition of medical supplies, lack of modern medical equipment which, combined with poor
infrastructure, results in chaotic care and untimely for seriously ill patients [5]–[7]. Furthermore, the shortage
of human resources in the MINSA (Peruvian Ministry of Health) is worsened in the poorest regions, where
the lack of personnel prevents timely care [8]. This is why the use of low-cost technologies can contribute to

Journal homepage: http://ijres.iaescore.com

484  ISSN: 2089-4864

preventing cardiovascular diseases in the elderly population of Lima, given the high prevalence of risk
factors.
In the review of the literature, studies have been found that focus on designing a low-cost and easy-
to-use electrocardiogram (ECG), focusing on capturing cardiac electrical signals, filtering noise, and
transmitting them [9], adding in some cases Bluetooth technology to facilitate the early diagnosis of cardiac
diseases [10]–[12] while other authors [13]–[15] present an approach on the design of a simulator of
electrocardiographic signals, highlighting the reduction of cost by converting analog to digital signals. On the
other hand, there have been significant advances in the monitoring and detection of cardiac arrhythmias [16],
[17] through the development of a portable ECG device [18], data transmission through artificial intelligence
while other research [19] presented a prototype based on machine learning for the detection and classification
of arrhythmias in low-cost ECG devices with MATLAB and PSoC4 [20], [21]. Finally, big data systems
allow medical decision making based on historical data to optimize medical signal monitoring processes [22],
[23], focusing, in some cases, on energy savings [24] .
Therefore, the objective of this research is to design a monitoring system based on LabVIEW for the
visualization of the heart rhythms of older adults in Metropolitan Lima, which is easy to use and adapted to
the needs of older adults. The system will provide a cardiac signal monitoring solution, through secure and
accessible information storage, facilitating analysis between health professionals. This can be complemented
with automatic notification mechanisms to alert about possible anomalies, improving timely medical care.

2. RESEARCH METHOD
The research focuses on developing an electronic device to collect the heart rhythms of people over
60 years of age and evaluate its effectiveness. A combined technology development approach is used,
involving design, implementation, and iterative testing of the device, along with practical evaluations and
pilot tests to validate its usefulness. In addition, easy-to-use features are integrated into the design of the
device, considering the needs of older people for the successful adoption of the system in their daily lives.
The implementation procedure begins with the design and development of the electronic device,
focusing on the selection of sensors, electronic circuits, and software. The descriptive diagram of operation
(Figure 1) shows the beginning of signal acquisition and subsequent treatment to eliminate noise, using a
notch filter, a band-pass filter and a final amplification stage. Subsequently, the conditioned signal will be
converted from analog to digital using an Arduino Uno module. The information is displayed through a
graphical interface created in LabVIEW, obtaining a detailed representation of the heart signal.

Figure 1. Descriptive diagram of operation

2.1. Components
For the design of the acquisition stage, essential electronic components required to assemble the
hardware stage are used, among which we have: i) instrumentation amplifier: which is built with high
impedance operational amplifiers (Op-amps) at the input using the AD620 device [25]; ii) Op-amps:
the LM741 is used as an inverting comparator, adder and filter to process the signals obtained by the
transducers [26]; iii) ECG cable with jack output: snap connectors are used to facilitate connection to the

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circuit, allowing the second derivative to be analyzed (left leg with right arm); iv) electrodes: it is used to
acquire the electrical activity generated using an silver/silver chloride (Ag/AgCl) electrode; and v) Arduino
Uno it integrates an ATmega328P microcontroller and an analog to digital conversion (DAQ) module,
conducting programming through its integrated development environment (IDE).

2.2. Electrocardiogram circuit stage design

The ECG circuit has a set of stages that range from signal transduction through the electrodes,
through the stages of amplification, filtering and DAQ using Arduino UNO, until reaching the LabVIEW
interface used to analyze the patient's signals (Figure 2). The stages are: i) transduction: it captures electrical
signals from the heart through electrodes placed on the skin; ii) circuit amplification: an AD620 Op-amps is
used to compare and adjust the captured ECG signal, to eliminate noise and maintain a good frequency
response; iii) signal filtering: low pass and high pass filters are used. Additionally, a 60 Hz notch filter is
implemented using resistors and capacitors to eliminate electrical interference. In addition, there is a reverse
amplification process that establishes a gain of 50 with the purpose of inverting the signal; and iv) data
acquisition with Arduino and LabVIEW: the analog signal is converted through transduction and filtering to
a digital signal using the ATmega328P microcontroller. Subsequently, this data is displayed on a PC for
analysis and treatment of the patient.

