PVC Arrhythmia Detection Using Neural Networks

Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features from MIT/BIH ECG signals database and then applying LDA algorithm, we reduced them into nine features. Using a neural network for classification, satisfactory results obtained with an accuracy rate of 99.8%.