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

Cardiac Analysis and Classification of ECG Signal using GA and NN

by Naval Kishore, Sukhmanpreet Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 12
Year of Publication: 2015
Authors: Naval Kishore, Sukhmanpreet Singh
10.5120/ijca2015906551

Naval Kishore, Sukhmanpreet Singh . Cardiac Analysis and Classification of ECG Signal using GA and NN. International Journal of Computer Applications. 127, 12 ( October 2015), 23-27. DOI=10.5120/ijca2015906551

@article{ 10.5120/ijca2015906551,
author = { Naval Kishore, Sukhmanpreet Singh },
title = { Cardiac Analysis and Classification of ECG Signal using GA and NN },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 12 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number12/22781-2015906551/ },
doi = { 10.5120/ijca2015906551 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:18:02.633726+05:30
%A Naval Kishore
%A Sukhmanpreet Singh
%T Cardiac Analysis and Classification of ECG Signal using GA and NN
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 12
%P 23-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram represents electrical activity of the compassion. Sinus tachycardia and Sinus Bradycardia are among the most common ECG abnormality. Millions of ECGs are in use for the diagnosis of various lesions of patients, where ECG can give a lot of information regarding the abnormality in the concerned patient; ECGs are analyzed by the physicians and interpreted depending upon their knowledge. The understanding may vary by physician to medical doctor. Hence, this labor is all about the mechanization and consistency in the analysis of the ECG signals so that they must be diagnosed and interpreted accurately irrespective of the physician. This would help to create an early action intended for the problems and many lives might be saved. Many works have been done previously but this work presents Electrocardiogram (ECG) classification to diagnose patient’s condition. For classification of such Difficult to Diagnose Signals, P-Wave, PR-Interval, QRS Interval, ST Interval, T- Wave etc, analysis of each Input pulse used to train the neural network and features are obtained using Genetic Algorithm. Output of the neural network gives weight factors of each signal to create a data set. Electrocardiogram (ECG) PQRSTU-waveforms time intervals and weight factors and prediction of particular decease infection or state of a patient condition saved in database. A software program is written in MATLAB 7.10.Corresponding output-datasets indicates related disease and predict the causes. The results show a considerable improvement in conditions of FRR, FAR and accuracy of image retrieval.

References
  1. P. de Chazal, M. O'Dwyer, and R. B. Reilly, "Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features," IEEE Trans. on Biomedical Engineering, Vol. 51, No. 7, pp.1196-1206, July 2004.
  2. C. Alexakis, H. O. Nyongesa, R. Saatchi, N. D. Harris, C. Davis, C. Emery, R. H. Ireland, and S. R. Heller, "Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycaemia," Computers in Cardiology, Vol. 30, pp.537-540, 2003.
  3. I. Romero and L. Serrano, "ECG Frequency Domain Features Extraction: A New Characteristic for Arrhythmias Classification," Proc. Int'l. Conf. on Engineering in Medicine and Biology Society (EMBS'2001), pp.2006-2008, October 2001.
  4. P. de Chazal and R. B. Reilly, "A Comparison of the ECG Classification Performance of Different Feature Sets," Computers in Cardiology, Vol. 27, pp.327-330, 2000.
  5. Y. H. Hu, W. J. Tompkins, J. L. Urrusti, and V. X. Afonso, "Applications of Artificial Neural Networks for ECG Signal Detection and Classification," Journal of Electrocardiology, Vol. 26. (Suppl.), pp.66-73, 1994.
  6. S. Behbahani and N. J. Dabanloo, “Detection of QRS complexes in the ECG signal using Multiresolution wavelet and thresholding method” IEEE Computing in Cardiology, pp. 805-808, 2011.
  7. Hu, S.; Shao, Z.; Tan, J. A real-time cardiac arrhythmia classification system with wearable electrocardiogram. In Proceedings of the IEEE International Conference on Body Sensor Networks (BSN), Piscataway, NJ, USA, 23 May 2011; pp. 119–124.
  8. Yu, S.-N.; Chen, Y.-H. Electrocardiogram beat classification based wavelet and probabilistic neural network. Pattern Recogn. Lett. 2007, 7, 1142–1150.
  9. Rodriquez, J.; Gorii, A. Real-time classification of ECGs on a PDA. IEEE Trans. Inf. Technol. Biomed. 2005, 3, 23–34.
  10. Christov, I.; Gomez-Herrero, G. A comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys. 2006, 2, 876–887.
  11. S.Sumathi, Dr.M.Y. Sanavullah, “Comparative Study of QRS Complex Detection in ECG Based on Discrete Wavelet Transform,” Int. Journal of Recent Trends in Engineering, Vol 2, No. 5, November 2009, pp.273-277
  12. Krimm, H.; Brooks, D.H. Feature-based segmentation of ECG signals. In Proceedings of the IEEE International Symposium on Time-Frequency and Time-Scale Analysis, Paris, France, 18–21 June 1996; pp. 97–100.
  13. Pergamon, P. Comprehensive Electrocardiology: Theory and Practice in Health and Disease, 1st ed.; Pergamon Press: New York, NY, USA, 1989; Volume 1–3.
  14. A. Dallali, A. Kachouri and M. Samet, “Fuzzy C-means clustering, neural networks, WT, & HRV for classification of cardiac arrhythmia” in ARPN Journal of Engineering and Applied Sciences , October 2011, ISSN 1819-6608, VOL. 6, NO. 10, pp. 112–118.
  15. Argyro Kampouraki, George Manis, and Christophoros Nikou, “Heartbeat Time Series Classification with Support Vector Machines,” IEEE Trans. on Information Technology in Biomedicine, Vol. 13, No. 4, July 2009, pp. 512- 518.
  16. Dusit Thanapatay, Chaiwat Suwansaroj, Chusak Thanawattano, “ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines”, International Conference on Electronics and Information Engineering, Volume 1, 2010, pp. 72-75 .
  17. Manpreet Kaur, A.S.Arora, “Unsupervised Analysis of Arrhythmias using K-means Clustering,” International Journal of Computer Science and Information Technologies, Vol. 1 (5) , 2010, pp. 417-419.
  18. Emina Alickovic, Abdulhamit Subasi ,” Medical Decision Support System for Diagnosis of Cardiovascular Diseases using DWT and kNN” .
  19. J. Sarma and K. De Jong, "An Analysis of Local Selection Algorithms in a Spatially Structured Evolutionary Algorithm," Proc. Int'l Conf. on Genetic Algorithms (ICGA-97), pp. 181-186, 1997.
  20. S. W. Moon and S. G. Kong, "Pattern Recognition with Block-based Neural Networks," Proc. Int'l J. Conf. on Neural Networks (IJCNN-2002), pp.992-996, May 2002.
  21. S. G. Kong, "Time Series Prediction with Evolvable Block-based Neural Networks," Proc. Int'l J. Conf. on Neural Networks (UCNN-2004), July 2004.
  22. Chuang-Chien Chiu, Chou-Min Chuang, And Chih-Yu Hsu, Int. J. Wavelets Multiresolut Inf. Process. 07, 341 (2009). DOI: 10.1142/S0219691309002957
Index Terms

Computer Science
Information Sciences

Keywords

ECG Signal Neural Network Genetic Algorithm T-Cardia B-Cardia.

Powered by PhDFocusTM