Advances in Computing 13th Colombian Conference CCC 2018 Cartagena Colombia September 26 28 2018 Proceedings Jairo E. Serrano C. Download PDF
Advances in Computing 13th Colombian Conference CCC 2018 Cartagena Colombia September 26 28 2018 Proceedings Jairo E. Serrano C. Download PDF
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Advances
in Computing
13th Colombian Conference, CCC 2018
Cartagena, Colombia, September 26–28, 2018
Proceedings
123
Communications
in Computer and Information Science 885
Commenced Publication in 2007
Founding and Former Series Editors:
Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu,
Dominik Ślęzak, and Xiaokang Yang
Editorial Board
Simone Diniz Junqueira Barbosa
Pontifical Catholic University of Rio de Janeiro (PUC-Rio),
Rio de Janeiro, Brazil
Joaquim Filipe
Polytechnic Institute of Setúbal, Setúbal, Portugal
Igor Kotenko
St. Petersburg Institute for Informatics and Automation of the Russian
Academy of Sciences, St. Petersburg, Russia
Krishna M. Sivalingam
Indian Institute of Technology Madras, Chennai, India
Takashi Washio
Osaka University, Osaka, Japan
Junsong Yuan
University at Buffalo, The State University of New York, Buffalo, USA
Lizhu Zhou
Tsinghua University, Beijing, China
More information about this series at http://www.springer.com/series/7899
Jairo E. Serrano C.
Juan Carlos Martínez-Santos (Eds.)
Advances
in Computing
13th Colombian Conference, CCC 2018
Cartagena, Colombia, September 26–28, 2018
Proceedings
123
Editors
Jairo E. Serrano C. Juan Carlos Martínez-Santos
Universidad Tecnológica de Bolívar Universidad Tecnológica de Bolívar
Cartagena Cartagena
Colombia Colombia
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Conference Chairs
Juan Carlos Universidad Tecnológica de Bolívar, Colombia
Martínez-Santos
Jairo Serrano Castañeda Universidad Tecnológica de Bolívar, Colombia
Program Committee
Mauricio Alba Universidad Autónoma de Manizales, Colombia
Luis Fernando Castro Universidad del Quindio, Colombia
César Collazos Universidad del Cauca, Colombia
Toni Granollers Universidad de Lleida, Spain
Leonardo Flórez Pontificia Universidad Javeriana de Bogotá, Colombia
María Patricia Trujillo Universidad del Valle, Colombia
Nestor Duque Universidad Nacional de Colombia, Colombia
Iván Cabezas Universidad de San Buenaventura, Colombia
Carlos Hernán Gómez Universidad de Caldas, Colombia
Harold Castro Universidad de los Andes, Colombia
Enrique González
María Clara Gómez
Yenny Alexandra Méndez Alegría
Iván M. Cabezas T.
Juan Carlos Martinez
Andrés Solano
Jorge Iván Ríos Patiño
Abstract. The analysis of physiological signals is widely used for the devel-
opment of diagnosis support tools in medicine, and it is currently an open
research field. The use of multiple signals or physiological measures as a whole
has been carried out using data fusion techniques commonly known as multi-
modal fusion, which has demonstrated its ability to improve the accuracy of
diagnostic care systems. This paper presents a review of state of the art, putting
in relief the main techniques, challenges, gaps, advantages, disadvantages, and
practical considerations of data fusion applied to the analysis of physiological
signals oriented to diagnosis decision support. Also, physiological signals data
fusion architecture oriented to diagnosis is proposed.
1 Introduction
Physiological signals deliver relevant information on the status of the human being,
which helps the doctor to give a diagnosis for specifics pathologies, and therefore
provide appropriate treatment. However, in many cases, these tasks become more
complicated since patients can present several pathologies that must be managed
simultaneously. Additionally, physiological parameters change frequently, requiring a
rapid analysis, and high-risk decisions [1] that result from the interpretation of the
human expert that analyses the available clinical evidence.
Recently, studies the analysis of multimodal signals, for diagnostic support using
multimodal has increased [2, 3] in data fusion. This last covers the analysis of different
sources and types of data. Its aims is to provide information with less uncertainty [4]
and potentially allows ubiquitous and continuous monitoring of physiological param-
eters [5] and reduce adverse effects of the signals due to sensor movements, irregular
sampling, bad connections and signal noise [6–10]. Data fusion can include different
processes such as association, correlation, combine data, and information achieved
from one or multiple sources to identify objects, situations, and threats [11].
This paper presents a literature review of the data fusion oriented to clinical
diagnosis discussing and identifying their most common techniques, properties, and
highlighting advantages, disadvantages, challenges, lacks, and gaps. This review was
carried out from Scopus and Web of Sciences database, based on these search criteria:
(i) (physiological signals) and (diagnosis decision support); and (ii) ((“data fusion”) or
(“information fusion”) or (“multimodal”) and (diagnosis or diagnostic)) and (“physi-
ological signals”). The selected papers were reported between years 2013 and 2018 in
journals of quartile 1 and quartile 2 principally. Also, a data fusion framework oriented
to clinical diagnostic was proposed for physiological signals processing based on the
Joint Directors of Laboratories (JDL) model. The rest of the document is organized as
follows: in section two, a description of the physiological signals is presented. In
section three, we describe the most common multi-modal fusion models, spotlighting
data processing, and fusion techniques; Section four contains the proposed architecture;
and finally, the conclusions and future work are presented.
intracranial pressure (ICP), body move (BM), systolic volume (SV); (v) bioimpedance
signals: correspond to electrodermal activity e.g. skin conductivity (SC) or galvanic skin
response (GSR); (vi) biochemical signals: These are based on chemical components
measures e.g. blood glucose (BG).
