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...

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Bibliographic Details
Published in:Journal of network and computer applications 2014-09, Vol.44, p.83-91
Main Authors: Sidek, Khairul Azami, Mai, Vu, Khalil, Ibrahim
Format: Article
Language:English
Subjects:
Android
Applied sciences
Biological and medical sciences
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Computer systems performance. Reliability
Data mining
Data processing. List processing. Character string processing
ECG
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
Exact sciences and technology
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Memory organisation. Data processing
Mobile biometric
QRS complex
Software
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Summary: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 experimentations 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.
ISSN:1084-8045
1095-8592
DOI:10.1016/j.jnca.2014.04.008