Multivariate Features Extraction for Detection of Epileptic Seizures in Electroencephalogram

Recently more researchers in the biomedical engineering have introduced many techniques which try to detect epileptic seizures in Electroencephalogram (EEG). The main objective of this paper is to develop technique that is capable of differentiating between epileptic and normal signals. This technique consists of two stages. The first stage is the features extraction and the second stage is the classification of these features. Fast fourier transform, autoregressive model, and other nonlinear features were used as input features for the classification phase. The classification phase consists of three neural network classifiers and a majority decision to classify the input features. The classification accuracy of the proposed technique was superior compared to other techniques for accuracy. The proposed technique after using the majority method has accuracy equal 99.5%.