Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines

Chia Ping Shen, Wen Chung Kao*, Yueh Yiing Yang, Ming Chai Hsu, Yuan Ting Wu, Feipei Lai

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

46 引文 斯高帕斯(Scopus)

摘要

The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. The experimental results show that the proposed ECG analysis approach can obtain a higher recognition rate than the published approaches. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 98.92%, and the recognition rate for each class is kept above 92%.

原文英語
頁(從 - 到)7845-7852
頁數8
期刊Expert Systems with Applications
39
發行號9
DOIs
出版狀態已發佈 - 2012 七月

ASJC Scopus subject areas

  • 工程 (全部)
  • 電腦科學應用
  • 人工智慧

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