Automatic estimation of the optimal AR order for epilepsy analysis using EEG signals

Abstract

In this paper, we propose a computationally efficient method to estimate the optimal order of the autoregressive (AR) modeling of electroencephalographic (EEG) signals in order to use the AR coefficients as features for the analysis of EEG signals and the automatic detection of epileptic seizures. The estimation of the optimal AR-order is made using regression analysis of statistical features extracted from the samples of the EEG signals. The proposed method was evaluated in both background and ictal EEG segments using recordings from 10 epileptic patients. The experimental evaluation showed that the mean absolute error of the estimated optimal AR order is approximately 4 units.