Application of data mining on polynomial based approach for ECG biometric

Sidek, K and Khalil, I 2011, 'Application of data mining on polynomial based approach for ECG biometric', in Noor Azuan Abu Osman, Wan Abu Bakar Wan Abas, Ahmad Khairi Abdul Wahab, Hua-Nong Ting (ed.) Proceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering 2011, Volume 35, Kuala Lumpur, Malaysia, 20-23 June 2011, pp. 476-479.


Document type: Conference Paper
Collection: Conference Papers

Title Application of data mining on polynomial based approach for ECG biometric
Author(s) Sidek, K
Khalil, I
Year 2011
Conference name BIOMED 2011, IFMBE Proceedings
Conference location Kuala Lumpur, Malaysia
Conference dates 20-23 June 2011
Proceedings title Proceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering 2011, Volume 35
Editor(s) Noor Azuan Abu Osman, Wan Abu Bakar Wan Abas, Ahmad Khairi Abdul Wahab, Hua-Nong Ting
Publisher Springer
Place of publication Heidelberg, Germany
Start page 476
End page 479
Total pages 4
Abstract In this paper, the application of data mining techniques on polynomial based approach for better electrocardiogram (ECG) authentication mechanism is presented. Polynomials being used for ECG data processing have a history of nearly two decades. Recently it has been bringing about promising solutions for heart beat recognition problem. General polynomial based approach are used in this research and by using the polynomial coefficients extracted as unique features from the ECG signals, data mining techniques was applied for person identification. A total of 18 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) were used for development and evaluation. QRS complexes from each dataset was divided into two parts, the training and the testing dataset which was used to prove the validity of the data mining technique applied. Experimental results was classified using Multilayer Perceptron (MLP) in order to confirm the identity of an individual and was compared with the previous research using polynomials without the use of data mining technique. Our experimentation on a public ECG database suggest that the proposed data mining technique on polynomial based approach significantly improves the identification accuracy by 96% as compared to 87% from the existing study.
Subjects Pattern Recognition and Data Mining
Keyword(s) ECG
biometrics
polynomial
Multilayer Perceptron
Neural Networks
DOI - identifier 10.1007/978-3-642-21729-6_120
Copyright notice © 2011 Springer
ISBN 9783642217296
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