Comparative Analysis of Forecasting Neural Networks in the Application for Epilepsy Detection

Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, namely determination largest Lyapunov´s exponent or correlation dimension of the scalp EEG signals. This paper presents the neural network technique for epilepsy detection. It is based on computing of the largest Lyapunov´s exponent. This paper also describes analysis of experimental results where we applied different forecasting neural networks for computing the largest Lyapunov ´s exponent.