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Fault Tolerant Neural Network for ECG Signal Classification SystemsMERAH, M. , OUAMRI, A. , NAIT-ALI, A. , KECHE, M.
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fault tolerant, artificial neural networks, hybrid backpropagation algorithms, medical diagnosis
neural(19), networks(13), network(5), learning(5), fault(5), systems(4), algorithms(4)
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About this article
Date of Publication: 2011-08-31
Volume 11, Issue 3, Year 2011, On page(s): 17 - 24
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.03003
Web of Science Accession Number: 000296186700003
SCOPUS ID: 80055082608
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.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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