Compressed ECG biometric using cardioid graph based feature extraction

In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompressi...

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Main Authors:	Iqbal, Fatema-tuz-Zohra, Sidek, Khairul Azami
Format:	Article
Language:	English
Published:	Asian Research Publishing Network (ARPN) 2015
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/46654/ http://irep.iium.edu.my/46654/ http://irep.iium.edu.my/46654/1/jeas_1215_3154.pdf

id	iium-46654
recordtype	eprints
spelling	iium-466542017-11-16T09:45:16Z http://irep.iium.edu.my/46654/ Compressed ECG biometric using cardioid graph based feature extraction Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami TK7885 Computer engineering In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompression of the compressed ECG signal is necessary before the recognition step. The QRS complexes obtained from the ECG signal is compressed using Discrete Wavelet Transform (DWT), followed by the Cardioid graph retrieval procedure. Compression is performed in three decomposition levels and with the first two Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 87.5% is achieved with an accuracy rate of 93.75%. For compression rate of 85%, the identification rate obtained is 98.75%. The same highest recognition rate of 98.75% is attained both with non-compressed and compressed data. The classification accuracy rates suggest that compressed ECG biometric with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system. Asian Research Publishing Network (ARPN) 2015 Article PeerReviewed application/pdf en http://irep.iium.edu.my/46654/1/jeas_1215_3154.pdf Iqbal, Fatema-tuz-Zohra and Sidek, Khairul Azami (2015) Compressed ECG biometric using cardioid graph based feature extraction. ARPN Journal of Engineering and Applied Sciences, 10 (22). pp. 17219-17224. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1215_3154.pdf
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 Compressed ECG biometric using cardioid graph based feature extraction
description	In this paper, a Cardioid graph based feature extraction technique is applied to perform compressed Electrocardiogram (ECG) biometric. To the best of our knowledge, Cardioid graph based method has not been implemented on compressed ECG before. Another merit of this methodology is that no decompression of the compressed ECG signal is necessary before the recognition step. The QRS complexes obtained from the ECG signal is compressed using Discrete Wavelet Transform (DWT), followed by the Cardioid graph retrieval procedure. Compression is performed in three decomposition levels and with the first two Daubechies wavelets. Classification is conducted on all the three levels using Multilayer Perceptron (MLP) Neural Network. Maximum compression of 87.5% is achieved with an accuracy rate of 93.75%. For compression rate of 85%, the identification rate obtained is 98.75%. The same highest recognition rate of 98.75% is attained both with non-compressed and compressed data. The classification accuracy rates suggest that compressed ECG biometric with Cardioid graph based feature extraction is feasible and is capable of producing a robust biometric system.
format	Article
author	Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami
author_facet	Iqbal, Fatema-tuz-Zohra Sidek, Khairul Azami
author_sort	Iqbal, Fatema-tuz-Zohra
title	Compressed ECG biometric using cardioid graph based feature extraction
title_short	Compressed ECG biometric using cardioid graph based feature extraction
title_full	Compressed ECG biometric using cardioid graph based feature extraction
title_fullStr	Compressed ECG biometric using cardioid graph based feature extraction
title_full_unstemmed	Compressed ECG biometric using cardioid graph based feature extraction
title_sort	compressed ecg biometric using cardioid graph based feature extraction
publisher	Asian Research Publishing Network (ARPN)
publishDate	2015
url	http://irep.iium.edu.my/46654/ http://irep.iium.edu.my/46654/ http://irep.iium.edu.my/46654/1/jeas_1215_3154.pdf
first_indexed	2023-09-18T21:06:25Z
last_indexed	2023-09-18T21:06:25Z
_version_	1777410963436208128

Compressed ECG biometric using cardioid graph based feature extraction

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