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

  • Authors

    • Achmad Rizal
    • Riandini .
    • Teni Tresnawati
    https://doi.org/10.14419/ijet.v7i4.36.28993
  • Premature ventricle contraction, electrocardiogram, multilevel wavelet entropy, support vector machine, arrhythmia

  • 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.

     

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  • How to Cite

    Rizal, A., ., R., & Tresnawati, T. (2018). Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy. International Journal of Engineering & Technology, 7(4.36), 1391-1394. https://doi.org/10.14419/ijet.v7i4.36.28993

    Received date: 2019-04-25

    Accepted date: 2019-04-25