Figure 2. Block diagram

2.3. Printed circuit board design

The process of creating a printed circuit board (PCB) is crucial for electronic development. The
Proteus design software is used to generate the signal routing diagram to avoid interference and optimize
performance (Figure 3). This design is then translated into a manufacturing diagram, which includes details
on the physical arrangement of components on the board, location of holes, and other aspects for production
(Figure 4).

Figure 3. PCB routing diagram for ECG signal acquisition

Low-cost electrocardiogram monitoring system for elderly people using LabVIEW (Ricardo Yauri)
486  ISSN: 2089-4864

Figure 4. 3D model of the ECG signal acquisition system PCB

2.4. Acquisition algorithm

The algorithm is responsible for reading signals using two digital control pins (pin 10 and pin 11)
and one analog pin (A0). During the initialization process, serial communication is configured at a speed of
9600 baud and pins 10 and 11 are established as digital inputs. Within the main loop it is checked if either of
the two digital pins (pin 10 or pin 11) are high. The analog value from pin A0 is then read and printed to the
serial port for verification. After each read, there is a 5 milli second delay inside the loop (Figure 5).

Figure 5. Acquisition algorithm flowchart

2.5. Graphical interface in LabVIEW

In LabVIEW, is create a graphical interface by selecting a waveform chart and customizing it with a
scale from 0 to 100 on the x-axis and 0 to 4 on the y-axis. A light-emitting diode (LED) is added, a stop
button and numerical indicators are incorporated to show the beats per minute (BPM), the pulse rate and a

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serial port control (Figure 6). Furthermore, the flexibility of LabVIEW not only allows the creation of an
intuitive interface, but also facilitates the implementation of signal processing algorithms in real time.

Figure 6. LabVIEW dashboard

In LabVIEW, the node creation process is essential to effectively understand and analyze cardiac
signals. First, nodes are generated for the input of ECG and BPM data, which come from sensors connected
to the Arduino module. Signal processing nodes are used to filter and condition ECG signals, removing
unwanted noise and preparing them for analysis. These elements connect to display nodes, which represent
the ECG signals and BPM data graphically and numerically respectively (Figure 7).

Figure 7. Block diagram in LabVIEW

3. RESULTS AND DISCUSSION

3.1. Electronic board for electrocardiogram signals
This section shows the electronic components, previously described in the previous sections,
initially integrated into a test board to evaluate their operation, obtaining good signal amplification and low
noise. After finalizing the PCB design through the Proteus program, the next step was the manufacturing of
the board. A suitable Bakelite is carefully selected, and precautions taken when dipping it in acid to ensure
that the tracks match the design exactly, avoiding potential failures that could affect signal capture. Once the
PCB is ready, we proceed to solder the components in place (Figure 8).
On the electronic board, double tracks were used in both the top and bottom views, necessary due to
the considerable number of components. These tracks allow the interconnection of electronic materials and
are essential to reduce the noise generated during connections. The components are then integrated into the
PCB board, considering crucial aspects to ensure the precision and stability of the device. Specific electrodes
are used that are strategically placed on the patient's arms and right leg, optimizing signal capture, with the
device powered by two 9 V batteries (Figure 9).

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488  ISSN: 2089-4864

Figure 8. PCB for ECG acquisition Figure 9. Interconnection of the PCB to the Arduino
module and the computer

3.2. Visualization of electrocardiogram signals in LabVIEW and the Arduino IDE

The Arduino hardware module is used to display the signal captured with the serial plotter tool,
which allows generating accurate graphs of the signals clearly showing the QRS complex and the P and T
segments. It is important to mention that there is a slight noise in the signal, which can be attributed to
several factors such as grounding and noise (Figure 10), which is almost zero due to the use of filters. This
low-cost design offers an optimal tool for rapid diagnosis of diseases in older adults. The graphical interface,
created in LabVIEW, has the function of displaying the digital signal coming from the Arduino through the
serial port and includes a cardiac pulse counter that detects peaks in the signal greater than a maximum
voltage, calculating the BPM (Figure 11). Every 15 seconds and using a buzzer that sounds with each pulse.
At the top of the LabVIEW interface, there is a stop button to stop the process.

