Comparison of PVC Arrhythmia Detection Via Neural Networks and ANFIS

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 Premature Ventricular Contraction 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. Finally we use a neural network and an ANFIS as a classifier satisfactory result obtained with an accuracy rate of 99.8% for neural network classifier and 97.8673% for ANFIS classifier.