Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

  • Abstract
  • Keywords
  • References
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  • Abstract

    One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.



  • Keywords

    Premature ventricle contraction; electrocardiogram; multilevel wavelet entropy; support vector machine; arrhythmia

  • References

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Article ID: 26975
DOI: 10.14419/ijet.v7i4.44.26975

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