CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data

by Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 15
Year of Publication: 2012
Authors: Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol
10.5120/6338-8532

Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol . Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data. International Journal of Computer Applications. 44, 15 ( April 2012), 8-13. DOI=10.5120/6338-8532

@article{ 10.5120/6338-8532,
author = { Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol },
title = { Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 15 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number15/6338-8532/ },
doi = { 10.5120/6338-8532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:37.919015+05:30
%A Shivajirao M. Jadhav
%A Sanjay L. Nalbalwar
%A Ashok A. Ghatol
%T Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 15
%P 8-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately causes irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia disease diagnosis system using standard 12 lead ECG signal recordings data. In this study, we are mainly interested in classifying disease in normal and abnormal classes. We have used UCI ECG signal data to train and test three different ANN models. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. ANN models are trained by static backpropagation algorithm with momentum learning rule to diagnose cardiac arrhythmia. The classification performance is evaluated using measures such as mean squared error (MSE), classification specificity, sensitivity, accuracy, receiver operating characteristics (ROC) and area under curve (AUC). Out of three different ANN models Multilayer perceptron ANN model have given very attractive classification results in terms of classification accuracy and sensitivity of 86. 67% and 93. 75% respectively while Modular ANN have given 93. 1% classification specificity

References
  1. Dale Dubin , MD , "Rapid Interpretation of EKG's", USA, ISBN: 9780912912066 2001
  2. Zuo, W. M. ; Lu, W. G. ; Wang, K. Q. ; Zhang, H. , "Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier", Computers in Cardiology 2008 , Page(s): 253 - 256
  3. G. D. Srinivasan N, Krishnan S. M. , "Cardiac arrhythmia classification using autoregressive modeling", BioMed Eng Online, 2002, 1(1):5
  4. Pooja Bhardwaj, Rahul R Choudhary and Ravindra Dayama. Article: Analysis and Classification of Cardiac Arrhythmia Using ECG Signals. International Journal of Computer Applications 38(1):37-40, January 2012. Published by Foundation of Computer Science, New York, USA
  5. Raut, R. D. ; Dudul, S. V. , "Arrhythmias Classification with MLP Neural Network and Statistical Analysis", First IEEE International Conference on Emerging Trends in Engineering and Technology, 2008, Page(s): 553 – 558
  6. G. Selvakumar, K. Boopathy Bagan, "Wavelet Decomposition for Detection and Classification of Critical ECG Arrhythmias", Proceeding of the 8th WSEAS International Conference on Mathematics and computers in Biology and Chemistry, June 2007, pp. 80-84.
  7. G. Selvakumar, K. Boopathy Bagan, "An Efficient QRS Complex Detection Algorithm using Optimal Wavelet", WSEAS Transactions on Signal Processing, Volume 2, Issue 8, August 2006, pp. 1069-1073.
  8. Sung-Nien Yu,Ying-Hsiang Chen, "Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network", Periodical Journal Pattern Recognition Letters, Elsevier Science Inc New York, NY, USA, Volume 28 , Issue 10, July 2007, pp. 1142-1150.
  9. Sung-Nien Yu,Ying-Hsiang Chen, "Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network", periodical journal Pattern Recognition Letters, Elsevier Science Inc New York, NY, USA, Volume 28 , Issue 10, July 2007, pp. 1142-1150.
  10. Hafizah Hussain and Lai Len Fatt , "Efficient ECG Signal Classification Using Sparsely Connected Radial Basis Function Neural Network", Proceeding of the 6th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, December 2007, pp. 412-416.
  11. Rahime, Ceylan, Yuksel and Ozbay, "Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network", International Journal on Expert Systems with Applications Volume 33, Issue 2, August 2007, pp. 286-295.
