Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Random Forest Based Abnormal ECG Dichotomization using Linear and Nonlinear Feature Extraction

선형-비선형 특징추출에 의한 비정상 심전도 신호의 랜덤포레스트 기반 분류

  • Kim, Hye-Jin (Department of Medical Engineering, College of Medicine, Yonsei University) ;
  • Kim, Byeong-Nam (Department of Medical Engineering, College of Medicine, Yonsei University) ;
  • Jang, Won-Seuk (Department of Medical Engineering, College of Medicine, Yonsei University) ;
  • Yoo, Sun-K. (Department of Medical Engineering, College of Medicine, Yonsei University)
  • 김혜진 (연세대학교 의과대학 의학공학교실) ;
  • 김병남 (연세대학교 의과대학 의학공학교실) ;
  • 장원석 (연세대학교 의과대학 의학공학교실) ;
  • 유선국 (연세대학교 의과대학 의학공학교실)
  • Received : 2015.10.15
  • Accepted : 2016.03.23
  • Published : 2016.04.30
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Abstract

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

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References

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