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ECG Signal Classification Using Various Machine Learning Techniques

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction disturbance. In this paper the proposed method is used to classify the ECG signal by using classification technique. First the Input signal is preprocessed by using filtering method such as low pass, high pass and butter worth filter to remove the high frequency noise. Butter worth filter is to remove the excess noise in the signal. After preprocessing peak points are detected by using peak detection algorithm and extract the features for the signal are extracted using statistical parameters. Finally, extracted features are classified by using SVM, Adaboost, ANN and Naïve Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. Experimental result shows that the accuracy of the SVM, Adaboost, ANN and Naïve Bayes classifier is 87.5%, 93%, 94 and 99.7%. Compared to other classifier naïve bayes classifier accuracy is high.

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References

  1. Zhao, Q., and Zhan, L., ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. International Conference on Neural Networks and Brain, ICNN&B 2:1089–1092, 2005.

    Google Scholar 

  2. El-Khafif, S. H., and El-Brawany, M. A., Artificial Neural Network-Based Automated ECG Signal Classifier. ISRN Biomedical Engineering 2013:1–6, 2013.

    Article  Google Scholar 

  3. Jiang, W., Kong, S. G., and Peterson, G. D., ECG Signal Classification using Block-based Neural Networks. Proceedings of International Joint Conference on Neural Networks, pp. 326–331, 2005.

  4. Jiang, W., Member, S., and Kong, S. G., Block-Based Neural Networks for Personalized ECG Signal Classification. IEEE Trans. Neural Netw. 18(6):1750–1761, 2007.

    Article  Google Scholar 

  5. Subbiah, S., Patro, R., and Subbuthai, Feature extraction and classification for ECG signal processing based on artificial neural network and machine learning approach. International Conference on Inter Disciplinary Research in Engineering and Technology, pp. 50–57, 2015.

  6. Djahida, M., Talha, K., and Slimane, S.., Neural networks and SVM for heartbeat classification. 11th International Conference on Information Sciences, Signal Processing and their Applications, pp. 830–835, 2012.

  7. Güler, I., and Übeyli, E. D., ECG beat classifier designed by combined neural network model. Pattern Recogn. 38(2):199–208, 2005.

    Article  Google Scholar 

  8. Tayel, M. B., Eltrass, A. S., and Ammar, A. I., A new multi-stage combined kernel filtering approach for ECG noise removal. J. Electrocardiol. 51:265–275, 2018.

    Article  Google Scholar 

  9. Mahapatraa, S., Mohantab, D., Mohantyc, P., Nayakd, S., and Beharie, P., A Neuro-fuzzy based model for analysis of an ECG signal using Wavelet Packet Tree. International Conference on Intelligent Computing, Communication & Convergence 92:175–180, 2016.

    Google Scholar 

  10. Mahapatra, S., Nayak, S. K., and Sabat, S. L., Neuro -Fuzzy model for adaptive filtering of oscillatory Signals. Elsevier Science, Measurement 30:231–239, 2001.

    Google Scholar 

  11. Chen, Y. C., and Teng, C. C., Fuzzy neural network in model reference control systems. Fuzzy Logic and Expert Systems Applications, San Diego-Toronto: Academic Press, pp. 285–313, 1998.

    Chapter  Google Scholar 

  12. Engin, M., ECG beat classification using neuro-fuzzy network. Elsevier pattern Recognisation Letter 25:1715–1722, 2004.

    Article  Google Scholar 

  13. Jaykumar S. Karnewar, Milind V. Sarode,” The Combined Effect of Median and FIR Filter in Pre-processing of ECG Signal using Matlab”, International Journal of Computer Applications,pp.30-33March 2013.

  14. Upganlawar, I. V., and Chowhan, H., Pre-processing of ECG Signals Using Filters. International Journal of Computer Trends and Technology (IJCTT) 11(4):166–168, 2014.

    Article  Google Scholar 

  15. Lyons, R., Interpolated narrowband lowpass FIR filters. IEEE Signal Process. Mag. 20(1):50–57, 2003.

    Article  Google Scholar 

  16. Saramaki, T., Neuvo, Y., and Mitra, S. K., Design of computationally efficient interpolated FIR filters. IEEE Transactions on Circuits and Systems 5(1):70–88, 1988.

    Article  Google Scholar 

  17. Daskalov, I. K., and Christov, I. I., Electrocardiogram signal preprocessing for automatic detection of QRS boundaries. Journal of Elsevier Medical Engineering & Physics 21:37–44, 1999.