ECG is widely used to understand and investigate cardiac health condition [2, 26,
27]. EOG is related to the eye movement which is derived from Cornea-Retinal
Potential [28, 29]. EMG is acquired using electrodes through a muscle fiber skin to
observe the muscle activity. It is also associated with the neural signals, sent from the
spinal cord to muscles [30, 31]. EEG signals indicate any nervous excitement by
detecting brain activities derived from neurons in the brain that communicate through
electrical impulses [15, 32, 33]. ECoG records are an electrical activity of the brain by
means of invasive electrodes [23, 34]. Obtaining information from bioelectric signals
becomes extremely difficult due to limited data and presence of noise which signifi-
cantly affects the ability to detect weak sources of interest [26, 35].
PCG acquisition is plain, non-invasive, low-cost and precise for assessing a wide
range of heart disease (e.g. cardiac murmurs) [19, 36]. However, they are altered by
external acoustic sources (such as speech, environmental noise, etc.) and physiological
interference (such as lung sounds, cough, etc.) [37]. Respiratory rate (RR) [18], can be
altered by noise and movement artifacts [38]. PPG signal consists of direct current
(DC) and alternating current (AC) components. The AC component represents the
changes in arterial blood volume between the systolic and diastolic phases of a cardiac
cycle. The DC component corresponds to the detected light intensity from tissues,
venous blood, and non-pulsatile components of arterial blood, an example of trans-
mission type is a fingertip pulse oximeter (Spo2), which is clinically accepted and
widely used. Clinical applications of PPG sensors are limited by their low signal to
noise ratio (SNR), which is caused by the large volume of skin, muscle, and fat and
relatively small pulsatile component of arterial blood [17, 39].
BP is defined by systolic and diastolic pressure, and it is measured in millimeters of
mercury (mmHg), but main forms of noninvasive blood pressure measurement are
divided into intermittent and continuous blood pressure measurements [40, 41], con-
secutively affecting the calculated measure of systolic volume (SV), ICP is the pressure
within skull [42]; BM capture body movements [22, 43]; SC is the electrodermal
activity, indicator of sympathetic activation and a useful tool for investigating
4 Y. F. Uribe et al.
psychological and physiological arousal [44, 45]; BG indicates the amount of energy in
the body [43, 46]. Finally, the temperature measurement (Temp) is a measure of the
ability of the body or skin to generate and release heat [3, 43]. These signals can be
easily altered by movement and body mass, environmental noise, intermittent con-
nections, etc. In Table 1 is shown a summarize of some applications of physiological
signals for monomodal clinical support systems.
3 Signal Fusion
Multiple information about the same phenomenon can be acquired from different types
of detectors or sensors, under different conditions, in multiple experiments or subjects.
Particularly multimodal fusion refers to the combination of various signals of multiple
modalities to improve the performance of the systems decreasing the uncertain of their
results. Each modality contributes a type of added value that cannot be deduced or
obtained from only type of physiological signals [51, 52].
There are several techniques of multimodal fusion reported in the literature, like the
sum and the product, which have been used for data fusion, and consecutively these
operators have evolved into more advanced ones, particularly through the results of
Physiological Signals Fusion Oriented to Diagnosis - A Review 5
soft-computing and fuzzy operator research (Fig. 2) [53] which are widely discussed in
[54] as follows: (i) Fusion of imperfect data are approaches capable of representing
specific aspects of imperfect data (Probabilistic fusion, Evidential belief reasoning,
fusion based on Random set theoretic fusion, Fusion and fuzzy reasoning, Possibilistic
fusion, Rough set based fusion, Hybrid fusion approaches (the main idea behind
development of hybrid fusion algorithms is that different fusion methods complement
each other to give a more precise approach); (ii) Fusion of correlated data provide
either independence or prior knowledge of the cross covariance of data to produce
consistent results; (iii) Fusion of inconsistent data is the notion of data inconsistency
(Spurious data, Out of sequence data, Conflicting data), and (iv) fusion of disparate
data is the input data to a fusion system, which is generated by a wide variety of
sensors, humans, or even stored sensory data [54]. However, categorizations most used
are described in [11, 52, 55–57]; which consists of three types of fusion: (i) early: the
characteristics obtained from different modalities are combined into a single repre-
sentation before feeding the learning phase, it is known as feature fusion, and its major
advantage is the detection of correlated features generated by different sensor signals so
to identify a feature subset that improves recognition accuracy; In addition, the main
drawback is to find the most significant feature subset, large training sets are typically
required [11, 50, 58]; (ii) intermediate: it can cope with the imperfect data, along with
the problems of reliability and asynchrony between different modalities, and (iii) late
[59]: it is known as fusion level decision each modality is processed separately by a
first recognizer, and another model is trained on the unimodal predictions to predict the
actual single modal gold standard [33], main decision-level fusion advantages include
communication bandwidth savings and improved decision accuracy. Another important
aspect of decision fusion is the combination of the heterogeneous sensors whose
measurement domains have been processed with different algorithms [11, 50, 58, 60].
In general, the main problem of multimodal data processing is that the data must be
processed separately and must be combined only at the end, the dimensionality of joint
feature space, different feature formats, and time-alignment. The information theory
provides with a set of information measures that not only assess the amount of
information that one single source of data contains, but also the amount of information
that two sources of data have in common [52, 61].
In Table 3 is shown multiple studies of fusion of several physiological signals
alongside the techniques applied for specific clinical diagnostic decision support with
their respective accuracy (Acc). We highlighted the applications in emotion recogni-
tion, monitoring and reduce the false alarms hart diagnosis, and the applicability of
ECG signals for fusing with other signals for several diagnostics.
