Data mining in mobile ECG based biometric identification

This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (N...

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Main Authors:	Sidek, Khairul Azami, Mai, Vu, Khalil, Ibrahim
Format:	Article
Language:	English
Published:	Elsevier, USA 2014
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf

id	iium-36914
recordtype	eprints
spelling	iium-369142018-06-11T07:01:24Z http://irep.iium.edu.my/36914/ Data mining in mobile ECG based biometric identification Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim TK7885 Computer engineering This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (NSRDB) were used to validate the reliability and stability of the subject recognition methods. Discriminatory features extracted from the experimentation were later applied to different classifiers for performance measures based on the complexity of our proposed sample extraction method when compared to other related algorithms, the total execution time (TET) applied on different classifiers in various mobile devices and the classification accuracies when applied to various classification techniques. Experimentation results showed that our method simplifies biometric identification process by obtaining reduced computational complexity when compared to other related algorithms. This is evident when TET values were significantly low on mobile devices as compared to a non-mobile device while maintaining high accuracy rates ranging from 98.30% to 99.07% in different classifiers. Therefore, these outcomes support the usability of ECG based biometric identification in a mobile environment. Elsevier, USA 2014-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf Sidek, Khairul Azami and Mai, Vu and Khalil, Ibrahim (2014) Data mining in mobile ECG based biometric identification. Journal of Network and Computer Applications, 44. pp. 83-91. ISSN 1084-8045 http://www.sciencedirect.com/science/article/pii/S1084804514000915 10.1016/j.jnca.2014.04.008
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 Mai, Vu Khalil, Ibrahim Data mining in mobile ECG based biometric identification
description	This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (NSRDB) were used to validate the reliability and stability of the subject recognition methods. Discriminatory features extracted from the experimentation were later applied to different classifiers for performance measures based on the complexity of our proposed sample extraction method when compared to other related algorithms, the total execution time (TET) applied on different classifiers in various mobile devices and the classification accuracies when applied to various classification techniques. Experimentation results showed that our method simplifies biometric identification process by obtaining reduced computational complexity when compared to other related algorithms. This is evident when TET values were significantly low on mobile devices as compared to a non-mobile device while maintaining high accuracy rates ranging from 98.30% to 99.07% in different classifiers. Therefore, these outcomes support the usability of ECG based biometric identification in a mobile environment.
format	Article
author	Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim
author_facet	Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim
author_sort	Sidek, Khairul Azami
title	Data mining in mobile ECG based biometric identification
title_short	Data mining in mobile ECG based biometric identification
title_full	Data mining in mobile ECG based biometric identification
title_fullStr	Data mining in mobile ECG based biometric identification
title_full_unstemmed	Data mining in mobile ECG based biometric identification
title_sort	data mining in mobile ecg based biometric identification
publisher	Elsevier, USA
publishDate	2014
url	http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf
first_indexed	2023-09-18T20:52:57Z
last_indexed	2023-09-18T20:52:57Z
_version_	1777410115700260864

Data mining in mobile ECG based biometric identification

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