    Article  CAS  Google Scholar 

  18. Arias-Castro, E., and Donoho, D. L., Does median filtering truly preserve edges better than linear filtering? Ann. Stat. 37(3):1172–1206, 2009.

    Article  Google Scholar 

  19. Kropf, M., Hayn, D., and Schreier, G., ECG Classification Based on Time and Frequency Domain Features Using Random Forests. Comput. Cardiol. 44:1–4, 2017.

    Google Scholar 

  20. Li, T., and Zhou, M., ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy 18(8):1–16, 2016.

    Article  Google Scholar 

  21. Emanet, N., ECG beat classification by using discrete wavelet transform and random forest algorithm. International Conference on Soft Computing, 2009.

  22. Padmavathia, S., and Ramanujamb, E., Naïve Bayes Classifier for ECG abnormalities using Multivariate Maximal Time Series Motif. Procedia Computer Science 47:222–228, 2015.

    Article  Google Scholar 

  23. Kandala, N. V. P. S., and Rajesh, R. D., Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier. Biomedical Signal Processing and Control 41:242–254, 2018.

    Article  Google Scholar 

  24. Maciejewski, M., Dzida, G., ECG parameter extraction and classification in noisy signals, Signal Processing, pp. 243–248, 2017.

  25. Kaistha, T., Mahajan, A., and Ahuja, K., A novel approach for Extraction and Classification of ECG signal using SVM, International Journal of Computer Technology and Application. 9(41):177–182, 2016.

  26. Xiong, Z., Stiles, M. K., Zhao, J., Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. Cardiology, 44, 2017.

  27. Ahilan, A., & Deepa, P., Improving lifetime of memory devices using evolutionary computing based error correction coding. Computational Intelligence, Cyber Security and Computational Models. pp. 237–245, 2016.

    Google Scholar 

  28. Harikumar, R., Shivappriya, S. N., Analysis of QRS Detection Algorithm for Cardiac Abnormalities – A Review. International Journal of Soft Computing and Engineering (IJSCE). 1 (5), 2011.

  29. Golrizkhatami, Z., Taheri, S., and Acan, A., Multi-scale features for heartbeat classification using directed acyclic graph CNN. An International Journal of Applied Artificial Intelligence, 2018.

  30. Nayak, S., Soni, M. K., and Bansal, D., Filtering Techniques For ECG Signal Processing”, International Journal of Research in Engineering & Applied Sciences 2(2), 2012.

  31. Ahilan, A., and Deepa, P., Radiation Induced Multiple Bit Upset Mitigation and Correction in Memories using Cost Efficient CMC. Microelectronics, Electronic Components and Materials 46(4):257–266, 2016.

    Google Scholar 

  32. Ahilan, A., and Deepa, P., Design for built-in FPGA reliability via fine-grained 2-D error correction codes. Microelectron. Reliab. 55(9–10):2108–2112, 2015.

    Article  Google Scholar 

  33. Ahilan, A., & Deepa, P., Design for Reliability: A Novel Counter Matrix Code for FPGA based Quality Applications. 6th Asia Symposium on Quality Electronic Design Electronic Design (ASQED 2015), Kuala Lumpur, Malaysia Vol. 6, pp. 56–61, 2015.

  34. Gaikwad, K. M., Chavan, M. S., Removal of high frequency noise from ECG signal using digital IIR butterworth filter. IEEE Global Conference on Wireless Computing and Networking, pp. 121–124, 2014.

  35. Sarma, P., Nirmala, S. R., Sarma, K. K., Classification of ECG using some Novel Features. IEEE International Conference on Emerging Trends and Applications in Computer Science, pp. 187–191, 2013.

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Authors and Affiliations

  1. Satyabama Institute of Science and Technology, Chennai, India

    S Celin

  2. Vidya Jyothi Institute of Technology, Hyderabad, Telangana, 500075, India

    K. Vasanth

Authors
  1. S Celin
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  2. K. Vasanth
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Contributions

First author: Celin S. collected materials and prepared the draft under the supervision of Profs. Vasanth K. The manuscript has gone through several rounds of internal revisions.

Corresponding author

Correspondence to S Celin.

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This article content has no conflict of interest regarding Publication.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

S. Celin is a research scholar and K. Vasanth is a professor.

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Cite this article

Celin, S., Vasanth, K. ECG Signal Classification Using Various Machine Learning Techniques. J Med Syst 42, 241 (2018). https://doi.org/10.1007/s10916-018-1083-6

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  • DOI: https://doi.org/10.1007/s10916-018-1083-6

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