Data mining technique on cardioid graph based ECG biometric authentication

Sidek, K, Sufi, F and Khalil, I 2011, 'Data mining technique on cardioid graph based ECG biometric authentication', in Christian Baumgartner (ed.) Biomedical Engineering 2011, Innsbruck, Austria, 16-18 February 2011, pp. 53-57.

Document type: Conference Paper
Collection: Conference Papers

Title Data mining technique on cardioid graph based ECG biometric authentication
Author(s) Sidek, K
Sufi, F
Khalil, I
Year 2011
Conference name The Eighth IASTED International Conference on Biomedical Engineering
Conference location Innsbruck, Austria
Conference dates 16-18 February 2011
Proceedings title Biomedical Engineering 2011
Editor(s) Christian Baumgartner
Publisher ACTA Press
Place of publication Calgary, Canada
Start page 53
End page 57
Total pages 5
Abstract 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.
Subjects Biomedical Engineering not elsewhere classified
Keyword(s) Biomedical signal processing
ECG biometrics
Bayes network
DOI - identifier 10.2316/P.2011.723-144
ISBN 9780889868663
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Citation counts: Scopus Citation Count Cited 6 times in Scopus Article | Citations
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