Table 3. (continued)
Ref Fused signals Techniques Diagnostic
[67] GSR, attitude of the head, Reference model (CSALP),
eyes and facial expressions valence-arousal method,
boosting algorithm, model
(ASM), Haar-like features,
flow-based algorithm, POSIT
algorithms, RANSAC
regression, entropy, SVM-
based method, Support vector
machine (SVM), filters and
multimodal fusion
[52] EEG, GSR, EMG and EOG Discrete wavelet transform Predict emotions
Acc: 85%
[5] ECG and SpO2 Stochastic Petri net (SPN) and Improve
Wearable health monitoring monitoring and
system (WHMS) reduce the false
[8] ECG, PA, SV, PPG and Robust algorithm alarms
EEG
Acc: 89.63%
[2] ECG Beat-by-beat algorithm, Location of the
Function ‘gqrs’ of the WFDB heart beat
toolbox, Open-source
algorithm, ‘wabp’ of the
WFDB Toolbox and candidate
detections ratio (CDR)
[68] EEG and EOG Approximate entropy (ApEn), Drowsiness
Acc: 97.3% Sample entropy (SampEn),
Renyientropy (RenEn),
Recurrence quantification
analysis (RQA), Extreme
learning machine (ELM) and
wavelet-based nonlinear
features
[69] Change eye gaze direction SLD (Standard Lateral
and duration of flicker Deviation), D-S, decision
Acc: 70% fusion
[43] BP, ECG, EEG, EMG, Preprocessing, puts filter, self- Heart rate
Spo2, FC, Temp and BG adaptive, data compression variability [70]
(CR and PRD), Gateway data
fusion, fuzzy logic, artificial
neural networks, support
vector machines and
classification (specificity and
sensitivity)
[71] ECG and PCG Wavelet transform, discrete
Acc: 97% wavelet transform STFT, band
pass filter and decision fusion
(continued)
8 Y. F. Uribe et al.
Table 3. (continued)
Ref Fused signals Techniques Diagnostic
[60] BP, ECG and FC The Processing Elements Hypotension and
Acc: 99.7% (PEs) and decision-level fusion hypertension [40]
[72] ECG and accelerometer Hamilton-Tompkins algorithm, Congestive heart
Acc: 99% bandpass filter, wavelet failure and sleep
transform and data fusion apnea and asthma
algorithm
[73] ECoG Criterion of Neyman-Pearson, Epilepsy
preprocessing, fusion channels
unification and voting, ROC
curve and area under the curve
(AUC)
[7] BP and ECG Kalman Filter (KF), fusion Left ventricular
Acc: 99.4% technique Townsend and hypertrophy [74]
Tarassenko and signal quality
index (SQI)
[1] ECG, BP and PCA (principal component Arrhythmias
PPG analysis), Kalman filter, LSP
(Lomb - Scargleperiodogram)
and data fusion covariance
[6] BP, ECG and RR DWT (Discrete Wavelet
Acc: 94.15% transform) and decision fusion
[75] ECG, GSR, rotation of the FFT, fusion based on Bayesian Fatigue and stress
head, movement of the eyes network data, pre-filter
and yawn Butterworth fission and
Gaussian filter
[76] Essential tremor (ET), EMD (Empirical mode Tremor
Parkinson’s disease (PD), decomposition), DWT
physiological tremor (discrete wavelet transform),
(PT) and EMG D S (Dempster-Shafer), BPNN
Acc: 99.6% (back-propagation neural
network) and decision fusion
[42] ICP The median and the tendency Hydrocephalus
of the waveform, FIR (low
pass filter), evidence fusion
and global fusion
[77] FC Fuzzy logic, Neural networks, Hypovolemia
Bayesian probability and belief
network
[55] BP, ECG and EEG Signal quality index (SQI), Alterations in
Acc: 86.26% Estimation of regular intervals, cardiac
Heartbeats detection, autonomic control
adaptative filter, Multimodal peripheral [78]
fusion and QRS detection
Physiological Signals Fusion Oriented to Diagnosis - A Review 9
4 Proposed Model
Different architectures and methodologies of data fusion have been reported in [11, 60,
79, 80], based on the Joint Directors of Laboratories (JDL) model which focus on the
abstraction level of the manipulated data by a fusion system. We proposed a general
framework for processing and fusion of multimodal physiological signals oriented to
diagnostic support systems. The architecture consists of four levels (Fig. 3), where the
level 0 has for purpose make the acquisition of different physiological signals and
realize the pre-processing, which consists of the stage of filtration, feature extraction,
and normalization; Level 1, is composed by a spatial-temporal alignment and data
correlation, the latter checks the proportionality of the information, i.e., if the infor-
mation is not consistent will be feedback to the preprocessing stage, otherwise the
process continues. Subsequently, the association of information executes a classifica-
tion with multiple hypothesis tests, which tracks multiple targets in dense environments
with the help of Bayesian networks or similar techniques, providing labels to each
signal obtained from the sensors, but when the objective position is doubtful, data
estimation is performed with the maximum posterior method that is based on Bayesian
theory, and is used when the X parameter to be estimated is the output of a random
Sensor 1 S1
Pre - processing
Level 0
AassociaƟon (MHT)
NO
and esƟmaƟon (MAP) S1
S1 S2 Sn
Algorithm to eliminate
False alarms
false alarms
Level 1
Pathology 1
Pathology n
Treatment 1
Level 2
ValuaƟon, risk
Treatment 2
and impact
Treatment n
Level 3
variable with a known Pr P(X) function, consecutively the system performs an analysis
verifying the status of the labels, if at any moment a different label to those assigned to
the physiological parameters is identified as false alarm, it is eliminated by means of the
algorithm; afterwards, sets of characteristics obtained are fused to form vectors of
significant features. Consequently, level 2 has the function to determine the possible
pathologies presented by the patient through learning machines; finally level 3 includes
the decision level, which will determine the best hypothesis for the pathology, pro-
viding a clinical diagnosis and a possible treatment, besides this determines the
assessment, risk, and impact of the process based on forecast system. All stages allow
including hard and soft data, context information, together medical criteria and a
mapping system based on performance quality metrics that allow optimizing the
processing.
The proposed model was developed to diminish the high rate of false alarms in
services of constant monitoring, supply a timely diagnosis and a possible treatment to
the pathology of the patient, providing support the specialist.
