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Non-fiducial based ECG biometric authentication using one-class support vector machine

Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a rando...

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Main Authors:	Hejazi, Maryamsadat, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Hashim, Shaiful Jahari, Abdul Aziz, Ahmad Fazli, Singh, Yashwant Prasad
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2017
Online Access:	http://psasir.upm.edu.my/id/eprint/59477/1/Non-fiducial%20based%20ECG%20biometric%20authentication%20using%20one-class%20support%20vector%20machine.pdf http://psasir.upm.edu.my/id/eprint/59477/
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id	my.upm.eprints.59477
record_format	eprints
spelling	my.upm.eprints.594772018-03-07T01:35:01Z http://psasir.upm.edu.my/id/eprint/59477/ Non-fiducial based ECG biometric authentication using one-class support vector machine Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli Singh, Yashwant Prasad Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered by other biometric traits, but has been so far left aside for analysis of ECG signals. This paper investigates the effect of different parameters of data set size, labeling data, configuration of training and testing data sets, feature extraction, different recording sessions, and random partition methods on accuracy and error rates of these SVM classifiers. The experiments were carried out with defining a number of scenarios on ECG data sets designed rely on feature extractors which were modeled based on an autocorrelation in conjunction with linear and nonlinear dimension reduction methods. The experimental results show that Kernel Principal Component Analysis has lower error rate in binary and one-class SVMs on random unknown ECG data sets. Moreover, one-class SVM can be robust recognition algorithm for ECG biometric verification if the sufficient number of biometric samples is available. IEEE 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59477/1/Non-fiducial%20based%20ECG%20biometric%20authentication%20using%20one-class%20support%20vector%20machine.pdf Hejazi, Maryamsadat and Syed Mohamed, Syed Abdul Rahman Al-Haddad and Hashim, Shaiful Jahari and Abdul Aziz, Ahmad Fazli and Singh, Yashwant Prasad (2017) Non-fiducial based ECG biometric authentication using one-class support vector machine. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA 2017), 20-22 Sept. 2017, Poznan, Poland. (pp. 190-194). 10.23919/SPA.2017.8166862
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English
description	Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered by other biometric traits, but has been so far left aside for analysis of ECG signals. This paper investigates the effect of different parameters of data set size, labeling data, configuration of training and testing data sets, feature extraction, different recording sessions, and random partition methods on accuracy and error rates of these SVM classifiers. The experiments were carried out with defining a number of scenarios on ECG data sets designed rely on feature extractors which were modeled based on an autocorrelation in conjunction with linear and nonlinear dimension reduction methods. The experimental results show that Kernel Principal Component Analysis has lower error rate in binary and one-class SVMs on random unknown ECG data sets. Moreover, one-class SVM can be robust recognition algorithm for ECG biometric verification if the sufficient number of biometric samples is available.
format	Conference or Workshop Item
author	Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli Singh, Yashwant Prasad
spellingShingle	Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli Singh, Yashwant Prasad Non-fiducial based ECG biometric authentication using one-class support vector machine
author_facet	Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli Singh, Yashwant Prasad
author_sort	Hejazi, Maryamsadat
title	Non-fiducial based ECG biometric authentication using one-class support vector machine
title_short	Non-fiducial based ECG biometric authentication using one-class support vector machine
title_full	Non-fiducial based ECG biometric authentication using one-class support vector machine
title_fullStr	Non-fiducial based ECG biometric authentication using one-class support vector machine
title_full_unstemmed	Non-fiducial based ECG biometric authentication using one-class support vector machine
title_sort	non-fiducial based ecg biometric authentication using one-class support vector machine
publisher	IEEE
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/59477/1/Non-fiducial%20based%20ECG%20biometric%20authentication%20using%20one-class%20support%20vector%20machine.pdf http://psasir.upm.edu.my/id/eprint/59477/
_version_	1643837084035710976
score	13.211869

Non-fiducial based ECG biometric authentication using one-class support vector machine

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