International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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The Classification of Electrocardiograph Arrhythmia Patterns using Fuzzy Support Vector Machines

  • Lee, Soo-Yong (Dept. of General Education and Teacher Training, College of Humanities & Arts, Yonsei Univ.) ;
  • Ahn, Deok-Yong (Dept. of Computer Science, Graduate School of Engineering, Yonsei University) ;
  • Song, Mi-Hae (Dept. of Biomedical Engineering, College of Health Sciences, Yonsei University) ;
  • Lee, Kyoung-Joung (Dept. of Biomedical Engineering, College of Health Sciences, Yonsei University)
  • Received : 2011.08.19
  • Accepted : 2011.09.08
  • Published : 2011.09.25
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Abstract

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

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References

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