Support Vector Machine Based Arrhythmia Classification Using Reduced Features

@inproceedings{Song2005SupportVM,
  title={Support Vector Machine Based Arrhythmia Classification Using Reduced Features},
  author={Mi Hye Song and Jeon Lee and Sung Pil Cho and Kyoung Joung Lee and Sun Kook Yoo},
  year={2005}
}
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… CONTINUE READING
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