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In 12 epileptic patients suffering from "absences" 8-channel EEG was recorded by telemetry. The autoregressive model was applied to the signal and the prediction coefficients being the basis for calculation of the poles of the predictor. The location of the poles in the z- and s-planes was described as a function of time for 0.1 s steps along the pre-seizure EEG. In 10 of the 12 patients, and in 25 of the 28 recorded seizures this presentation of the poles of the predictor showed specific pattern linked with the occurrence of the seizure. The trajectory of the "most mobile pole" during the pre-seizure period could aid in the prediction of the seizure by several seconds.

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Multivariate spectral estimation based on parametric modelling has been applied to epileptic surface EEG in order to detect EEG changes that occur prior to the clinical outbreak of the seizure. A better time/frequency resolution has been achieved using residual energy ratios (Dickinson's method). Prediction of oncoming seizures was based on detection of increased preictal synchronisation by calculation of coherence and pole trajectories. The method has been tested on simulated EEG data and on real EEG data from patients with primary generalised epilepsy. Prediction times of 1-6 s have been found in several seizures from five patients.

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Estimation of autospectra and coherence and phase spectra of seizure EEG, using the FFT technique, will cause "smearing" of the rapid dynamic changes which occur during the seizure. This is inherent to FFT spectral estimation, due to the averaging process which is necessary in order to get consistent spectral estimates. A different approach suggested in the present study is to carry out multivariate autoregressive modeling of the multichannel seizure EEG, combined with adaptive segmentation. In order to obtain good estimates in cases of short record length, the vectorial AR modeling was based on residual energy ratios. The method has been tested on multichannel seizure EEG recordings from rats with focal epilepsy, caused by intracerebral administration of Kainic acid, and in depth EEG recordings in patients with temporal lobe epilepsy.