5 Conclusion
In this work were discussed multiple physiological signals alongside multimodal data
fusion systems applied in clinical diagnosis support systems, highlighting advantages,
disadvantages, shortcomings, and challenges. It has highlighted the capability of
multimodal data fusion systems because of allowing obtaining more reliable and robust
psychological or physiological information using multiple sources respect to unimodal
systems, revealing an increase in the accuracy of diagnoses, and demonstrating com-
plementarity of modalities. Additionally, multimodal data fusion yields important
insights processes and structures, spatiotemporal resolution complementarity, including
a comprehensive physiological view, structures, quantification, generalization and
normalization [81]. Nevertheless, accurate synchronization of multimodal data streams
is critical to avoid parameter skews for analysis.
For some diagnosis, the results can be considered low. Therefore, studies in this
field must follow. We consider that other signals can be included in the data fusion
systems and complement it with information quality evaluation systems as the pro-
posed in [82]. In addition, we proposed a physiological signal fusion architecture,
based on the JDL model; in order to provide a more reliable diagnosis and treatment
based on evidence, all of the above to support the specialist in their decisions; The
interface for the model will present continuous monitoring, without alterations with
minimum response times, and easy to use.
Finally, to develop more effective clinical decision support mechanisms, an
architecture was proposed, which covers all levels of development of diagnostic of the
assistance systems in the field health taking into account the gaps found in the literature
such as lack traceability of the systems from acquisition until results, visualizations,
and treatments. Besides, other problems such as signals that cannot be directly merged
and must be done separately, the low availability of data in the time, the high com-
putational cost of complex models, and limitations about the assessment of situation
and risk.
Physiological Signals Fusion Oriented to Diagnosis - A Review 11
References
1. Clifford, G.D., Long, W.J., Moody, G.B., Szolovits, P.: Robust parameter extraction for
decision support using multimodal intensive care data. Philos. Trans. A. Math. Phys. Eng.
Sci. 367(1887), 411–429 (2009)
2. Mollakazemi, M.J., Atyabi, S.A., Ghaffari, A.: Heart beat detection using a multimodal data
coupling method. Physiol. Meas. 36(8), 1729–1742 (2015)
3. Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect
recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42–55 (2012)
4. Begum, S., Barua, S., Filla, R., Ahmed, M.U.: Classification of physiological signals for
wheel loader operators using multi-scale entropy analysis and case-based reasoning. Expert
Syst. Appl. 41(2), 295–305 (2014)
5. Pantelopoulos, A., Bourbakis, N.: SPN-model based simulation of a wearable health
monitoring system. In: Proceedings of the 31st Annual International Conference of the IEEE
Engineering in Medicine and Biology Society Engineering the Future of Biomedicine,
EMBC 2009, pp. 320–323 (2009)
6. Ryoo, H.C., Sun, H.H., Hrebien, L.: Two compartment fusion system designed for
physiological state monitoring. In: Annual Reports Res. React. Inst., pp. 2224–2227 (2001)
7. Li, Q., Mark, R.G., Clifford, G.D.: Artificial arterial blood pressure artifact models and an
evaluation of a robust blood pressure and heart rate estimator. Biomed. Eng. Online 15, 1–15
(2009)
8. Galeotti, L., Scully, C.G., Vicente, J., Johannesen, L., Strauss, D.G.: Robust algorithm to
locate heart beats from multiple physiological waveforms by individual signal detector
voting. Physiol. Meas. 36(8), 1705–1716 (2015)
9. Tsiliki, G., Kossida, S.: Fusion methodologies for biomedical data. J. Proteomics 74(12),
2774–2785 (2011)
10. Setz, C., Schumm, J., Lorenz, C., Arnrich, B., Tröster, G.: Using ensemble classifier systems
for handling missing data in emotion recognition from physiology: one step towards a
practical system. In: Affective Computing and Intelligent Interaction (ACII 2009), pp. 1–8
(2009)
11. Castanedo, F.: A review of data fusion techniques. Sci. World J. 2013, 704504 (2013)
12. Patil, R.: Digital signal preservation approaches of archived biomedical paper records - a
review. In: 5th International Conference on Wireless Networks and Embedded Systems,
WECON 2016, pp. 13–16 (2016)
13. Liu, T., Si, Y., Wen, D., Zang, M., Lang, L.: Dictionary learning for VQ feature extraction in
ECG beats classification. Expert Syst. Appl. 53, 129–137 (2016)
14. Alvarez-Estevez, D., Moret-Bonillo, V.: Spectral heart rate variability analysis using the
heart timing signal for the screening of the sleep apnea–hypopnea syndrome. Comput. Biol.
Med. 71, 14–23 (2016)
15. Liu, Q., Chen, Y.F., Fan, S.Z., Abbod, M.F., Shieh, J.S.: A comparison of five different
algorithms for EEG signal analysis in artifacts rejection for monitoring depth of anesthesia.
Biomed. Sig. Process. Control 25, 24–34 (2016)
16. Mack, D.J., Schönle, P.: An EOG-based, head-mounted eye tracker with 1 kHz sampling
rate. In: IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds
and Able Bodies, BioCAS, pp. 7–10 (2015)
17. Khan, M., et al.: Analysing the effects of cold, normal, and warm digits on transmittance
pulse oximetry. Biomed. Sig. Process. Control 26, 34–41 (2016)
18. Janik, P., Janik, M.A., Wróbel, Z.: Integrated micro power frequency breath detector. Sens.
Actuators A Phys. 239, 79–89 (2016)
12 Y. F. Uribe et al.
19. Essentials, F., Taylor, A.J.: Learning Cardiac Auscultation. Springer, London (2015). https://
doi.org/10.1007/978-1-4471-6738-9
20. Francisco, J., et al.: Changes in the severity of aortic regurgitation at peak effort during
exercise ☆. Int. J. Cardiol. 228, 145–148 (2017)
21. Chuiko, G.P., Dvornik, O.V., Shyian, S.I., Baganov, Y.A.: A new age-related model for
blood stroke volume. Comput. Biol. Med. 79(Oct), 144–148 (2016)
22. Lorenzi, P., Rao, R., Romano, G., Kita, A., Irrera, F.: Mobile devices for the real-time
detection of specific human motion disorders. IEEE Sens. J. 16(23), 8220–8227 (2016)
23. Takaura, K., Tsuchiya, N., Fujii, N.: Frequency-dependent spatiotemporal profiles of visual
responses recorded with subdural ECoG electrodes in awake monkeys: differences between
high- and low-frequency activity. NeuroImage 124, 557–572 (2016)
24. Antelis, J.M., Gudi, B., Eduardo, L., Sanchez-ante, G., Sossa, H.: Dendrite morphological
neural networks for motor task recognition from electroencephalographic signals. Biomed.
