Implementation of Kernel Sparse Representation Classifier for ECG Biometric System

Authors

  • Haryati Jaafar Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.
  • Nurhidayah Ramli Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.
  • Aimi Salihah Abdul Nasir Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.

Keywords:

Autocorrelation Method, ECG Signal, KSRC, SRC, STE and STAZCR.

Abstract

In this paper, a biometric human recognition system based on Electrocardiography (ECG) signal is proposed. Three processes i.e., pre-processing, feature extraction and classification is discussed. A combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rate (STAZCR) is employed in the pre-processing. Subsequently, an autocorrelation method is applied in feature extraction. For the classification process, the kernel sparse representation classifier (KSRC) is proposed as a classifier to increase the system performance in high dimensional feature space. 79 recorded signals from 79 subjects are used are employed in this study. To validate the performance of the KSRC, several classifiers, i.e. sparse representation classifier (SRC), k nearest neighbor (kNN) and support vector machine (SVM) are compared. An experiment based on different sizes of feature dimensions is conducted. The classification performance for four classifiers are found to be 90.93%, 92.8%, 94.24%, 62.9%, 97.23% and 95.87% for the kNN, SVM (Polynomial and RBF), SRC and KSRC (Polynomial and RBF), respectively. The results reveal that the KSRC is a promising classifier for the ECG biometric system compared to the existing reference classifiers.

Downloads

Published

2018-05-29

How to Cite

Jaafar, H., Ramli, N., & Abdul Nasir, A. S. (2018). Implementation of Kernel Sparse Representation Classifier for ECG Biometric System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 89–94. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4128

Issue

Vol. 10 No. 1-13: Exploring Man-Machines Compliance in Signal Processing and Artificial Intelligence

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.