Analysis and classification of ECG beat based on wavelet decomposition and SVM

Sundari Tribhuvanam; H C Nagaraj; V P S Naidu

doi:10.17485/IJST/v13i24.452

Article

Analysis and classification of ECG beat based on wavelet decomposition and SVM

VIEWS 1027
PDF 838

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i24.452

Year: 2020, Volume: 13, Issue: 24, Pages: 2404-2417

Original Article

Analysis and classification of ECG beat based on wavelet decomposition and SVM

Sundari Tribhuvanam^1*, H C Nagaraj², V P S Naidu³

¹Research Scholar, Department of Electronics, University of Mysore, Mysore, 570005, India.
Tel.: +91-973-912-7272
²Department of Electronics, Nitte Research and Education Academy, NMIT Campus, Bengaluru, 560064, India
³MSDF, FMCD, CSIR-NAL, Bengaluru, 560078, India

^*Corresponding author
Tel.: +91-973-912-7272
Email: [email protected]

Received Date:17 May 2020, Accepted Date:13 June 2020, Published Date:08 July 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To extract the features of single arrhythmia ECG beat. To develop efficient algorithms for automated detection of arrhythmia based on ECG. Methods/Statistical analysis: The methodology includes pre-processing and segmentation of ECG. Extraction of ECG features are to support the ECG beat classification and analysis of cardiac abnormalities using machine learning techniques. Wavelet decomposition is considered for feature extraction and classification with multiclass support vector machine. Findings: This work evaluates the suitability of the wavelet features of ECG for classifier. The proposed arrhythmia classifier results in an accuracy up to 98% for various classes of arrhythmia considered in this work. Novelty/Applications: This work is an assistive tool for medical practitioners to examine ECG in a limited time with their expertise to make the accurate abnormality diagnosis of the arrhythmia.

Keywords: Arrhythmia; classification; feature extraction; support vector machine; wavelet decomposition

References

Liu F, Liu C, Jiang X, Zhang Z, Zhang Y, Li J, et al. Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases. Journal of Healthcare Engineering. 2018;2018:1–8. Available from: https://dx.doi.org/10.1155/2018/9050812
Osman J, Tan SC, Lee PY, Low TY, Jamal R. Sudden Cardiac Death (SCD) – risk stratification and prediction with molecular biomarkers. Journal of Biomedical Science. 2019;26(1):39–40. Available from: https://dx.doi.org/10.1186/s12929-019-0535-8
Scirè A, Tropeano F, Anagnostopoulos A, Chatzigiannakis I. Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning. Algorithms. 2019;12(32):1–21. Available from: https://dx.doi.org/10.3390/a12020032
Chashmi AJ, Amirani MC. An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure. Journal of Electrical Bioimpedance. 2019;10(1):47–54. Available from: https://dx.doi.org/10.2478/joeb-2019-0007
Qin Q, Li J, Zhang L, Yue Y, Liu C. Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification. Scientific Reports. 2017;7(1):6067. Available from: https://dx.doi.org/10.1038/s41598-017-06596-z
Luz EJdS, Schwartz WR, Cámara-Chávez G, Menotti D. ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine. 2016;127:144–164. Available from: https://dx.doi.org/10.1016/j.cmpb.2015.12.008
Alfarhan KA, Mashor MY, Saad ARM, Azeez HA, Sabry MM. Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification. Journal of Biomimetics, Biomaterials and Biomedical Engineering. 2017;30:1–11. Available from: https://dx.doi.org/10.4028/www.scientific.net/jbbbe.30.1
Rahouma HK, Muhammad HR, Eldahab FAHMAA. Analysis of Electrocardiogram for Heart Performance Diagnosis Based on Wavelet Transform and Prediction of Future complications. Egyption Computer Science Journal. 2017;41(2):11–30. Available from: https://doi.org/10.13141/RG.2.2.35948.41608
Kora P, Kalva SR. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block. SpringerPlus. 2015;4(1):1–19. Available from: https://dx.doi.org/10.1186/s40064-015-1240-z
Yanowitz GF. Introduction to ECG Interpretation” V9.0, . Available from: http://library.med.utah.edu/kw/ecg/
“the MB. Available from: http://www.physionet.org/physiobank/database/mitdb
Hadji MSE, Salleh M, Rohani MF, Kamat M. Wavelet-based Performance in Denoising ECG Signal. In: International Scientific conference of Pure Science(ISCPS). .
Tribhuvanam S, Nagaraj HC, Naidu VPS. ECG Abnormality Classification with Single Beat Analysis. In: International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). .
Tribhuvanam S, Nagaraj HC, Naidu VPS. Arrhythmia Classification with Single Beat ECG Evaluation and Support Vector Machine”. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2019;12:2278–3075.
Sarkaleh MK. Classification Of Ecg Arrhythmias Using Discrete Wavelet Transform and Neural Networks. International Journal of Computer Science, Engineering and Applications. 2012;2(1):1–13. Available from: https://dx.doi.org/10.5121/ijcsea.2012.2101
Rajani J, Naidu VPS. Bearing Fault Diagnosis using DWT and SVM. In: International Journal of Engineering Research & Technology, NCESC-2018 Conference Proceedings. 6:1–6.
García M, Ródenas J, Alcaraz R, Rieta JJ. Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. Computer Methods and Programs in Biomedicine. 2016;131:157–168. Available from: https://dx.doi.org/10.1016/j.cmpb.2016.04.009
Zhe, Wang X, Xue. Springer International Publishing Switzerland. 2014. Available from: https://www.scribd.com/document/322745147/Support-Vector-Machines-Applications
Hsu CW, Chang CC. 2016. Available from: https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Copyright

© 2020 Tribhuvanam, Nagaraj, Naidu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Published By Indian Society for Education and Environment (iSee)