An effective ECG arrhythmia classification algorithm

研究成果: Conference contribution

11 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
頁面545-550
頁數6
DOIs
出版狀態Published - 2011 十二月 1
事件7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
持續時間: 2011 八月 112011 八月 14

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6840 LNBI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other7th International Conference on Intelligent Computing, ICIC 2011
國家China
城市Zhengzhou
期間11-08-1111-08-14

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

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