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Reseach Article

Arrithmia Analysis Using Artificial Neural Network

Published on None 2011 by Gajanan P. Dhok, S.A. Ladhake
journal_cover_thumbnail
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 5
None 2011
Authors: Gajanan P. Dhok, S.A. Ladhake
208c3117-a001-41bc-b6e9-f2e55f28fee6

Gajanan P. Dhok, S.A. Ladhake . Arrithmia Analysis Using Artificial Neural Network. International Conference and Workshop on Emerging Trends in Technology. ICWET, 5 (None 2011), 46-52.

@article{
author = { Gajanan P. Dhok, S.A. Ladhake },
title = { Arrithmia Analysis Using Artificial Neural Network },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 5 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 46-52 },
numpages = 7,
url = { /proceedings/icwet/number5/2096-bm46/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Gajanan P. Dhok
%A S.A. Ladhake
%T Arrithmia Analysis Using Artificial Neural Network
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 5
%P 46-52
%D 2011
%I International Journal of Computer Applications
Abstract

This work shows an effective application of Artificial Neural Network using the modified approach to support supervised learning, and the evaluation of its performance in the classification of R-R Interval of the Electrocardiogram (ECG) from patients with cardiac arrhythmias. A second aim of this study is to investigate the ability of ANN to classify R-R Interval when the original data samples are used as input variables. The classifier is developed and tested with the MIT-BIH Arrhythmia Database. The obtained results become equivalent to the most sophisticated methods in the literature when input data are properly pre-processed and the final classifier is allowed to adapt to the normal pattern of each analyzed patient.

References
  1. N. Kumaravel, K. S. Sridhar, N. Nithiyanandan. ‘Automatic diagnosis of Heart diseases using neural network.’ IEEE, Southern Biomedical Engineering conference, Piscataway, USA, pp 319-322, 1996.
  2. K. Sutherland, R. Desilva, R. G. Will. ‘Clinical diagnosis of Crentzfeldt-Jakob disease using multi-layer perceptron neural classifier.’ Journal of Intelligent systems, Vol.7, No.2, U. K., pp 1-18, 1997.
  3. R. L. Kennedy, R. F. Harrison, A. M. Burton. ‘Artificial neural network system for the diagnosis of acute myocardiai infraction (AMI) in the accident & emergency department’. Computer Methods and Programs in Biomedicine, Vol. 52, No. 2, Sunderland, U. K. pp. 93-103, 1997.
  4. Tang, K. Wendy, Pingle. ‘Artificial neural networks for the diagnosis of Coronary Artery disease.’ Journal of Intelligent system, Vol.- 07, No. USA, pp. 307-338, 1997.
  5. D. L. Bounds, P. J. Lioyd, B. Mathew & G. G. Wadell. ‘A multilayer perceptron network for the diagnosis of low back pain.’ Proceeding of the IEEE International conference on neural networks, san diego, vol.II, IEEE, New York. Pp. 481-489, 1988.
  6. Y. O. Yoon, R. W. Brobst, P. R. Bergstresser & L. L. Peterson. ‘A desktop neural network for dermatology diagnosis.’ Journal of neural network computing, summer, pp- 43-52, 1989.
  7. W. G. Baxt. ‘Use of artificial neural network for data analysis in clinical decision- making: The diagnosis of acute coronary occlusion.’ Neural computation, pp. 480-189, April 1990.
  8. Leslie Cromwell,Fred J. Weibell, Erich A. Pfeiffer ‘ Biomedical Instrumentation and Measurement’ PHI 2001 second edition
  9. D.C. Reddy ‘ Biomedical signal processing’ TMH2005
  10. Willis J. Tompkins ‘ Biomedical digital Signal processing’ PHI 2002
  11. Joseph J. Carr , John M. Brown ‘ Introduction to biomedical equipment technology’ fourth edition Pearson education
  12. R.S. Khandpur ‘ Hand book of Biomedical Instrumentation’ TMH
  13. John G. Webster ‘ Medical Instrumentation Application and design ‘ Third Edition
  14. Bart Kosko 'Neural Networks and Fuzzy systems' Prentice Hall Of India
  15. Limin Fu ' Neural Networks in Computer Intelligence' Tata Megraw Hill Publication.
  16. Satish Kumar 'Neural Networks' Tata Megraw Hill Publication.
  17. Simon Haykin 'Neural Networks' Second Edition, Prentice Hall Of India.
  18. S. N. Sivanadam, S. Sumathi, S. N. Deppa 'Introduction to Neural Networks using Mathlab 6.0' Tata Megraw Hill Publication.
  19. Orlando De jesus and martin T Hagan 'Backpropagation algorithms for Broad class of dynmaic Network' A publication of the IEEE Computational Intelligence Society, volume 18 January 2007.
Index Terms

Computer Science
Information Sciences

Keywords

ECG Artificial Neural Network Back propagation algorithm Training Learning parameter RBF