Sig. Process. Control 44, 12–24 (2018)
25. Becerra, M.A., Alvarez-Uribe, K.C., Peluffo-Ordoñez, D.H.: Low data fusion framework
oriented to information quality for BCI systems. In: Rojas, I., Ortuño, F. (eds.) IWBBIO
2018. LNCS, vol. 10814, pp. 289–300. Springer, Cham (2018). https://doi.org/10.1007/978-
3-319-78759-6_27
26. Kaur, H., Rajni, R.: On the detection of cardiac arrhythmia with principal. Wirel. Pers.
Commun. 97(4), 5495–5509 (2017)
27. Rajesh, K.N.V.P.S., Dhuli, R.: Biomedical signal processing and control classification of
imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier.
Biomed. Sig. Process. Control 41, 242–254 (2018)
28. Mulam, H.: Optimized feature mapping for eye movement recognition using electroocu-
logram signals. In: 8th International Conference on Computing, Communication and
Networking Technologies, ICCCNT 2017 (2017)
29. Lv, Z., Zhang, C., Zhou, B., Gao, X., Wu, X.: Design and implementation of an eye gesture
perception system based on electrooculography. Expert Syst. Appl. 91, 310–321 (2018)
30. Young, A.J., Kuiken, T.A., Hargrove, L.J.: Analysis of using EMG and mechanical sensors
to enhance intent recognition in powered lower limb prostheses. J. Neural Eng. 11(5), 56021
(2014)
31. Kaur, A., Agarwal, R., Kumar, A.: Adaptive threshold method for peak detection of surface
electromyography signal from around shoulder muscles. J. Appl. Stat. 4763, 714–726 (2018)
32. Khurana, V., Kumar, P., Saini, R., Roy, P.P.: ScienceDirect EEG based word familiarity
using features and frequency bands combination Action editor: Ning Zhong. Cogn. Syst.
Res. 49, 33–48 (2018)
33. Koelstra, S.: Deap: a database for emotion analysis; using physiological signals. IEEE Trans.
Affect. Comput. 3(1), 18–31 (2012)
34. Degenhart, A.D., Hiremath, S.V., Yang, Y.: Remapping cortical modulation for electrocor-
ticographic brain–computer interfaces: a somatotopy-based approach in individuals with
upper-limb paralysis. J. Neural Eng. 15(2), 026021 (2018)
35. Ravan, M.: Beamspace fast fully adaptive brain source localization for limited data
sequences. Inverse Probl. 33(5), 055021 (2017)
36. Alonso-ar, M.A., Ibarra-hern, R.F., Cruz-guti, A., Licona-ch, A.L., Villarreal-reyes, S.:
Design and evaluation of a parametric model for cardiac sounds. Comput. Biol. Med. 89
(Aug), 170–180 (2017)
37. Babu, K.A., Ramkumar, B., Manikandan, M.S.: Real-time detection of S2 sound using
simultaneous recording of PCG and PPG. In: IEEE Region 10 Annual International
Conference, pp. 1475–1480 (2017)
Physiological Signals Fusion Oriented to Diagnosis - A Review 13
38. Prabha, A., Trivedi, A., Kumar, A.A., Kumar, C.S.: Automated system for obstructive sleep
apnea detection using heart rate variability and respiratory rate variability. In: International
Conference on Advances in Computing, pp. 1303–1307 (2017)
39. Lee, H., Chung, H., Ko, H., Lee, J.: Wearable multichannel photoplethysmography
framework for heart rate monitoring during intensive exercise. IEEE Sens. J. 18(7), 2983–
2993 (2018)
40. Oliveira, C.C., Machado Da Silva, J.: A fuzzy logic approach for highly dependable medical
wearable systems. In: Proceedings of the 2015 IEEE 20th International Mixed-Signal
Testing Workshop, IMSTW 2015 (2015)
41. Li, J., et al.: Design of a continuous blood pressure measurement system based on pulse
wave and ECG signals. IEEE J. Transl. Eng. Heal. Med. 6(Jan), 1–14 (2018)
42. Conte, R., Longo, M., Marano, S., Matta, V., Elettrica, I., Dea, A.: Fusing evidences from
intracranial pressure data using dempster-shafer theory. In: 15th International Conference on
Digital Signal Processing, pp. 159–162 (2007)
43. Al-Saud, K., Mahmuddin, M., Mohamed, A.: Wireless body area sensor networks signal
processing and communication framework: survey on sensing, communication technologies,
delivery and feedback. J. Comput. Sci. 8(1), 121–132 (2012)
44. Torniainen, J., Cowley, B., Henelius, A., Lukander, K., Pakarinen, S.: Feasibility of an
electrodermal activity ring prototype as a research tool. In: IEEE Engineering in Medicine
and Biology Society, EMBS, pp. 6433–6436 (2015)
