Over the past few decades, application of neural networks and chaos theory to electroencephalogram (EEG) analysis has grown rapidly due to the complex and nonlinear nature of EEG data. We report a novel method for epileptic seizure detection that is depending on the maximal short-term Lyapunov exponent (STLmax). The proposed approach is based on the automatic segmentation of the EEG into time segments that correspond to epileptic and non-epileptic activity. The STL-max 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. It consists of 21 records during 8 seconds of eight adult patients. Furthermore the publicly available data were used for experiments. The main advantages of presented neural technique is its ability to detect rapidly the small EEG time segments as the epileptic or non-epileptic activity, training without desired data set about epileptic and non-epileptic activity in EEG signals. The proposed approach permits to detect exactly the epileptic and non-epileptic EEG segments of different duration and shape in order to identify a pathological activity in a remission state as well as detect a paroxysmal activity in a preictal period.
TOWARDS AUTOMATIC EPILEPTIC SEIZURE DETECTION IN EEGS BASED ON NEURAL NETWORKS AND LARGEST LYAPUNOV EXPONENT
