Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages

OBJECTIVE A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on pre-recorded data from 43 patients. METHODS The system is centred on the use of an artificial neural network, known as the self-organising feature map (SOFM), as a novel pattern classifier. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. RESULTS The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour. CONCLUSIONS Based on these initial results the overall performance is favourable when compared with other leading systems in the literature. This encourages us to further test the system on a larger population base with the ultimate aim of introducing it into routine clinical use.

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