International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Support Vector Machine Based Arrhythmia Classification Using Reduced Features

  • Song, Mi-Hye (Department of Biomedical Engineering, College of Health Science, Center for Emergency Medical Informatics (CEMI), Yonsei University) ;
  • Lee, Jeon (Department of Biomedical Engineering, College of Health Science, Center for Emergency Medical Informatics (CEMI), Yonsei University) ;
  • Cho, Sung-Pil (Department of Biomedical Engineering, College of Health Science, Center for Emergency Medical Informatics (CEMI), Yonsei University) ;
  • Lee, Kyoung-Joung (Department of Biomedical Engineering, College of Health Science, Center for Emergency Medical Informatics (CEMI), Yonsei University) ;
  • Yoo, Sun-Kook (Department of Medical Engineering, Center for Emergency Medical Informatics, College of Medicine, Yonsei University)
  • Published : 2005.12.01
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Abstract

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

Keywords

References

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