45. Muller, J., et al.: Repeatability of measurements of galvanic skin response – a pilot study.
Open Complement. Med. J. 5(1), 11–17 (2013)
46. Wang, Y.-Z., et al.: Nonenzymatic electrochemiluminescence glucose sensor based on
quenching effect on luminol using attapulgite–TiO2. Sens. Actuators B Chem. 230, 449–455
(2016)
47. Belgacem, N., Fournier, R., Nait-Ali, A., Bereksi-Reguig, F.: A novel biometric
authentication approach using ECG and EMG signals. J. Med. Eng. Technol. 39(4), 226–
238 (2015)
48. Kume, D., Akahoshi, S., Yamagata, T., Wakimoto, T., Nagao, N.: Does voluntary
hypoventilation during exercise impact EMG activity? SpringerPlus 5(1), 149 (2016)
49. Stuart, S., Galna, B., Lord, S., Rochester, L.: A protocol to examine vision and gait in
Parkinson’s disease: impact of cognition and response to visual cues [version 2; referees: 2
approved] Referee Status, pp. 1–18 (2016)
50. Abdat, F., Maaoui, C., Pruski, A.: Bimodal system for emotion recognition from facial
expressions and physiological signals using feature-level fusion. In: Symposium on
Computer Modeling and Simulation, pp. 24–29 (2011)
51. Zapata, J.C., Duque, C.M., Rojas-Idarraga, Y., Gonzalez, M.E., Guzmán, J.A., Becerra
Botero, M.A.: Data fusion applied to biometric identification – a review. In: Solano, A.,
Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 721–733. Springer, Cham (2017). https://
doi.org/10.1007/978-3-319-66562-7_51
52. Verma, G.K., Tiwary, U.S.: Multimodal fusion framework: a multiresolution approach for
emotion classification and recognition from physiological signals. NeuroImage 102(P1),
162–172 (2014)
53. Soria-Frisch, A., Riera, A., Dunne, S.: Fusion operators for multi-modal biometric
authentication based on physiological signals. In: IEEE International Conference on Fuzzy
Syst, FUZZ 2010, pp. 18–23 (2010)
54. Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of
the state of the art. Inf. Fusion 14(1), 28–44 (2013)
14 Y. F. Uribe et al.
55. Jeon, T., Yu, J., Pedrycz, W., Jeon, M., Lee, B., Lee, B.: Robust detection of heartbeats
using association models from blood pressure and EEG signals. Biomed. Eng. Online 15, 1–
14 (2016)
56. Lahat, D., Adali, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges,
and prospects. Proc. IEEE 103(9), 1449–1477 (2015)
57. Van Gerven, M.A.J., Taal, B.G., Lucas, P.J.F.: Dynamic Bayesian networks as prognostic
models for clinical patient management. J. Biomed. Inform. 41, 515–529 (2008)
58. Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor
networks: state-of-the-art and research challenges. Inf. Fusion 35, 68–80 (2017)
59. Ringeval, F., et al.: Prediction of asynchronous dimensional emotion ratings from
audiovisual and physiological data. Pattern Recognit. Lett. 66, 22–30 (2015)
60. Alemzadeh, H., Saleheen, M.U., Jin, Z., Kalbarczyk, Z., Iyer, R.K.: RMED: a reconfigurable
architecture for embedded medical monitoring. In: 2011 IEEE/NIH Life Science Systems
and Applications Workshop, pp. 112–115 (2011)
61. Magalhães, J., Rüger, S.: Information theoretic semantic multimedia indexing. In:
Proceedings of the 6th ACM International Conference on Image and Video Retrieval,
pp. 619–626 (2007)
62. Sivanathan, A., Lim, T., Louchart, S., Ritchie, J.: Temporal multimodal data synchronisation
for the analysis of a game driving task using EEG. Entertain. Comput. 5(4), 323–334 (2014)
63. Ruiz, M.D., Gómez-Romero, J., Molina-Solana, M., Ros, M., Martin-Bautista, M.J.:
Information fusion from multiple databases using meta-association rules. Int. J. Approx.
Reason. 80, 185–198 (2017)
64. Nemati, S., Malhotra, A., Clifford, G.D.: Data fusion for improved respiration rate
estimation. EURASIP J. Adv. Sig. Process. 2010, 926305 (2010)
65. Zong, C.Z.C., Chetouani, M.: Hilbert-Huang transform based physiological signals analysis
for emotion recognition. In: 2009 IEEE International Symposium on Signal Processing and
Information Technology (ISSPIT), pp. 334–339 (2009)
66. Martínez, H., Yannakakis, G.: Mining multimodal sequential patterns: a case study on affect
detection. In: International Conference on Multimodal, pp. 3–10 (2011)
67. Chen, J., Luo, N., Liu, Y., Liu, L., Zhang, K., Kolodziej, J.: A hybrid intelligence-aided
approach to affect-sensitive e-learning. Computing 98(1–2), 215–233 (2016)
68. Chen, L., Zhao, Y., Zhang, J., Zou, J.: Automatic detection of alertness/drowsiness from
physiological signals using wavelet-based nonlinear features and machine learning. Expert
Syst. Appl. 42(21), 7344–7355 (2015)
69. Su, H., Zheng, G.: A non-intrusive drowsiness related accident prediction model based on D-
S evidence theory. In: 1st International Conference on Bioinformatics and Biomedical
Engineering, ICBBE, pp. 570–573 (2007)
70. Cosoli, G., Casacanditella, L., Tomasini, E., Scalise, L.: Evaluation of heart rate variability
by means of laser doppler vibrometry measurements. J. Phys. Conf. Ser. 658, 12002 (2015)
71. Fatemian, S.Z., Agrafioti, F., Hatzinakos, D.: HeartID: cardiac biometric recognition. In:
IEEE 4th International Conference Biometrics Theory, Applications and Systems, BTAS
2010, pp. 1–5 (2010)
72. Pantelopoulos, A., Saldivar, E., Roham, M.: A wireless modular multi-modal multi-node
patch platform for robust biosignal monitoring. In: Proceedings of the Annual International
Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6919–
6922 (2011)
73. Zreik, M., Ben-Tsvi, Y., Taub, A., Almog, R.O., Messer, H.: Detection of auditory stimulus
onset in the pontine nucleus using a multichannel multi-unit activity electrode. In: IEEE
International Conference on Acoustics, Speech and Signal Processing, ICASSP, vol. 2, no.
