Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy
DOI:
https://doi.org/10.14419/ijet.v7i4.36.28993Keywords:
Premature ventricle contraction, electrocardiogram, multilevel wavelet entropy, support vector machine, arrhythmiaAbstract
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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Received date: April 25, 2019
Accepted date: April 25, 2019