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

被引：0

作者：

Sadr, Ali ^{[1
]}

Mohsenifar, Najmeh ^{[2
]}

Okhovat, Raziyeh Sadat ^{[2
]}

机构：

[1] Iran Univ Sci & Technol, Dept Elect Engn, Nondestruct Testing NDT Lab, Tehran, Iran

[2] Iran Univ Sci & Technol, Dept Elect Engn, Tehran, Iran

来源：

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2011年 / 11卷 / 11期

关键词：

electrocardiogram; artificial neural network; predict; accuracy;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：124 / 128

页数：5

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