Neural networks and largest Lyapunov exponent for automatic epileptic seizure detection in EEGs

Abstract

We report a novel method for epileptic seizure detection that is reliant on the maximal short-term Lyapunov exponent (STLmax). The proposed approach is based on automatic segmentation of the EEG into epochs that correspond to epileptic and non-epileptic activity. The STLmax is then computed from both categories of EEG signal and used for classification of epileptic and non-epileptic EEG segments throughout the recording. Neural network techniques are proposed both for segmentation of EEG signals and computation of STLmax. The data set from hospital have been used for experiments performing. Furthermore, the publicly available data were used for experiments. The main advantages of presented neural technique is its ability to rapidly detect the small EEG time segments as epileptic or non-epileptic activity, training without desired data set about epileptic and non-epileptic activity in EEG signals .