Data mining technique on Cardioid graph based ECG biometric authentication

In this paper, a data mining technique is used on Cardioid based person identification mechanism using electrocardiogram (ECG). Recent studies in Cardioid based ECG biometric excites a new dimension of efficient patient authentication, which places new hope in faster patient care. However, existing...

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Main Authors:	Sidek, Khairul Azami, Sufi, Fahim, Khalil, Ibrahim
Format:	Conference or Workshop Item
Language:	English
Published:	2011
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/31999/ http://irep.iium.edu.my/31999/ http://irep.iium.edu.my/31999/1/723-144.pdf

id	iium-31999
recordtype	eprints
spelling	iium-319992013-09-17T01:14:19Z http://irep.iium.edu.my/31999/ Data mining technique on Cardioid graph based ECG biometric authentication Sidek, Khairul Azami Sufi, Fahim Khalil, Ibrahim TK7885 Computer engineering In this paper, a data mining technique is used on Cardioid based person identification mechanism using electrocardiogram (ECG). Recent studies in Cardioid based ECG biometric excites a new dimension of efficient patient authentication, which places new hope in faster patient care. However, existing research suffers from lower accuracy due to random biometric template selection from fixed points in Cartesian coordinate. In this paper, we have extracted the ECG features using set of Euclidean distances with the help of data mining techniques. Euclidean distances, being independent of fixed points (as opposed to existing research) maintains higher accuracy in biometric identification when Bayes Network was implemented for classification purposes. A total of 26 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) and MIT/BIH Arrythmia database (MITDB) are used for development and evaluation. Our experimentation on these two sets of public ECG databases shows the proposed data mining based approach on Euclidean distances obtained from Cardioid graph results to 98.60% and 98.30% classification accuracy respectively. 2011-02-16 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/31999/1/723-144.pdf Sidek, Khairul Azami and Sufi, Fahim and Khalil, Ibrahim (2011) Data mining technique on Cardioid graph based ECG biometric authentication. In: The Eighth IASTED International Conference on Biomedical Engineering (Biomed 2011), 16th - 18th February 2011, Innsbruck, Austria. http://www.actapress.com/Abstract.aspx?paperId=451665
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 Sufi, Fahim Khalil, Ibrahim Data mining technique on Cardioid graph based ECG biometric authentication
description	In this paper, a data mining technique is used on Cardioid based person identification mechanism using electrocardiogram (ECG). Recent studies in Cardioid based ECG biometric excites a new dimension of efficient patient authentication, which places new hope in faster patient care. However, existing research suffers from lower accuracy due to random biometric template selection from fixed points in Cartesian coordinate. In this paper, we have extracted the ECG features using set of Euclidean distances with the help of data mining techniques. Euclidean distances, being independent of fixed points (as opposed to existing research) maintains higher accuracy in biometric identification when Bayes Network was implemented for classification purposes. A total of 26 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) and MIT/BIH Arrythmia database (MITDB) are used for development and evaluation. Our experimentation on these two sets of public ECG databases shows the proposed data mining based approach on Euclidean distances obtained from Cardioid graph results to 98.60% and 98.30% classification accuracy respectively.
format	Conference or Workshop Item
author	Sidek, Khairul Azami Sufi, Fahim Khalil, Ibrahim
author_facet	Sidek, Khairul Azami Sufi, Fahim Khalil, Ibrahim
author_sort	Sidek, Khairul Azami
title	Data mining technique on Cardioid graph based ECG biometric authentication
title_short	Data mining technique on Cardioid graph based ECG biometric authentication
title_full	Data mining technique on Cardioid graph based ECG biometric authentication
title_fullStr	Data mining technique on Cardioid graph based ECG biometric authentication
title_full_unstemmed	Data mining technique on Cardioid graph based ECG biometric authentication
title_sort	data mining technique on cardioid graph based ecg biometric authentication
publishDate	2011
url	http://irep.iium.edu.my/31999/ http://irep.iium.edu.my/31999/ http://irep.iium.edu.my/31999/1/723-144.pdf
first_indexed	2023-09-18T20:46:10Z
last_indexed	2023-09-18T20:46:10Z
_version_	1777409689468796928

Data mining technique on Cardioid graph based ECG biometric authentication

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