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Comparative Study of ECG Classification Performance Using Decision Tree Algorithms

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  • Faiza Charfi

    (Electronic and Information Technologies Laboratory, Sfax University, Sfax, Tunisia)

  • Ali Kraiem

    (Electronic and Information Technologies Laboratory, Sfax University, Sfax, Tunisia)

Abstract

The electrocardiogram (ECG) signal has often been reported to play an important role in the primary diagnosis, prognosis, and survival analysis of heart diseases. Electrocardiography has brought several valuable impacts on the practice of medicine. This paper deals with the feature extraction and automatic analysis of different ECG signal waves using derivative based/ Pan-Tompkins based algorithms. The ECG signal contains an important amount of information that can be exploited in different way. It allows for the analysis of cardiac health condition. The discrimination of ECG signals using the Data Mining Decision Tree techniques is of crucial importance in the cardiac disease therapy and control of cardiac arrhythmias. Different ECG signals from MIT/BIH Arrhythmia data base are used for ECG features extraction and analysis. Two pathologies are considered: atrial fibrillation and right bundle branch block. Some decision tree classification algorithms currently in use, including C4.5, Improved C4.5, CHAID (Chi square Automatic Interaction Detector) and Improved CHAID are performed for performance analysis. Promising results have been achieved using the C4.5 classifier, with an overall accuracy of 96.87%.

Suggested Citation

  • Faiza Charfi & Ali Kraiem, 2012. "Comparative Study of ECG Classification Performance Using Decision Tree Algorithms," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 3(4), pages 102-120, October.
    • Handle: RePEc:igg:jehmc0:v:3:y:2012:i:4:p:102-120
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