Fault Tolerant Neural Network for ECG Signal Classification Systems

被引：5

作者：

Merah, Mostefa ^{[1
,2
]}

Ouamri, Abdelazziz ^{[2
]}

Nait-Ali, Amine ^{[1
]}

Keche, Mokhtar ^{[2
]}

机构：

[1] Univ Paris 12, LISSI, EA 3956, F-94010 Creteil, France

[2] Univ MB, LSI, USTO, Oran El Mnouar, Algeria

来源：

ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING | 2011年 / 11卷 / 03期

关键词：

Fault tolerant; artificial neural networks; hybrid backpropagation algorithms; medical diagnosis; INDEPENDENT COMPONENT ANALYSIS; ALGORITHMS;

D O I：

10.4316/AECE.2011.03003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN) for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT - BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

引用

页码：17 / 24

页数：8

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