Classification of PVC Beat in ECG Using Basic Temporal Features

Year 2018, Volume: 6 Issue: 2, 78 - 82, 30.04.2018

Yasin Kaya

https://doi.org/10.17694/bajece.419541

Cited By: 7

Abstract

Premature ventricular contraction (PVC) is
one of the most important arrhythmias among the various hearth abnormalities. Premature
depolarization of the myocardium in the ventricular region causes PVC and it is
usually associated with
structural heart conditions. Arrhythmias can be detected by examining the ECG
signal and this review requires large-size data to be examined by physicians. The
time spent by the physician in examining the signal can be reduced using CAD
systems. In this study, we propose a high performance
PVC detection system using the feature extraction and classification scheme
bringing low computational burden. The test set consisting of 81844 beats from
the MIT-BIH arrhythmia database was used for the experimental results. We
compared the performances of the various classifiers using proposed feature set
in the experiments and obtained classification accuracy of 98.71% using NN
classifier. 

Keywords

Arrhythmia, Classification, Decision Tree, Heartbeat, k-Nearest Neighbor, k-NN, Neural Network, Premature Ventricular Contraction, PVC, Support Vector Machine

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