Volume 4 Number 5 (Oct. 2012)
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IJCEE 2012 Vol.4(5): 762-765 ISSN: 1793-8163 DOI: 10.7763/IJCEE.2012.V4.601

Cardiac Arrhythmia Classification using Beat-by-Beat Autoregressive Modeling

Chusak Thanawattano and Thaweesak Yingthawornsuk

Abstract—This paper presents the classification of the cardiac arrhythmia whose features are extracted by applying the autoregressive signal modeling. The different coefficients of the 4th to 9th-order AR models were tested with the support vector machine as one-against-the-rest approach for each arrhythmia in order to investigate their performance. The results lead to the proposed method where the first step is to apply APC-against-non-APC SVM on the extracted AR features. Secondly, the PVC-against-Normal SVM classifier is applied to the non-APC feature classified from the first step. The mean value of the best overall accuracy is 94.40% which is obtained when the 6th-order AR model is applied to modeling. The APC-against-non-APC classification alone provides the high accuracy of 97.11%.

Index Terms—Electrocardiography, support vector machine, classification, autoregressive estimation.

C. Thanawattano is with National Electronic and Computer Technology Center, Pathumthani, Thailand (e-mail: chusak.tha@nectec.or.th ).
T. Yingthawornsuk is with Department of Media Technology, King Mongkut’s University of Technology Thonburi – Bang Khunthian Campus, Bangkok, Thailand (e-mail: thaweesak.yin@kmutt.ac.th).

Cite: Chusak Thanawattano and Thaweesak Yingthawornsuk, "Cardiac Arrhythmia Classification using Beat-by-Beat Autoregressive Modeling," International Journal of Computer and Electrical Engineering vol. 4, no. 5, pp. 762-765, 2012.


General Information

ISSN: 1793-8163 (Print)
Abbreviated Title: Int. J. Comput. Electr. Eng.
Frequency: Quarterly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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