A Rough Set Based Approach for ECG Classification

An inference engine for classification of Electrocardiogram (ECG) signals is developed with the help of a rule based rough set decision system. For this purpose an automated data extraction system from ECG strips is being developed by using a few image processing techniques. A knowledge base is developed after consulting different medical books as well as feedback of reputed cardiologists on interpretation and selection of essential time-plane features of ECG signal. An algorithm for extraction of different time domain features is also developed with the help of differentiation techniques and syntactic approaches. Finally, a rule-based rough set decision system is generated using these time-plane features for the development of an inference engine for disease classification. Two sets of rules are generated for this purpose. The first set is for general separation between normal and diseased subjects. The second set of rules is used for classifications between different diseases.

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