Linear prediction modelling for the analysis of the epileptic EEG

Abstract

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures. This study deals with a preliminary investigation to detect epileptic components in the electroencephalogram (EEG) waveform, which results in a reduction of analysis time by the expert neurologist. As an alternative to the Fast Fourier Transform (FFT) spectral analysis approach, an Auto Regressive (AR), a Moving Average (MA) and an Auto Regressive Moving Average (ARMA) model-based spectral estimators can be used to process the EEG signal. An AR signal-processing model for the epileptic EEG is proposed. The AR modelling has been used to analyse physiological signals such as the human EEG. The interpretation of an autoregressive model as a recursive digital filter and its use in spectral estimation are considered. This is used to formulate an analysis model, based on Linear Prediction Coding (LPC). The theory behind the method is explained and the implementation is described. The algorithm is computationally efficient and can be implemented in real-time on a small microcomputer system for on-line analysis. Results produced by this method may be used for further analysis. © 2010 IEEE.