17, pp. 2708–2711 (2011)
Physiological Signals Fusion Oriented to Diagnosis - A Review 15
74. Ueda, H., Miyawaki, M., Hiraoka, H.: High-normal blood pressure is associated with new-
onset electrocardiographic left ventricular hypertrophy. J. Hum. Hypertens. 29(1), 9–13
(2015)
75. Benoit, A., et al.: Multimodal focus attention and stress detection and feedback in an
augmented driver simulator. Pers. Ubiquitous Comput. 13(1), 33–41 (2009)
76. Ai, L., Wang, J., Wang, X.: Multi-features fusion diagnosis of tremor based on artificial
neural network and D–S evidence theory. Sig. Process. 88, 2927–2935 (2008)
77. Sukuvaara, T., Heikela, A.: Computerized patient monitoring. Acta Anaesthesiol. Scand. 37,
185–189 (1993)
78. Liou, L.M., et al.: Functional connectivity between parietal cortex and the cardiac autonomic
system in uremics. Kaohsiung J. Med. Sci. 30(3), 125–132 (2014)
79. Almasri, M.M., Elleithy, K.M.: Data fusion models in WSNs: comparison and analysis. In:
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education
-Engineering Education: Industry Involvement and Interdisciplinary Trends, ASEE Zone 1,
no. 203 (2014)
80. Synnergren, J., Gamalielsson, J., Olsson, B.: Mapping of the JDL data fusion model to
bioinformatics. In: Conference Proceedings - IEEE International Conference on Systems,
Man and Cybernetics, pp. 1506–1511 (2007)
81. Uluda, K., Roebroeck, A.: General overview on the merits of multimodal neuroimaging data
fusion. NeuroImage 102(P1), 3–10 (2014)
82. Mohamed, S., Haggag, S., Nahavandi, S., Haggag, O.: Towards automated quality
assessment measure for EEG signals. Neurocomputing 237, 281–290 (2017)
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the whole quadrangle, and others are picturesque, vine-wreathed
masses, looking most like the standing chimneys of a burnt house.
This Buddhist sanctuary of the eleventh century has almost the
same general plan as Boro Boedor, but a Boro Boedor spread out
and built all on the one level. The five lines of temples, with broad
processional paths between them, correspond to the five square
terraces of Boro Boedor; and the six superior chapels correspond to
the circles of latticed dagobas near Boro Boeder’s summit. The
empty central shrine at Chandi Sewou has crumbled to a heap of
stones, with only its four stepped-arch entrance-doors distinct; and
the smaller temples, each of them eleven feet square and eighteen
feet high, with inner walls covered with bas-reliefs, are empty as
well. When the British officers surveyed Chandi Sewou, five of the
chapels contained cross-legged images seated on lotus pedestals—
either Buddha, or the tirthankars, or Jain saints; but even those
headless and mutilated statues are missing now. Every evidence
could be had of wilful destruction of the group of shrines, and the
same mysterious well-hole was found beneath the pedestal of the
image in each chapel—whether as receptacle for the ashes of
priests and princes; a place for the safe keeping of temple treasures;
as an empty survival of the form of the earliest tree-temples, when
the mystery of animate nature commanded man’s worship; or, as M.
de Charnay suggests, the orifice from which proceeded the voice of
the concealed priest who served as oracle.
With these Brambanam temples, when Sivaism or Jainism had
succeeded Buddhism, and even before Mohammedanism came, the
decadence of arts and letters began. The Arab conquest made it
complete, and the art of architecture died entirely, no structures
since that time redeeming the people and religion which in India and
Spain have left such monuments of beauty.
PLAN OF CHANDI SEWOU (“THOUSAND TEMPLES”).
The present susunhan of Solo is not the son of the last emperor,
but a collateral descendant of the old emperors, who claims descent
from both Mohammedan and Hindu rulers, the monkey flag of Arjuna
and the double-bladed sword of the Arab conquerors alike his
heirlooms and insignia. His portraits show a gentle, refined face of
the best Javanese type, and he wears a European military coat, with
the native sarong and Arab fez, a court sword at the front of his belt,
and a Solo kris at the back. Despite his trappings and his sovereign
title, he is as much a puppet and a prisoner as any of the lesser
princes, sultans, and regents whom the Dutch, having deposed and
pensioned, allow to masquerade in sham authority. He maintains all
the state and splendor of the old imperialism within his kraton, which
is confronted and overlooked by a Dutch fort, whose guns, always
trained upon the kraton, could sweep and level the whole imperial
establishment at a moment’s notice. The susunhan may have ten
thousand people living within his kraton walls; he may have nine
hundred and ninety-nine wives and one hundred and fifty carriages,
as reported; but he may not drive beyond his own gates without
informing the Dutch resident where he is going or has been, with his
guard of honor of Dutch soldiers, and he has hardly the liberty of a
tourist with a toelatings-kaart. He may amuse himself with a little
body-guard of Javanese soldiers; but there is a petty sultan of Solo,
an ancient vassal, whose military ambitions are encouraged by the
Dutch to the extent of allowing him to drill and command a private
army of a thousand men that the Dutch believe would never by any
chance take arms against them, as allies of the susunhan’s fancy
guard. Wherever they have allowed any empty show of sovereignty
to a native ruler, the Dutch have taken care to equip a military rival,
with the lasting grudge of an inherited family feud, and establish him
in the same town. But little diplomacy is required to keep such
jealousies alive and aflame, and the Dutch are always an apparent
check, and pacific mediators between such rivals as the susunhan
and the sultan at Solo, and the sultan and Prince Pakoe Alam at
Djokja.
The young susunhan maintains his empty honors with great
dignity and serenity, observing all the European forms and etiquette
at his entertainments, and delighting Solo’s august society with
frequent court balls and fêtes. Town gossip dilates on his marble-
floored ball-room, the fantastic devices in electric lights employed in
illuminating the palace and its maze of gardens on such occasions,
and on the blaze of heirloom jewels worn by the imperial ladies and
princesses at such functions. The susunhan sometimes grants
audiences to distinguished strangers, and one French visitor has told
of some magnificent Japanese bronzes and Chinese porcelains in
the kraton, which were gifts from the Dutch in the early time when
the Japanese and Javanese trade were both Holland monopolies.
