INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY RESEARCH ARTICLE OPEN ACCESS Received: 17-05-2020 Accepted: 13-06-2020 Published: 08-07-2020 Editor: Dr. Natarajan Gajendran Citation: Tribhuvanam S, Nagaraj HC, Naidu VPS (2020) Analysis and classification of ECG beat based on wavelet decomposition and SVM. Indian Journal of Science and Technology 13(24): 2404-2417. https://doi.org/ 10.17485/IJST/v13i24.452 * Corresponding author. Sundari Tribhuvanam Research Scholar, Department of Electronics, University of Mysore, Mysore, 570005, India. Tel.: +91-973-912-7272 stribhuvanam@yahoo.co.in Funding: None Competing Interests: None 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) Analysis and classification of ECG beat based on wavelet decomposition and SVM Sundari Tribhuvanam 1* , H C Nagaraj 2 , V P S Naidu 3 1 Research Scholar, Department of Electronics, University of Mysore, Mysore, 570005, India. Tel.: +91-973-912-7272 2 Department of Electronics, Nitte Research and Education Academy, NMIT Campus, Bengaluru, 560064, India 3 MSDF, FMCD, CSIR-NAL, Bengaluru, 560078, India 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 eval- uates 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 1 Introduction Cardiac diseases are the most common cause of death around the globe. Te design of health monitoring systems is always a topic of active research to support the car- diac patient. Electrocardiogram (ECG) provides detailed information of the condition of the heart (1,2) . Cardiologists can infer heart conditions from ECG wave patterns and inter wave intervals. To assist the medical doctors, researchers have proposed many algorithms for segmentation and classifcation of ECG signals more precisely and cor- rectly in real-time (3) . An arrhythmia classifcation system includes the pre-processing of ECG signal, abnormal beat segmentation, extraction of wavelet domain features and beat classifcation (4–6) . Te objective is to identify the various ECG arrhythmias as per AAMI standard thereby assisting the cardiologist for early diagnosis of heart disease. Arrhythmia detection procedure difers in selecting the size of ECG signal window, ECG feature extraction and classifcation approaches (7) . Te heart performance and prediction of future complications are done using Linear prediction method, Grid par- titioning and Fuzzy C-means clustering for ECG classifcation (8) . ECG feature selection is implemented by Bacterial Forging Optimization (BFO) and Particle Swarm https://www.indjst.org/ 2404