Data mining in mobile ECG based biometric identification
This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (N...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier, USA
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf |
| _version_ | 1848781319598768128 |
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| author | Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim |
| author_facet | Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim |
| author_sort | Sidek, Khairul Azami |
| building | IIUM Repository |
| collection | Online Access |
| description | This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (NSRDB) were used to validate the reliability and stability of the subject recognition methods. Discriminatory features extracted from the experimentation were later applied to different classifiers for performance measures based on the complexity of our proposed sample extraction method when compared to other related algorithms, the total execution time (TET) applied on different classifiers in various mobile devices and the classification accuracies when applied to various classification techniques. Experimentation results showed that our method simplifies biometric identification process by obtaining reduced computational complexity when compared to other related algorithms. This is evident when TET values were significantly low on mobile devices as compared to a non-mobile device while maintaining high accuracy rates ranging from 98.30% to 99.07% in different classifiers. Therefore, these outcomes support the usability of ECG based biometric identification in a mobile environment. |
| first_indexed | 2025-11-14T15:47:41Z |
| format | Article |
| id | iium-36914 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:47:41Z |
| publishDate | 2014 |
| publisher | Elsevier, USA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-369142018-06-11T07:01:24Z http://irep.iium.edu.my/36914/ Data mining in mobile ECG based biometric identification Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim TK7885 Computer engineering This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (NSRDB) were used to validate the reliability and stability of the subject recognition methods. Discriminatory features extracted from the experimentation were later applied to different classifiers for performance measures based on the complexity of our proposed sample extraction method when compared to other related algorithms, the total execution time (TET) applied on different classifiers in various mobile devices and the classification accuracies when applied to various classification techniques. Experimentation results showed that our method simplifies biometric identification process by obtaining reduced computational complexity when compared to other related algorithms. This is evident when TET values were significantly low on mobile devices as compared to a non-mobile device while maintaining high accuracy rates ranging from 98.30% to 99.07% in different classifiers. Therefore, these outcomes support the usability of ECG based biometric identification in a mobile environment. Elsevier, USA 2014-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf Sidek, Khairul Azami and Mai, Vu and Khalil, Ibrahim (2014) Data mining in mobile ECG based biometric identification. Journal of Network and Computer Applications, 44. pp. 83-91. ISSN 1084-8045 http://www.sciencedirect.com/science/article/pii/S1084804514000915 10.1016/j.jnca.2014.04.008 |
| spellingShingle | TK7885 Computer engineering Sidek, Khairul Azami Mai, Vu Khalil, Ibrahim Data mining in mobile ECG based biometric identification |
| title | Data mining in mobile ECG based biometric identification |
| title_full | Data mining in mobile ECG based biometric identification |
| title_fullStr | Data mining in mobile ECG based biometric identification |
| title_full_unstemmed | Data mining in mobile ECG based biometric identification |
| title_short | Data mining in mobile ECG based biometric identification |
| title_sort | data mining in mobile ecg based biometric identification |
| topic | TK7885 Computer engineering |
| url | http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/ http://irep.iium.edu.my/36914/1/JNCA_published11June2014.pdf |