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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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Author keywords
fault tolerant, artificial neural networks, hybrid backpropagation algorithms, medical diagnosis
References keywords
neural(19), networks(13), network(5), learning(5), fault(5), systems(4), algorithms(4)
Blue keywords are present in both the references section and the paper title.
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
Abstract
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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[1] Lenin, Mao and Aidit, van der Kroef, Justus M., The China Quarterly, ISSN 0305-7410, Issue , 1962.
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[2] Detection of electrocardiogram characteristic points using lifting wavelet transform and Hilbert transform, Li, Hongqiang, Wang, Xiaofei, Transactions of the Institute of Measurement and Control, ISSN 0142-3312, Issue 5, Volume 35, 2013.
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[3] R-peaks detection based on stationary wavelet transform, Merah, M., Abdelmalik, T.A., Larbi, B.H., Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue 3, Volume 121, 2015.
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[4] A New Method for EEG Compressive Sensing, FIRA, M., GORAS, L., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 12, 2012.
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[5] Development of a Medical Care Terminal for Efficient Monitoring of Bedridden Subjects, Pereira, Filipe, Carvalho, Vítor, Soares, Filomena, Machado, José, Bezerra, Karolina, Silva, Rui, Matos, Demétrio, Journal of Engineering, ISSN 2314-4904, Issue , 2016.
Digital Object Identifier: 10.1155/2016/3591059 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
