Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Detection of Abnormal Heartbeat using Hierarchical Qassification in ECG

계층구조적 분류모델을 이용한 심전도에서의 비정상 비트 검출

  • Lee, Do-Hoon (Department of Biomedical Engineering, Hanyang University) ;
  • Cho, Baek-Hwan (Department of Biomedical Engineering, Hanyang University) ;
  • Park, Kwan-Soo (Department of Biomedical Engineering, Hanyang University) ;
  • Song, Soo-Hwa (Department of Biomedical Engineering, Hanyang University) ;
  • Lee, Jong-Shill (Department of Biomedical Engineering, Hanyang University) ;
  • Chee, Young-Joon (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, In-Young (Department of Biomedical Engineering, Hanyang University) ;
  • Kim, Sun-Il (Department of Biomedical Engineering, Hanyang University)
  • 이도훈 (한양대학교 의용생체공학과) ;
  • 조백환 (한양대학교 의용생체공학과) ;
  • 박관수 (한양대학교 의용생체공학과) ;
  • 송수화 (한양대학교 의용생체공학과) ;
  • 이종실 (한양대학교 의용생체공학과) ;
  • 지영준 (한양대학교 의용생체공학과) ;
  • 김인영 (한양대학교 의용생체공학과) ;
  • 김선일 (한양대학교 의용생체공학과)
  • Published : 2008.12.31
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Abstract

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

Keywords