No prostrations or Oriental salaams are required of European visitors
at court, although the old susunhans obliged even the crown prince
and prime minister to assume the dodok, and sidle about like any
cup-bearer in his presence. The princes and petty chiefs were so
precisely graded in rank in those days that, while the highest might
kiss the sovereign’s hand, and those of a lower rank the imperial
knee, there were those of lesser pretensions who adoringly kissed
the instep, and, last of all, those who might only presume to kiss the
sole, of the susunhan’s foot. The susunhan is always accompanied
on his walks in the palace grounds, and on drives abroad, by a
bearer with a gold pajong, or state umbrella, spreading from a
jeweled golden staff. The array of pajongs carried behind the
members of his family and court officials present all the colors of the
rainbow, and all the variegations a fancy umbrella is capable of
showing—each striped, banded, bordered, and vandyked in a
different way, that would puzzle the brain of any but a Solo courtier,
to whom they speak as plainly as a door-plate.
Solo has the same broad streets and magnificent shade-trees as
the other towns of Java, and some of the streets have deep ditches
or moats on either side of the drive, with separate little bridges
crossing to each house-front, which give those thoroughfares a
certain feudal quaintness and character of their own. At the late
afternoon hour of our arrival we only stopped for a moment to
deposit the luggage at the enormously porticoed Hotel Sleier, and
then drove on through and about the imperial city. The streets were
full of other carriages,—enormous barouches, “milords,” and family
carryalls, drawn by big Walers,—with which we finally drew up in line
around the park, where a military band was playing. We had seen
bewildering lines of palace and fort and barrack walls, marching
troops, and soldiers lounging about off duty, until it was easy to see
that Solo was a vast garrison, more camp than court. Later, when we
had returned to the hotel portico, to swing at ease in great broad-
armed rocking-chairs,—exactly the Shaker piazza-chairs of
American summer life,—there was still sound of military music off
beyond the dense waringen shade, and the fanfare of bugles to right
and to left.
Solo’s hotel, with its comforts, offered more material inducements
for us to make a long stay, than any hotel we had yet encountered in
Java; and the clear-headed, courteous landlady was a hostess in the
most kindly sense. The usual colonial table d’hôte assembled at nine
o’clock in the vast inner hall or pavilion, looking on a garden; and in
this small world, where every one knows every one, his habitat and
all his affairs, the new-comers were given a silent, earnest attention
that would have checked any appetites save those engendered by
our archæological afternoon at Brambanam. When beefsteak was
served with a sauce of pineapple mashed with potato, and the
succeeding beet salad was followed by fried fish, and that by a
sweet pudding flooded with a mixture of melted chocolate and
freshly ground cocoanut, we were oblivious to all stares and
whispers and open comments in Dutch, which these colonials take it
for granted no alien understands or can even have clue to through its
likeness to German. While we rocked on the great white portico we
could see and hear that Solo’s lizards were as gruesome and
plentiful as those of other towns. While tiny fragilities flashed across
white columns and walls, and arrested themselves as instantaneous
traceries and ornaments, a legion of toads came up from the garden,
and hopped over the floor in a silence that made us realize how
much pleasanter companions were the croaking and bemoaning
geckos, who keep their ugliness out of sight.
THE DODOK.
The stir of camp and court, the state and pomp and pageantry of
three such grandees as emperor, sultan, and resident in the one city,
made such street-scenes in Solo as tempted the kodaker to constant
play while the sun was high. Bands and marching troops were
always to be seen in the street, and the native officials of so many
different kinds made pictures of bewildering variety. The resident,
returning from an official call, dashed past in a coach and four, with
pajong-bearers hanging perilously on behind, and a mounted escort
clattering after. Members of the imperial household staff were
distinguished by stiff sugar-loaf caps or fezzes of white leather; and
such privileged ones stalked along slowly, magnificently, each with a
kris at the back of his belt, and always followed by one or two lesser
minions. Those of superior rank went accompanied by a pajong-
bearer balancing the great flat umbrella of rank above the
distinguished one’s head; and the precision with which the grandee
kept his head within the halo of shadow, or the bearer managed to
keep such a true angle on the sun, were something admirable, and
only to be accomplished by generations of the two classes practising
their respective feats. The emperor’s mounted troops were objects of
greater interest, these dragoons wearing huge lacquered vizors or
crownless caps over their turbaned heads, the regulation jackets,
sarongs, and heavy krises, and bestriding fiery little Timor ponies.
The native stirrup is a single upright bar of iron, which a rider holds
between the great toe and its neighbor; and these troopers seemed
to derive as much support from this firm toe-grip as booted riders do
from resting the whole ball of the foot on our stirrups.
There is a labyrinthine passer at Solo, where open sheds and
rustic booths have grown upon one another around several open
court spaces, which are dotted with the huge mushrooms of palm-
leaf umbrellas, and whose picturesqueness one cannot nearly
exhaust in a single morning’s round. The pepper- and fruit- and
flower-markets are, of course, the regions of greatest attraction and
richest feasts of color. The horn of plenty overflowed royally there,
and the masses of bananas and pineapples, durians, nankos,
mangosteens, jamboas, salaks, dukus, and rambutans seemed
richer in color than we had ever seen before; and the brass-, the
basket-, the bird-, the spice-, and the gum-markets had greater
attractions too. The buyers were as interesting as the venders, and a
frequent figure in these market groups that tempted the kodaker to
many an instantaneous shot, regardless of the light,—better any
muddy impression of that than none at all,—was the Dutch
housewife on her morning rounds. I braved sunstroke and apoplexy
in the hot sunshine, and trailed my saronged subjects down crowded
aisles to open spots, to fix on film the image of these sockless
matrons in their very informal morning dress. I lurked in booths and
sat for endless minutes in opposite shops, with focus set and button
at touch, to get a good study of Dutch ankles, when certain typical
Solo hausfraus should return to and mount their carriage steps—only
to have some loiterer’s back obscure the whole range of the lens at
the critical second.
JAVA, BALI, AND MADURA KRISES.