Electrocardiogram Classification Method Based on SVM

DOI

10.2991/iske.2007.282How to use a DOI?

Keywords

Support vector machine, feature extraction, electrocardiogram classification, CAD

Abstract

Heart disease is one of the main diseases threatening human beings health, and electrocardiogram is the important basis of diagnosing cardiovascular disease. Therefore, the computer auto analysis of ECG remains the research hotspot in medical Engineering. Since the distinctiveness and variability of QRS wave, many present ECG classification techniques exist difficulties to realize. Although many methods could work successfully in recognizing certain types of ECG signals, the recognition rate usually can not be substantially promoted throughout all kinds of ECG signals. In this paper, 1-vs-rest algorithm of SVM is used for ECG classification. The algorithm for ECG classification is tested with the data of MIT-BIH. Finally a high recognize rate is obtained. It is better than normal way in constructing algorithm model and classification speed

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiao Tang
AU  - Mo Zhiwen
PY  - 2007/10
DA  - 2007/10
TI  - Electrocardiogram Classification Method Based on SVM
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1646
EP  - 1650
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.282
DO  - 10.2991/iske.2007.282
ID  - Tang2007/10
ER  -