Support vector machine based arrhythmia classification using reduced features

Mi Hye Song, Jeon Lee, Sung Pil Cho, Kyoung Joung Lee, Sun Kook Yoo

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)571-579
Number of pages9
JournalInternational Journal of Control, Automation and Systems
Issue number4
StatePublished - Dec 2005
Externally publishedYes


  • Arrhythmia classification
  • Linear discriminant analysis
  • Reduction of feature dimension
  • Support vector machine
  • Wavelet transform

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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