Instrumentation & Measurement Magazine 24-2 - 43

Holter electrocardiogram, telemetry, and bedside monitoring

[27]	P. X. Quesnel, A. D. C. Chan, and H. Yang, " Real-time biosignal

systems, " Eur. Heart J. Suppl., vol. 19, no. suppl_D, pp. D190-

quality analysis of ambulatory ECG for detection of myocardial

D211, May 2017.

ischemia, " in Proc. 2013 IEEE Int. Symp. Medical Meas. Applications

[12]	G. D. Fraser, A. D. C. Chan, J. R. Green, and D. T. MacIsaac,
" Automated biosignal quality analysis for electromyography

(MeMeA), pp. 1-5, May 2013.
[28]	M. Abdelazez, P. X. Quesnel, A. D. C. Chan, and H. Yang, " Signal

using a one-class support vector machine, " IEEE Trans. Instrum.

quality indices for ambulatory electrocardiograms used in

Meas., vol. 63, no. 12, pp. 2919-2930, Dec. 2014.

myocardial ischemia monitoring, " in World Congress on Medical

[13]	J. C. Lerner et al., " Top 10 Patient Safety Concerns for Healthcare
Organizations, " ECRI Institute, Plymouth Meeting, PA, USA,

Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada,
vol. 51, pp. 1076-1079, D. A. Jaffray, Ed. Cham, Switzerland:
Springer International Publishing, 2015.

Apr. 2015.
[14]	H. Yang, A. Fayad, A. Chaput, S. Oake, A. D. C. Chan, and

[29]	M. H. Crawford et al., " ACC/AHA guidelines for ambulatory

M. L. Crossan, " Postoperative real-time electrocardiography

electrocardiography: executive summary and recommendations

monitoring detects myocardial ischemia: a case report, " Can. J.

a report of the American College of Cardiology/American Heart

Anesth. Can. Anesth., vol. 64, no. 4, pp. 411-415, Apr. 2017.

Association Task Force on Practice Guidelines (Committee to

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ST values, " CMBES Proc., vol. 34, Jun. 2011.
[16]	M. Abdelazez, M. Hozayn, G. S. K. Hanna, and A. D. C. Chan,
" Gating of false identifications in electrocardiogram based
biometric system, " in Proc. 2017 IEEE Int. Symp. Medical Meas.
Applications (MeMeA), pp. 338-343, May 2017.
[17]	J. Behar, J. Oster, Q. Li, and G. D. Clifford, " ECG signal quality
during arrhythmia and its application to false alarm reduction, "
IEEE Trans. Biomed. Eng., vol. 60, no. 6, pp. 1660-1666, Jun. 2013.
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components of a new research resource for complex physiologic
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[19]	M. AlMahamdy and H. B. Riley, " Performance study of different

Revise the Guidelines for Ambulatory Electrocardiography)
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interpretation of the electrocardiogram, " J. Am. Coll. Cardiol., vol.
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[31]	X. He, R. A. Goubran, and X. P. Liu, " Signal enhancement of
wearable ECG monitoring sensors based on ensemble empirical
mode decomposition, " in Proc. 2011 IEEE Int. Symp. Medical Meas.
Applications, pp. 433-436, May 2011.
[32]	E. Farago and A. D. C. Chan, " Simulating motion artifact using
an autoregressive model for research in biomedical signal quality

denoising methods for ECG signals, " Procedia Comput. Sci., vol.

analysis, " in Proc. 42nd Annual Int. Conf. IEEE Engin. Medicine

37, pp. 325-332, Jan. 2014.

Biology Soc. (EMBC), pp. 940- 943, Jul. 2020.

[20]	U. Satija, B. Ramkumar, and M. S. Manikandan, " A review of
signal processing techniques for electrocardiogram signal quality
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[21]	C. Orphanidou, T. Bonnici, P. Charlton, D. Clifton, D.
Vallance, and L. Tarassenko, " Signal-quality indices for the
electrocardiogram and photoplethysmogram: derivation and
applications to wireless monitoring, " IEEE J. Biomed. Health
Inform., vol. 19, no. 3, pp. 832-838, May 2015.

Mohamed Abdelazez (MohamedAbdelazez@cmail.carleton.
ca) is a Ph.D. degree candidate and a Vanier Scholar at Carleton University, Ontario, Canada. He received the B.Eng. and
M.A.Sc. degrees from Carleton University in 2014 and 2016,
respectively, both in the field of biomedical engineering. His
research interests include biosignal analysis, health monitoring, and applications of machine learning in healthcare.

[22]	B. Taji, A. D. C. Chan, and S. Shirmohammadi, " Classifying
measured electrocardiogram signal quality using deep belief
networks, " in Proc. 2017 IEEE Int. Instrum. Meas. Technol. Conf.
(I2MTC), pp. 1-6, May 2017.
[23]	P. McCool, G. D. Fraser, A. D. C. Chan, L. Petropoulakis, and
J. J. Soraghan, " Identification of contaminant type in surface
electromyography (EMG) signals, " IEEE Trans. Neural Syst.
Rehabil. Eng., vol. 22, no. 4, pp. 774-783, Jul. 2014.
[24]	M. Abdelazez, S. Rajan, and A. D. C. Chan, " Detection of noise
type in electrocardiogram, " in Proc. 2018 IEEE Int. Symp. Medical
Meas. Applications (MeMeA), pp. 1-6, Jun. 2018.
[25]	Q. Li, C. Rajagopalan, and G. D. Clifford, " A machine learning
approach to multi-level ECG signal quality classification, "
Comput. Methods Programs Biomed., vol. 117, no. 3, pp. 435-447,
Dec. 2014.
[26]	P. X. Quesnel, A. D. C. Chan, and H. Yang, " Signal quality

Sreeraman Rajan (sreeraman.rajan@carleton.ca) is a Tier 2
Canada Research Chair in advanced sensor systems and signal
processing at the Department of Systems and Computer Engineering, Carleton University, Ontario, Canada, where he has
worked since July 2015. He is a senior member of IEEE and Director, Ottawa-Carleton Institute for Biomedical Engineering.
He received the bachelor's degree in electronics and communication engineering from Bharathiyar University, Coimbatore,
India in 1987, the M.Sc. degree in electrical engineering from
Tulane University in 1992, New Orleans, LA, and the Ph.D.
degree in electrical and computer engineering from the University of New Brunswick, Fredericton, New Brunswick,
Canada in 2004. His research interests include signal, image
and biomedical signal processing, signal processing for active,
passive, and wearable sensors, and applied machine learning.

and false myocardial ischemia alarms in ambulatory
electrocardiograms, " in Proc. 2014 IEEE Int. Symp. Medical Meas.
Applications (MeMeA), pp. 1-5, Jun. 2014.
April 2021	

Adrian D. C. Chan (adrian.chan@carleton.ca) is a Professor
with the Department of Systems and Computer Engineering at

IEEE Instrumentation & Measurement Magazine	43
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