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Classification of ECG arrhythmias based on statistical and time-frequency features

Kadbi, M. H ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1049/cp:20060376
  3. Publisher: 2006
  4. Abstract:
  5. In this paper a new approach to accurately classify ECG arrhythmias through a combination of the wavelet transform and artificial neural network is presented. Three kinds of features in a very computationally efficient manner are computed as follows: 1-Joint time-frequency features (discrete wavelet transform coefficients). 2-Time domain features (R-R intervals). 3-Statistical feature (form factor). Using these features, the limitations of other methods in classifying multiple kinds of arrhythmia with high accuracy for all of them at once are overcome. Finally, a cascade classifier including two ANNs has been designed. Considering the whole MIT-BIH arrhythmia database, 10 kinds of arrhythmia were classified. The overall accuracy of classification of the proposed approach is above 90%
  6. Keywords:
  7. Feature extraction ; Natural frequencies ; Neural networks ; Statistical methods ; Time domain analysis ; Wavelet transforms ; Arrhythmia ; Form factors ; Time frequency features ; Wavelet coefficients ; Electrocardiography
  8. Source: IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing, Glasgow, 17 July 2006 through 19 July 2006 ; Issue 520 , 2006 , Pages 24- ; 0863416586 (ISBN); 9780863416583 (ISBN)
  9. URL: https://digital-library.theiet.org/content/conferences/10.1049/cp_20060376