Classification of PVC Beat in ECG Using Basic Temporal Features

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

References

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