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http://dx.doi.org/10.9708/jksci.2013.18.9.043

Rhythm Classification of ECG Signal by Rule and SVM Based Algorithm  

Kim, Sung-Oan (Dept. of Computer Information, Suwon Science College)
Kim, Dae-Hwan (Dept. of Computer Information, Suwon Science College)
Publication Information
Journal of the Korea Society of Computer and Information / v.18, no.9, 2013 , pp. 43-51 More about this Journal
Abstract
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.
Keywords
ECG Signal; Rhythm Classification; Heartbeat Classification; Fusion Algorithm; Arrhythmia Diagnosis;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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