Abstract
An inference engine for classification of ECG signals is developed with the help of a rule based rough set decision system. For this purpose an automated ECG data extraction system from ECG strips is being developed by using few image processing techniques. Filtering techniques are used for removal of noises from recorded ECG. A knowledge base is developed after consultation of different medical books and feedback of reputed cardiologists regarding ECG 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 roughest decision system is generated from these time-plane features for the development of an inference engine for disease classification.
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Mitra, S., Mitra, M., Chaudhuri, B.B. (2006). An Approach to a Rough Set Based Disease Inference Engine for ECG Classification. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_42
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DOI: https://doi.org/10.1007/11908029_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
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