Seizure Detection with the Self-Organising Feature Map

Abstract

We have developed a system to detect the presence of seizures in the multichannel scalp EEG. At the heart of the system is the Self-Organising Feature Map (SOFM) that has been trained on normal and epileptiform EEG segments of 12 patients (64 seizures). Following preliminary spatial analysis, autoregressive (AR) parameters are extracted from variable width segments which have been delineated through the use of a non-linear energy operator. The AR parameters are used as feature vectors for the SOFM training process. Following initial training, probability values are automatically assigned to the ‘prototype’ seizure segments based on the consensus of 3 EEGers. The use of a self-organising network retains objectivity in calculating the prototype seizure segments.Preliminary results (using the training set only) are given here. With a detection threshold of d th =0.49 the Sensitivity and Selectivity were both measured at 75% with a corresponding false detection rate of 0.5 / hour. These preliminary results indicate that the system shows promise for use as a generic seizure detection system - i.e., a non-patient specific seizure detection system.KeywordsCurrent DipoleSeizure DetectionFalse Detection RateFixed Model OrderAutomate Seizure DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.