Analysis and classification of interictal spike discharges in benign partial epilepsy of childhood on the basis of the Hilbert transformation.

The spatial distribution of instantaneous power during the occurrence of rolandic spikes (computed via Hilbert transformation) can be utilized for classification of topographically different spike types. By visual analysis of the instantaneous power maps we found seven characteristic spike classes which correspond to the results of visual analysis of potential distribution. A neural network (NN) was trained with representatives of these classes. Single instantaneous power maps of 55/56 visually selected rolandic spikes, as well as map sequences of averaged spike segments (on-line) of 17 patients, were correctly classified by means of NN. The sensitivity of the NN for spikes from unknown patients was 98%. The classification scheme enables an objective classification of (multi-) regional spikes for selective averaging and for studies dealing with syndrome classification.