A fuzzy pattern matching technique for diagnostic ECG classification

The authors describe a technique for the automatic acquisition of expert knowledge in order to set up a knowledge base for the diagnostic classification of ECG signals. The method is indirect, because the knowledge of the expert, in contrast with the general approach which learns through the direct communication of rules and facts, is derived from a learning set of classified ECGs. It is, on the other hand, different from conventional statistical techniques, because (1) the reference classification is given by experts and not by independent exams like autopsy, coronarography, echocardiography, cardiac surgery, and so on, and (2) this classification can be uncertain, i.e. the various classes are associated with each ECG with certainty factors which can differ from 0 or 1. The data are derived from the CSE pilot diagnostic library. In this preliminary study, the results of the method, which is based on fuzzy pattern matching, show a global type-4 error (complete disagreement) equal to 12.5%.<
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