An effective ECG arrhythmia classification algorithm

Authors:
Jeen-Shing Wang;Wei-Chun Chiang;Ya-Ting C. Yang;Yu-Liang Hsu
Affiliations:
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Institute of Education & Center for Teacher Education, National Cheng Kung University, Tainan, Taiwan, R.O.C.;Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme consisting of a feature reduction method combining principal component analysis (PCA) with linear discriminant analysis (LDA), and a probabilistic neural network (PNN) classifier to discriminate eight different types of arrhythmia from ECG beats. Each ECG beat sample composed of 200 sampling points at a 360 Hz sampling rate around an R peak is extracted from ECG signals. The feature reduction method is employed to find important features from ECG beats, and to improve the classification accuracy of the classifier. With the features, the PNN is then trained to serve as classifier for discriminating eight different types of ECG beats. The average classification accuracy of the proposed scheme is 99.71%. Our experimental results have successfully validated the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.