Morphological descriptors for automatic detection of epileptiform events

The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic detection process the morphological characteristics pertaining to these events may get mixed up if only conventional descriptors are used, it was necessary to create a new set of parameters that reveal more differences between them. The parameters are fed to artificial neural networks and the individual and collective contribution of each parameter was evaluated by statistical process. The proposed method achieved a success rate of 80-90%, sensitivity and specificity between 85% and 96%.