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

A Survey on various Machine Learning Approaches for ECG Analysis

by C. K. Roopa, B. S. Harish
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 9
Year of Publication: 2017
Authors: C. K. Roopa, B. S. Harish
10.5120/ijca2017913737

C. K. Roopa, B. S. Harish . A Survey on various Machine Learning Approaches for ECG Analysis. International Journal of Computer Applications. 163, 9 ( Apr 2017), 25-33. DOI=10.5120/ijca2017913737

@article{ 10.5120/ijca2017913737,
author = { C. K. Roopa, B. S. Harish },
title = { A Survey on various Machine Learning Approaches for ECG Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 9 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 25-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number9/27425-2017913737/ },
doi = { 10.5120/ijca2017913737 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:45.817462+05:30
%A C. K. Roopa
%A B. S. Harish
%T A Survey on various Machine Learning Approaches for ECG Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 9
%P 25-33
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing most of the cardiac disease. The main objective of this paper is to review the various machine learning approaches for diagnosing Myocardial Infarction (heart attack), differentiate Arrhythmias (heart beat variation), Hypertrophy (increase thickness of the heart muscle) and Enlargement of Heart. Further, we also present various machine learning approaches and compare different methods and results used to analyze the ECG. The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.

References
  1. Andreao, R., Dorizzi, B., and Boudy, J. (2006). ECG signal analysis through hidden Markov models. IEEE Transactions on Biomedical Engineering, vol.53, no.8, pp.1541-1549.
  2. Anuradha, B. and Reddy, V. (2008). ANN classification of cardiac arrhythmias, ARPN Journal of Engineering and Applied Sciences, vol.3, no.3, pp.1-6.
  3. Barman, T., Ghongade., and Ratnaparkhi, A. (2016). Rough set based segmentation and classification model for ECG. IEEE Conference on Advances in Signal Processing (CASP), pp.18-23.
  4. Batra, A. and Jawa, V. (2016). Classification of Arrhythmia using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria, International Journal of Biology and Biomedicine, vo1.1, pp.1-7.
  5. Behadada, O. and Chikh, M.A. (2013). An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree, Artificial Intelligence Research, vol.2, no.3, pp.45-58.
  6. Bensujin., Vijila, C.K. and Hubert, C. (2014). Detection of ST Segment Elevation Myocardial Infarction (STEMI) Using Bacterial Foraging Optimization Technique, International Journal of Engineering and Technology (IJET), vol.6, no.2, pp.1212-1224.
  7. Ceylan, R., Ozbay, Y. and Karlik, B. (2009). A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network, Expert Systems with Applications, vol.36, no.3, pp.6721-6726.
  8. Chen, Y., Wang, T., Wang, B., and Li, Z. (2009). A survey of fuzzy decision tree classifier. Fuzzy Information and Engineering, vol.1, no.2, pp.149-159.
  9. Dalal, S., and Birok, R. (2016). Analysis of ECG Signals using Hybrid Classifier. International Advanced Research Journal in Science, Engineering and Technology, vol.3 no.7, pp. 89-95.
  10. De Gaetano, A., Panunzi, S., Rinaldi, F., Risi, A., and Sciandrone, M. (2009). A patient adaptable ECG beat classifier based on neural networks. Applied Mathematics and Computation, vol.213, no.1, pp.243-249.
  11. Deja, A. W., and Paszek, P. (2003). Applying rough set theory to Multi stage medical diagnosing. Fundamental Informaticae, vol.54, no.4, pp.387-408.
  12. El-Dahshan, E. (2010). Genetic algorithm and wavelet hybrid scheme for ECG signal denoising. Telecommunication Systems, vol.46, no.3, pp.209-215.
  13. D. Da Costa, W. J. Brady, and J. Edhouse. (2002) “Abc of clinical electrocardiography: Bradycardias and atrioventricular conduction block,” BMJ: British Medical Journal, vol. 324, no.7336, pp.535.
  14. Finlay, D., Bond, R., Kennedy, A., Guldenring, D., Moran, K. and McLaughlin, J. (2015). The effects of electrode placement on an automated algorithm for detecting ST segment changes on the 12-lead ECG, Computing in Cardiology,vol. 42, pp.1161-1164.
  15. Frénay, B., Lannoy, G., and Verleysen, M. (2009). Improving the transition modelling in hidden Markov models for ECG segmentation. ESANN proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, Bruges (Belgium), pp.22-24.
  16. Gao, D., Madden, M., Schukat, M., Chambers, D., and Lyons, G. (2004). Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework. Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, vol.3, no.3, pp.390-409.
