Comparison of MLP and RBF neural networks for Prediction of ECG Signals
Ali Sadr, Najmeh Mohsenifar, Raziyeh Sadat Okhovat · 2011
Summary In this paper, we investigate the performance of MLP and RBF neural networks in terms of ECG signal prediction. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electrocardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that RBF neural network reconstructs ECG signals with 94% accuracy which is 2% better than MLP architecture.