Electrocardiogram based identification using a new effective intelligent selection of fused features
Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in s...
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doaj-64815efd82454d82a99beb148a056e9a2020-11-25T00:12:02ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772015-01-01513039Electrocardiogram based identification using a new effective intelligent selection of fused featuresHamidreza AbbaspourSeyyed Mohammad RazaviNasser MehrshadOver the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2015;volume=5;issue=1;spage=30;epage=39;aulast=AbbaspourBiometricsidentificationelectrocardiogramgenetic algorithmneural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hamidreza Abbaspour Seyyed Mohammad Razavi Nasser Mehrshad |
spellingShingle |
Hamidreza Abbaspour Seyyed Mohammad Razavi Nasser Mehrshad Electrocardiogram based identification using a new effective intelligent selection of fused features Journal of Medical Signals and Sensors Biometrics identification electrocardiogram genetic algorithm neural networks |
author_facet |
Hamidreza Abbaspour Seyyed Mohammad Razavi Nasser Mehrshad |
author_sort |
Hamidreza Abbaspour |
title |
Electrocardiogram based identification using a new effective intelligent selection of fused features |
title_short |
Electrocardiogram based identification using a new effective intelligent selection of fused features |
title_full |
Electrocardiogram based identification using a new effective intelligent selection of fused features |
title_fullStr |
Electrocardiogram based identification using a new effective intelligent selection of fused features |
title_full_unstemmed |
Electrocardiogram based identification using a new effective intelligent selection of fused features |
title_sort |
electrocardiogram based identification using a new effective intelligent selection of fused features |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Medical Signals and Sensors |
issn |
2228-7477 |
publishDate |
2015-01-01 |
description |
Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task. |
topic |
Biometrics identification electrocardiogram genetic algorithm neural networks |
url |
http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2015;volume=5;issue=1;spage=30;epage=39;aulast=Abbaspour |
work_keys_str_mv |
AT hamidrezaabbaspour electrocardiogrambasedidentificationusinganeweffectiveintelligentselectionoffusedfeatures AT seyyedmohammadrazavi electrocardiogrambasedidentificationusinganeweffectiveintelligentselectionoffusedfeatures AT nassermehrshad electrocardiogrambasedidentificationusinganeweffectiveintelligentselectionoffusedfeatures |
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