  12. Labib Khadra, Amjed S. Al-Fahoum, and Saed Binajjaj, "A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques", IEEE Transactions on Biomedical Engineering, Vol. 52, No. 11, November 2005, pp. 1840-1845
  13. Yang Wang, Yi. - Sheng Zhu, Nitish V. Thakor, and Yu-Hong Xu, "A Short Time Multifractal Approach for Arrhythmia Detection Based on Fuzzy Neural Network", IEEE Transactions on Biomedical Engineering, Vol. 48, No. 9, 2001, pp. 989-995.
  14. Shivajirao Jadhav, Sanjay Nalbalwar and Ashok Ghatol, "Modular Neural Network based Arrhythmia Classification System using ECG Signal Data", International Journal of Information Technology & Knowledge Management (ISSN 0973-4414), January–June 2011, Vol. IV, No. :1 pp. 205-209
  15. Shivajirao Jadhav, Sanjay Nalbalwar and Ashok Ghatol, "Artificial Neural Network Based Cardiac Arrhythmia Classification Using ECG Signal Data", in Proc. Int. Conference on Electronics and Information Engineering, Kyoto Japan, Volume: 1, 10. 1109/ICEIE. 2010. 5559887, Pages: V1-228 - 231
  16. Shivajirao Jadhav, Sanjay Nalbalwar and Ashok Ghatol, "ECG Arrhythmia Classification using Modular Neural Network Model", in Proc. 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences , IECBES. 2010. 5742200, Kuala Lumpur, Malaysia, Pages: 62-66
  17. Shivajirao Jadhav, Sanjay Nalbalwar and Ashok Ghatol, "Generalized Feedforward Neural Network based Cardiac Arrhythmia Classification from ECG Signal Data", 2010 6th International Conference on Advanced Information Management and Service (IMS) with ICMIA 2010, Seoul South Korea, Pages: 351-356
  18. Kemal Polat, Seral ?ahan, Salih Güne?, A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia, Expert Systems with Applications, Vol. 31, Issue 2, August 2006, pp. 264-269
  19. Jadhav, S. M. ; Nalbalwar, S. L. ; Ghatol, A. A. ; , "Arrhythmia disease classification using Artificial Neural Network model," Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on , vol. , no. , pp. 1-4, 28-29 Dec. 2010
  20. Jadhav, S. ; Nalbalwar, S. ; Ghatol, A. , "Modular neural network model based foetal state classification", 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW 2011), pp. 915-917
  21. Sang-Hong Lee, Jung-Kwon Uhm, and Joon S. Lim, "Extracting Input Features and Fuzzy Rules for Detecting ECG Arrhythmia Based on NEWFM", International Conference on Intelligent and Advanced Systems, Division of Software, Kyungwon University, Korea
  22. Alaa M. Elsayad, " Classification of ECG arrhythmia Using Learning Vector Quantization Neural Networks" (978-1-4244-5844-8/09/$26. 00 ©2009 IEEE )Manuscript received July 30, 2009: revised 1 October 2010
  23. Uyar A. , Gurgen F. , "Arrhythmia Classification Using Serial Fusion of Support Vector Machines and Logistic Regression," Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007. IDAACS 2007. 4th IEEE Workshop on , vol. , no. , pp. 560-565, 6-8 Sept. 2007
  24. Ali Mirza Mahmood and Mrithyumjaya Rao Kuppa, "A novel pruning approach using expert knowledge for data-specific pruning", Engineering with Computers 28, 1 (January 2012), 21-30.
  25. Blake CL, Merz CJ. UCI Repository of Machine Learning Databases. 1998. Available from: http://archive. ics. uci. edu/ml/datasets. html (Downloaded Date: 25th January, 2012 )
  26. Lippman R. , "An introduction to computing with neural nets", IEEE Trans. ASSP Magazine 4, 4-22,1987
  27. Jose Principe, Neil Euliano,Curt Lefebvre, Neural And Adaptive System, 2000 Jon Willey and Sons, Inc. , New York
  28. Thomas J. Downey, Donald J. Meyer, Rumi Kato Price, Using the Receiver Operating Characteristic to assess the performance of Neural Classifiers, IEEE 1999, 3642-3646
  29. Fawcett T. , ROC Graphs: Note and Practical Considerations for Data Mining Researchers, HP Labs Technical Report (HPL-2003-4), 2003 [Online]. (Downloaded date: 25 February 2012). Available: http://www. neurosolutions. com/download. h
Index Terms

Computer Science
Information Sciences

Keywords

Accuracy Ecg Arrhythmia Multilayer Perceptron Neural Network Model Momentum Learning Rule Sensitivity Specificity

Powered by PhDFocusTM