Open AccessPremature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy
Author(s) -
Achmad Rizal,
Riandini,
Teni Tresnawati
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.44.26975
Subject(s) - support vector machine , pattern recognition (psychology) , wavelet , artificial intelligence , beat (acoustics) , normalization (sociology) , speech recognition , wavelet transform , computer science , mathematics , acoustics , physics , sociology , anthropology
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.