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Rhythm Classification of ECG Signal by Rule and SVM Based Algorithm
Journal of The Korea Society of Computer and Information
Abbr :
JKSCI
2013, 18(9), pp.43-51
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
2013, 18(9), pp.43-51
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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Classification result by comprehensive analysis of rhythm section and heartbeat unit makes a reliable diagnosis of heart disease possible. In this paper, based on feature-points of ECG signals,rhythm analysis for constant section and heartbeat unit is conducted using rule-based classification and SVM-based classification respectively. Rhythm types are classified using a rule base deduced from clinical materials for features of rhythm section in rule-based classification, and monotonic rhythm or major abnormality heartbeats are classified using multiple SVMs trained previously for features of heartbeat unit in SVM-based classification. Experimental results for the MIT-BIH arrhythmia database show classification ratios of 68.52% by rule-based method alone and 87.04% by fusion method of rule-based and SVM-based for 11 rhythm types. The proposed fusion method is improved by about 19% through misclassification improvement for monotonic and arrangement rhythms by SVM-based method
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