Figure 10. Visualization of the ECG signal in the Arduino IDE Figure 11. LabVIEW interface for signal
visualization

Using the captured signals, the results of Table 1 are obtained, which presents evaluation results for
some patients. In the normal context, the human heart usually beats between 60 and 100 times per minute,
however, in individuals who exercise regularly or take medications to reduce heart rate, the heart rate may be
less than 60 BPM. By analyzing the cardiac characteristics of these patients, it is observed that the data
obtained are within the expected ranges, thus validating the optimal performance.

Table 1. Acquired data

Patient Sex Age Heart rate (BPM) Medical conditions
A M 62 60 NO
B M 25 75 NO
C F 61 67.5 NO
D M 66 67.5 NO

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4. CONCLUSION
This research has culminated in the development of a portable ECG device using analog filters and
an intuitive graphical interface in LabVIEW, significantly reducing noise. Optimization of ECG frequencies
and effective communication between components have improved the accuracy and clarity of the signals
obtained. The correct functioning of the system and use of low-cost components underlines the economic
viability of the technology used. In relation to noise and interference reduction, the use of Op-amps has
proven to be crucial to effectively reduce noise and interference in ECG measurements. Additionally, the
application of analog filters has allowed the ECG frequencies to be limited to a specific range, effectively
eliminating unwanted noise. In the case of the intuitive graphical interface, the use of LabVIEW has provided
an intuitive visual platform for the analysis of cardiac signals. In addition to offering a clear representation of
the data, it has also facilitated the subsequent analysis of the results, improving the monitoring capacity of
medical personnel, related to the portability of the device. In addition, the interface in LabVIEW is used as a
tool that provides the diagnostic capacity of medical personnel in clinical environments, homes, and
ambulances.
The feasibility of a portable and accessible ECG device that uses electronic technologies for the
detection and visualization of cardiac signals has been demonstrated. Despite significant achievements, future
research could focus on integrating artificial intelligence for more accurate diagnoses, implementing wireless
technologies for remote monitoring, and including early warnings. Additionally, may include customization
of the system to adapt to various medical conditions, and the inclusion of telemedicine.

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BIOGRAPHIES OF AUTHORS

Ricardo Yauri is a master of science in electronic engineering with a mention in

biomedical. He is associate professor at the Universidad Nacional Mayor de San Marcos
(UNMSM) and Ph.D. student in systems engineering. He is professor at Universidad
Tecnológica del Perú and Universidad Peruana del Norte. He has participated as a teacher in
courses oriented to the internet of things and applications in home automation and the Cisco
Academy for IoT. He was researcher at INICTEL-UNI in the embedded systems and internet
of things research group. He developed research projects on the implementation of low
consumption IoT devices that involve inference techniques, machine learning algorithms, and
computational intelligence. He can be contacted at email: C24068@utp.edu.pe,
ryaurir@unmsm.edu.pe, or boncer99@gmail.com.

Max Delgado is a student of electronic engineering at the Universidad Nacional

Mayor de San Marcos, currently in the final phase of his studies. He has an enthusiastic
interest in computer hardware and technology. Throughout his academic career, he has
maintained an outstanding academic record and has actively participated in research projects
focused on integrated systems and hardware for the acquisition of biological signals. His
professional focus is on bioelectrical signal processing methods and data visualization through
healthcare software systems. He can be contacted at email: max.delgado1@unmsm.edu.pe.

Enzo Flores is in the final stage of his degree in electronic engineering at the
Universidad Nacional Mayor de San Marcos and has shown a deep interest in hardware
technology and computers from an early age. His focus is on signal acquisition and processing,
especially in the field of bio signals, with a particular emphasis on remote monitoring
applications. Additionally, he has been actively involved in hardware development,
significantly contributing to the creation of various electronic devices. He can be contacted at
email: enzo.flores@unmsm.edu.pe.

Oscar Llerena is master in telecommunications engineering. Work experience in

mobile telephone networks, transport networks based on microwave systems, and underwater
fiber optic networks. Experience in scientific research on topics related to the design of passive
microwave devices and communications systems based on software defined radio devices.
Knowledge of advanced English and advanced Portuguese. Basic-intermediate knowledge of
programming languages such as python, C++, Arduino. He can be contacted at email:
oscar.llerena.c@gmail.com.

Int J Reconfigurable & Embedded Syst, Vol. 13, No. 2, July 2024: 483-490