KNOWLEDGE-BASED SYSTEMS FOR ARRHYTHMIA DETECTION AND CLASSIFICATION

In this paper two knowledge-based methods for arrhythmia detection and classification using ECG recordings are described, which utilize different information of the ECG signal. The first uses features of the ECG signal (R wave, QRS duration, P wave, RR interval, PR interval, PP interval, QRS similarity and P wave similarity), which are fed into a decision-tree like knowledge-based system. The system can classify all types of arrhythmias. The second is based on the utilization of the RR-duration signal only. Initially, rules based on medical knowledge are used for arrhythmic beat classification and the results are fed into a deterministic automato for arrhythmic episode detection and classification. The system can be used for the classification of limited types of arrhythmia due to the fact that only limited information is carried by the RR-duration signal.

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