Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals

Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals

In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method...

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Main Authors: Sidek, Khairul Azami, Khalil, Ibrahim
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/31998/
http://irep.iium.edu.my/31998/
http://irep.iium.edu.my/31998/1/embc2012.pdf
id iium-31998
recordtype eprints
spelling iium-319982013-09-11T06:56:32Z http://irep.iium.edu.my/31998/ Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals Sidek, Khairul Azami Khalil, Ibrahim TK7885 Computer engineering In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method using Euclidean distance could be further improved by using Mahalanobis distance measurement producing extracted coefficients which takes into account the correlations of the data set. Identification is then done by applying these extracted features to Radial Basis Function Network. A total of 30 ECG data from MITBIH Normal Sinus Rhythm database (NSRDB) and MITBIH Arrhythmia database (MITDB) were used for development and evaluation purposes. Our experimentation results suggest that the proposed feature extraction method has significantly increased the classification performance of subjects in both databases with accuracy from 97.50% to 99.80% in NSRDB and 96.50% to 99.40% in MITDB. High sensitivity, specificity and positive predictive value of 99.17%, 99.91% and 99.23% for NSRDB and 99.30%, 99.90% and 99.40% for MITDB also validates the proposed method. This result also indicates that the right feature extraction technique plays a vital role in determining the persistency of the classification accuracy for Cardioid based person identification mechanism. 2012-08-28 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/31998/1/embc2012.pdf Sidek, Khairul Azami and Khalil, Ibrahim (2012) Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals. In: 34th Annual International Conference of the IEEE EMBS, 28th August - 1st September 2012, San Diego, California. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6346694
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
Khalil, Ibrahim
Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
description In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method using Euclidean distance could be further improved by using Mahalanobis distance measurement producing extracted coefficients which takes into account the correlations of the data set. Identification is then done by applying these extracted features to Radial Basis Function Network. A total of 30 ECG data from MITBIH Normal Sinus Rhythm database (NSRDB) and MITBIH Arrhythmia database (MITDB) were used for development and evaluation purposes. Our experimentation results suggest that the proposed feature extraction method has significantly increased the classification performance of subjects in both databases with accuracy from 97.50% to 99.80% in NSRDB and 96.50% to 99.40% in MITDB. High sensitivity, specificity and positive predictive value of 99.17%, 99.91% and 99.23% for NSRDB and 99.30%, 99.90% and 99.40% for MITDB also validates the proposed method. This result also indicates that the right feature extraction technique plays a vital role in determining the persistency of the classification accuracy for Cardioid based person identification mechanism.
format Conference or Workshop Item
author Sidek, Khairul Azami
Khalil, Ibrahim
author_facet Sidek, Khairul Azami
Khalil, Ibrahim
author_sort Sidek, Khairul Azami
title Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
title_short Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
title_full Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
title_fullStr Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
title_full_unstemmed Biometric sample extraction using Mahalanobis distance in cardioid based graph using electrocardiogram signals
title_sort biometric sample extraction using mahalanobis distance in cardioid based graph using electrocardiogram signals
publishDate 2012
url http://irep.iium.edu.my/31998/
http://irep.iium.edu.my/31998/
http://irep.iium.edu.my/31998/1/embc2012.pdf
first_indexed 2018-09-07T05:23:27Z
last_indexed 2018-09-07T05:23:27Z
_version_ 1610925010105401344

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