Performance evaluation of Radial Basis Function Neural Network on ECG beat classification

In this study radial basis function neural network (RBFNN) was trained by different methods to study performance of each method on classification of ECG beats. To train the neural networks six types of beats including, normal beat (N), premature ventricular contraction (PVC), fusion of ventricular and normal beat (F), atrial premature beat (A), right bundle branch blok beat (R), and fusion of paced and normal beat (f) were selected from the MIT-BIH arrhythmia database. Training of the neural networks were performed with a training set which includes 100 beats for each class. Four time domain (morphological) features were extracted from the beats for classification process. Then several experiments were performed over the test set, and it was observed that, combining RBFNN with different methods has a positive effect on the classification performance of ECG beats.