Comparison of MLP and RBF neural networks for Prediction of ECG Signals

摘要

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.