Classification of EEG for epilepsy diagnosis in wavelet domain using artifical neural network and multi linear regression

In this study, classification methods were proposed for diagnosis of epilepsy in EEG signals using lifting based wavelet transform (LBWT) with artificial neural network (ANN) and multi linear regression (MLR). In classification of EEG signals, LBWT was used to increase computational speed in the extraction of the feature vectors. In comparison of LBWT with the classical wavelet transform, it was observed that LBWT decreased computational load as 50%. The coefficients in delta, theta, alpha, and beta bands that were obtained by LBWT were used as input signals of classifiers. ANN was trained as its output is logic 0 or logic 1 if EEG includes no epileptic seizure. The effects of different wavelet filters (Haar, Daubechies 4,6,8) on proposed methods were also observed. Proposed methods were compared from the point of accuracy, specifity, and sensitivity. With this study, we aimed to provide an automatic decision support tool for neurologists treating potential epilepsy by defining features in EEG signals. We obtained a new and safe classifier using LBWT together with ANN.