Cardioid graph based ECG biometric recognition incorporating physiological variability

This paper investigates ECG signal in different physiological conditions to identify different individuals. Data was acquired from 30 subjects, where each subject performed six types of physical activities namely walking, going upstairs, going downstairs, natural gait, lying with position changed an...

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Main Authors:	Iqbal, Fatema-tuz-Zohra, Sidek, Khairul Azami
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/41615/ http://irep.iium.edu.my/41615/ http://irep.iium.edu.my/41615/1/41615.pdf

id	iium-41615
recordtype	eprints
spelling	iium-416152016-03-13T08:40:45Z http://irep.iium.edu.my/41615/ Cardioid graph based ECG biometric recognition incorporating physiological variability Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami TK7885 Computer engineering This paper investigates ECG signal in different physiological conditions to identify different individuals. Data was acquired from 30 subjects, where each subject performed six types of physical activities namely walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Then from the signals of these physiological conditions, specific features exclusive to each subject was extracted employing the Cardioid graph method. In this model, features were extracted solely from the graph derived using QRS complexes. Subjects were recognized with Multilayer Perceptron. Results were obtained through two approaches. In the former procedure, classification was performed on the whole dataset consisting of both training and testing set, which produced 95.3% of correctly classified instances. In the later approach the training and testing set was predefined where correctly classified instances were 93.9%. These results confirm that subject identification at different physiological conditions with Cardioid graph based technique produces better classification rates than previous study using only QRS complex. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/41615/1/41615.pdf Iqbal, Fatema-tuz-Zohra and Sidek, Khairul Azami (2014) Cardioid graph based ECG biometric recognition incorporating physiological variability. In: 2014 IEEE Student Conference on Research and Development (SCOReD), 16-17 December 2014, Park Royal, Batu Feringghi, Penang. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7072961
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 Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami Cardioid graph based ECG biometric recognition incorporating physiological variability
description	This paper investigates ECG signal in different physiological conditions to identify different individuals. Data was acquired from 30 subjects, where each subject performed six types of physical activities namely walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Then from the signals of these physiological conditions, specific features exclusive to each subject was extracted employing the Cardioid graph method. In this model, features were extracted solely from the graph derived using QRS complexes. Subjects were recognized with Multilayer Perceptron. Results were obtained through two approaches. In the former procedure, classification was performed on the whole dataset consisting of both training and testing set, which produced 95.3% of correctly classified instances. In the later approach the training and testing set was predefined where correctly classified instances were 93.9%. These results confirm that subject identification at different physiological conditions with Cardioid graph based technique produces better classification rates than previous study using only QRS complex.
format	Conference or Workshop Item
author	Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami
author_facet	Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami
author_sort	Iqbal, Fatema-tuz-Zohra
title	Cardioid graph based ECG biometric recognition incorporating physiological variability
title_short	Cardioid graph based ECG biometric recognition incorporating physiological variability
title_full	Cardioid graph based ECG biometric recognition incorporating physiological variability
title_fullStr	Cardioid graph based ECG biometric recognition incorporating physiological variability
title_full_unstemmed	Cardioid graph based ECG biometric recognition incorporating physiological variability
title_sort	cardioid graph based ecg biometric recognition incorporating physiological variability
publishDate	2014
url	http://irep.iium.edu.my/41615/ http://irep.iium.edu.my/41615/ http://irep.iium.edu.my/41615/1/41615.pdf
first_indexed	2023-09-18T20:59:33Z
last_indexed	2023-09-18T20:59:33Z
_version_	1777410531188015104

Cardioid graph based ECG biometric recognition incorporating physiological variability

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