Adaptive predictive modelling for the analysis of the epileptic EEG

Abstract

A signal processing model for the epileptic EEG is used to formulate an analysis model, based on linear prediction. This formulation is implemented as a number of adaptive filters and applied for the detection of epileptic spikes. The theory behind the method is explained and the implementation described. Results are presented and compared for two adaptive filter realizations. The computationally efficient algorithm can be implemented in real-time on a small microcomputer system for on-line analysis. Intermediate results produced by this method may be used for further analysis. Generalization for the detection of other EEG transients and the removal of artifacts can be achieved easily.<>