ECG biometric recognition in different physiological conditions using robust normalized QRS complexes

This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are...

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Main Authors:	Sidek, Khairul Azami, Khalil, Ibrahim, Smolen, Magdalena
Format:	Article
Language:	English
Published:	Computing in Cardiology 2012
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/31987/ http://irep.iium.edu.my/31987/ http://irep.iium.edu.my/31987/1/CinC2012paper.pdf

id	iium-31987
recordtype	eprints
spelling	iium-319872013-09-17T02:53:08Z http://irep.iium.edu.my/31987/ ECG biometric recognition in different physiological conditions using robust normalized QRS complexes Sidek, Khairul Azami Khalil, Ibrahim Smolen, Magdalena TK7885 Computer engineering This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Unique features were extracted in these different physiological conditions from the same subject using normalized QRS complex technique. One physiological activity acts as the enrolment template while the remaining five activities represent the recognition data. Cross correlation was used to measure the similarity between activities. Later, Multilayer Perceptron classifier was applied to evaluate the distinctiveness between subjects. The results of the experiment show that QRS complexes in different activities from the same subject were strongly correlated to each other by obtaining correlation values of more than 0.9. A classification accuracy of 96.1% when using the proposed normalized method as compared to 93.4% without using the normalized QRS complex proves to distinguish between subjects. Computing in Cardiology 2012-09-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/31987/1/CinC2012paper.pdf Sidek, Khairul Azami and Khalil, Ibrahim and Smolen, Magdalena (2012) ECG biometric recognition in different physiological conditions using robust normalized QRS complexes. Computing in Cardiology, 39. pp. 97-100. ISSN 0276-6574 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6420339&tag=1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Sidek, Khairul Azami Khalil, Ibrahim Smolen, Magdalena ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
description	This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Unique features were extracted in these different physiological conditions from the same subject using normalized QRS complex technique. One physiological activity acts as the enrolment template while the remaining five activities represent the recognition data. Cross correlation was used to measure the similarity between activities. Later, Multilayer Perceptron classifier was applied to evaluate the distinctiveness between subjects. The results of the experiment show that QRS complexes in different activities from the same subject were strongly correlated to each other by obtaining correlation values of more than 0.9. A classification accuracy of 96.1% when using the proposed normalized method as compared to 93.4% without using the normalized QRS complex proves to distinguish between subjects.
format	Article
author	Sidek, Khairul Azami Khalil, Ibrahim Smolen, Magdalena
author_facet	Sidek, Khairul Azami Khalil, Ibrahim Smolen, Magdalena
author_sort	Sidek, Khairul Azami
title	ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
title_short	ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
title_full	ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
title_fullStr	ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
title_full_unstemmed	ECG biometric recognition in different physiological conditions using robust normalized QRS complexes
title_sort	ecg biometric recognition in different physiological conditions using robust normalized qrs complexes
publisher	Computing in Cardiology
publishDate	2012
url	http://irep.iium.edu.my/31987/ http://irep.iium.edu.my/31987/ http://irep.iium.edu.my/31987/1/CinC2012paper.pdf
first_indexed	2023-09-18T20:46:09Z
last_indexed	2023-09-18T20:46:09Z
_version_	1777409688164368384

ECG biometric recognition in different physiological conditions using robust normalized QRS complexes

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