Nonlinear models for detecting epileptic spikes

We present a technique for automatic detection of epileptic spikes in elect roencephalogram (EEG) recordings. We use a nonlinear modeling method based on information theory that enables us to defect rapidly and accurately epil eptic behavior in the EEG signal. An optimal embedding dimension of the mod el is determined by the minimum in the mean square error between EEG signal s and the corresponding model prediction. Our approach is illustrated by an application to two EEG time series: (i) interictal activity from a focal e pileptic patient, and (ii) a petit mal from a generalized epilepsy patient. [S1063-651X(98)15612-X].