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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2011 - 3


Fault Tolerant Neural Network for ECG Signal Classification Systems

MERAH, M. See more information about MERAH, M. on SCOPUS See more information about MERAH, M. on IEEExplore See more information about MERAH, M. on Web of Science, OUAMRI, A. See more information about  OUAMRI, A. on SCOPUS See more information about  OUAMRI, A. on SCOPUS See more information about OUAMRI, A. on Web of Science, NAIT-ALI, A. See more information about  NAIT-ALI, A. on SCOPUS See more information about  NAIT-ALI, A. on SCOPUS See more information about NAIT-ALI, A. on Web of Science, KECHE, M. See more information about KECHE, M. on SCOPUS See more information about KECHE, M. on SCOPUS See more information about KECHE, M. on Web of Science
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Download PDF pdficon (1,855 KB) | Citation | Downloads: 1,153 | Views: 3,789

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

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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.

References | Cited By  «-- Click to see who has cited this paper

[1] I. Splawski, J. Shen, K.W. Timothy, G.M. Vincent, M.H. Lehmann, MT. Keating, "Genomic structure of three long QT syndrome genes," Kvlqt, Herg, and Kcne1. Genomics, No. 50, pp. 86-97, 1998,
[CrossRef] [Web of Science Times Cited 184] [SCOPUS Times Cited 213]

[2] C. J. James, C. W. Hesse, "Independent component analysis for biomedical signals," Physiol Meas, No. 26, Pp.15-39, 2005,
[CrossRef] [Web of Science Times Cited 256] [SCOPUS Times Cited 316]

[3] C. Chui, K. Mehrotra, K. M. Chilukuri, R. Sanjay, "Modifying Training Algorithms for Improved Fault Tolerance," IEEE International Conference on Neural Networks, Florida, pp. 333-338, 1994,

[4] A. Hyvarinen, E. Oja, "Independent component analysis: algorithms and applications," Neural Network, No. 13, pp. 411-430, 2000,
[CrossRef] [Web of Science Times Cited 4297] [SCOPUS Times Cited 5293]

[5] T. Y. Kwok, D. Y. Yeung, "Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems," IEEE Trans. Neural Networks, Vol. 8, No. 3, pp. 630-645, May 1997,
[CrossRef] [Web of Science Times Cited 293] [SCOPUS Times Cited 355]

[6] F. L. Luo, "Applied Neural Networks for Signal Processing," Cambridge Univ. Press, Cambridge, Mass., 1999.

[7] C. Campbell, "Constructive learning techniques for designing neural network systems," In CT Leondes, editor, Neural Network Systems Technologies and Applications. Academic Press, 1997.

[8] M. L. Nasir, R.I. John, S.C. Bennett, "Selecting the neural network topology for student modelling of prediction of corporate bankruptcy, " Campus-Wide Information Systems, Vol. 18, No. 1, pp. 13 - 22, 2001,
[CrossRef] [SCOPUS Times Cited 4]

[9] F. BLAYO, "Reseaux de neurones artificiels du laboratoire au marche industriel," SAMOS (Statistiques Appliquees et Modelisation Stochastiques), Universite Paris1, Pantheon Sorbonne 1998.

[10] S. John, C. L. Andrew , "Prediction error of a fault tolerant neural network," Neurocomputing, Vol. 72, No.3, pp. 653-658, December 2008,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]

[11] C. S. Lin, I. C. Wu, "Maximizing Fault Tolerance in Multilayer Neural Networks", IEEE International Conference on Neural Networks, Florida, pp. 419-424 , 1994,

[12] T. Kurita, H. Asoh, S. Umeyama, A. Hosomi, "A structural Learning by Adding Independant Noises to Hidden Units", IEEE International Conference in Neural Networks, Florida, pp. 275-278, 1994,

[13] A. S . Weigend, D.E. Rumelhart, A.B. Huberman, "Generalization by Weight-Elimination applied to Currency Exchange Rate Prediction," IEEE International Conference on Neural Networks, Vol. 1, pp. 837-841, 1991,

[14] D. G. Jeong, S.Y. Lee, "Merging back-propagation and Hebbian learning rules for robust classifications," Neural Networks, Vol. 9, pp. 1213-1222, 1996,
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 31]

[15] W. Finnoff, F. Hergert" Improving model selection by non convergent methods, " Neural Networks, Vol. 6, pp. 771-783, 1993,
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 126]

[16] A. Korgh, J.A. Hertz, "A simple weight decay can improve generalization," Advances in neural information processing systems, San Mateo, CA, Morgan Kaumann, Vol. 4, pp. 950 - 957, 1992.

[17] Y. LE Cun, JS. Denker, S.A. Solla, "Optimal brain damage," Adv. In Neural Info. Proc. Sys, Morgan Kaufmann, Vol. 2, pp. 598-605, 1990.

[18] B. E. Segee, M.J. Carter, "Fault tolerance of pruned multilayer networks," Digest IJCNN, Vol. 2, pp. 447 - 452, 1991,

[19] L. Prechelt, "Connection pruning with static and adaptive pruning schedules," Fakultät für Informatik, Universität Karlsruhe, Germany, 8 Nov. 1995,
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 23]

[20] N. C. Hammadi, I. Hideo, "A Leaning Algorithm for Fault Tolerant Feedforward Neural Networks," Chiba Univesity, Chiba-shi, Japan, pp. 263, 1996.

[21] M. Merah, B. Nacredine "Algorithme de retro-propagation du gradient avec Penalisation des poids R.P.G.P. » CNIE, USTO, 15-16 December 2002.

[22] S. Y. Jeong, S. Y. Lee, "Adaptive learning algorithms to incorporate additional functional constraints into neural networks," Neurocomputing, Vol. 35, pp. 73-90, 2000,
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 27]

[23] M. Merah, A. Ouamri, "Analyse et traitements de l'ECG pour la conception d'une base d'apprentissage d'un R.N.A," The 3rd International Summer School on Signal Processing and its Applications, Jijel, Algeria, pp. 08-12, July 2006.

References Weight

Web of Science® Citations for all references: 5,222 TCR
SCOPUS® Citations for all references: 6,390 TCR

Web of Science® Average Citations per reference: 218 ACR
SCOPUS® Average Citations per reference: 266 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-10-22 14:45 in 104 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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