Categorization of Cardiac Arrhythmia from ECG Waveform by Using Super Vector Regression Method-Reference-Cited by-同舟云学术

Categorization of Cardiac Arrhythmia from ECG Waveform by Using Super Vector Regression Method

Published:2022 Issue: Volume: Page:599-613
ISSN:2367-3370
Container-title:Expert Clouds and Applications
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Author:

Sanamdikar S. T.,Karajanagi N. M.,Kowdiki K. H.,Kamble S. B.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-2500-9_45

Reference27 articles.

1. J.I. Willams, CSE Working Party, Recommendations for measurement standards in quantitative ECG. Eur. Heart J. 815–825 (1985)

2. Q. Zhao, L. Zhan, ECG feature extraction and classification using wavelet transform and support vector machines, in International Conference on Neural Networks and Brain, ICNN&B’05, vol. 2 (2005), pp. 1089–1092

3. V.S. Chouhan, S.S. Mehta, Total removal of baseline drift from ECG signal, in Proceedings of International Conference on Computing: Theory and Applications, ICTTA–07 (2007), pp. 512–515

4. S.C. Saxena, V. Kumar, S.T. Hamde, Feature extraction from ECG signals using wavelet transforms for disease diagnostic. Int. J. Syst. Sci. 33(13), 1073–1085 (2002)

5. A.B. Ramli, P.A. Ahmad, Correlation analysis for abnormal ECG signal features extraction, in 4th National Conference on Telecommunication Technology, 2003. NCTT 2003 Proceedings (2003), pp. 232–237

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