Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 24, Pages: 2404-2417
Sundari Tribhuvanam1*, H C Nagaraj2, V P S Naidu3
1Research Scholar, Department of Electronics, University of Mysore, Mysore, 570005, India.
2Department of Electronics, Nitte Research and Education Academy, NMIT Campus, Bengaluru, 560064, India
3MSDF, FMCD, CSIR-NAL, Bengaluru, 560078, India
Email: [email protected]
Received Date:17 May 2020, Accepted Date:13 June 2020, Published Date:08 July 2020
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
© 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)
Subscribe now for latest articles and news.