  17. Ghosh, B., Indurkar, M., and Jain, M.K. (2013). ECG: A Simple Noninvasive Tool to Localize Culprit Vessel Occlusion Site in Acute STEMI, Indian Journal of Clinical Practice, vol.23, no.10, pp.590-595.
  18. Goel, S., Tomar, P.,and Kaur, G. (2016). A Fuzzy Based Approach for Denoising of ECG Signal using Wavelet Transform. International Journal of Bio-Science and Bio-Technology, vol.8, no.2, pp. 143-156.
  19. Guler, I., and Ubeyli, E. D. (2007). Multiclass support vector machines for EEG signals classification. IEEE Transactions on Information Technology in Biomedicine., vol.11, no.2, pp. 117–126.
  20. Guler, J., Ubeyli, E.D. (2004). Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Systems with Applications, vol.37, no. 27, pp. 323-330.
  21. Gupta, K. O., and Chatur, P. N. (2012). ECG Signal Analysis and Classification using Data Mining and Artificial Neural Networks. International Journal of Emerging Technology and Advanced Engineering, vol.2, no.1, pp. 56-60.
  22. Huang, X. M., and Zhang, Y. H. (2003). A new application of rough set to ECG recognition. In Int. Conference on Machine Learning and Cybernetics, no.3, pp.1729-1734.
  23. Jadhav, S., Nalbalwar, S.L. and Ghatol, A. (2012). Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data, International Journal of Computer Applications, vol.44, no.15, pp. 8-13.
  24. Jeyalakshmi, M.S, and Robin, C. (2016). Fuzzy based Expert system for sleep apnea diagnosis. International Journal of Engineering Trends and Technology, vol.35, no.12, pp. 555-558.
  25. Kampouraki, A., Manis, G., and Nikou, C. (2009). Heartbeat Time Series Classification with Support Vector Machines. IEEE Transactions on Information Technology in Biomedicine, vol.1, no.4, pp. 512-518.
  26. Lei, W. K., Li, B. N., Dong, M. C. and Vai, M. I. (2007). AFC-ECG: An Intelligent Fuzzy ECG Classifier. In book: Soft Computing in Industrial Applications, vol.39, pp.189-199
  27. Mitra, S., Mitra, M. and Chaudhuri, B.B. (2006). An Approach to a Rough Set Based Disease Inference Engine for ECG Classification, IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 6, pp. 2198-2206.
  28. Nasiri, J. A., Naghibzadeh, M., Yazdi, H. S., and Naghibzadeh, B. (2009). ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm. Third UKSim European Symposium on Computer Modeling and Simulation.
  29. Omran, S. S., Taha, S. M. R., and Awadh, N. A. (2009). ECG Rhythm Analysis by Using Neuro-Genetic Algorithms. Journal of Basic and Applied Sciences, vol.1, no.3, pp.522-530.
  30. Pal, D., Mandana, K., Pal, S., Sarkar, D., and Chakraborty, C. (2012). Fuzzy expert system approach for coronary artery disease screening using clinical parameters. Knowledge-Based Systems, vol.36, pp.162-174.
  31. Polat, K., Akdemir, B., and Gune, S. (2008). Computer aided diagnosis of ECG data on the least square support vector machine, Digital Signal Processing, vol.18, pp.25–32.
  32. Priyadharshini, V. and Kumar, S.S. (2015). An Enhanced Approach on ECG Data Analysis using Improvised Genetic Algorithm, International Research Journal of Engineering and Technology (IRJET), vol.2, no.5, pp.1248-1256.
  33. Sao, P., Hegadi, R., and Karmakar, S. (2015). ECG Signal Analysis Using Artificial Neural Network. International Journal of Science and Research, pp.82-86.
  34. Senthilkumaran, N., and Rajesh, R. (2009). A Study on Rough Set Theory for Medical Image Segmentation. International Journal of Recent Trends in Engineering, vol.2, no.2, pp. 236-238.
  35. Setiawan, N.A., Venkatachalam, P.A. and Fadzil, A. (2009). Rule Selection for Coronary Artery Disease Diagnosis Based on Rough Set, International Journal of Recent Trends in Engineering, vol.2, no.5, pp. 198-202.
  36. Silveira, R. M., Agulhari, C. M., Bonatti, I. S., and Peres, P. D. L. (2007). A genetic algorithm to compress the electrocardiograms using parametrized wavelets. IEEE International Symposium on Signal Processing and Information Technology.
  37. Tripathy, B.K., Acharjya, D.P. and Cynthya, V. (2011). A Framework for Intelligent Medical Diagnosis Using Rough Set with Formal Concept Analysis, International Journal of Artificial Intelligence & Applications (IJAIA), vol.2, no. 2, pp.45-66.
  38. Vishwa, A., Lal, M., Dixit, S., and Vardwaj, P. (2011). Classification of Arrhythmic ECG Data Using Machine Learning Techniques. International Journal of Interactive Multimedia and Artificial Intelligence, vol.1, no.4, pp 67-70.
  39. Wang, T.Y., Chiang, H.M. (2007). Fuzzy support vector machine for multiclass categorization. Information Process Management, vol. 43, no.4, pp.914–929.
Index Terms

Computer Science
Information Sciences

Keywords

Heart Disease ECG Analysis Feature Extraction Segmentation.

Powered by PhDFocusTM