SchulzeBonhage A. How well can epileptic seizures be predicted? An evaluation of a nonlinear method

The unpredictability of the occurrence of epileptic seiz-ures contributes to the burden of the disease to a major degree. Thus, various methods have been proposed to predict the onset of seizures based on EEG recordings. A nonlinear feature motivated by the correlation dimension is a seemingly promising approach. In a pre-vious study this method was reported to identify `preic-tal dimension drops ' up to 19 min before seizure onset, exceeding the variability of interictal data sets of 30±50 min duration. Here we have investigated the sensitivity and speci®city of this method based on invasive long-term recordings from 21 patients with medically intractable partial epilepsies, who underwent invasive pre-surgical monitoring. The evaluation of interictal 24-h recordings comprising the sleep±wake cycle showed that only one out of 88 seizures was preceded by a sig-ni®cant preictal dimension drop. In a second analysis, the relation between dimension drops within time win-dows of up to 50 min before seizure onset and interictal periods was investigated. For false-prediction rates below 0.1/h, the sensitivity ranged from 8.3 to 38.3% depending on the prediction window length. Overall, the mean length and amplitude of dimension drops showed no signi®cant differences between interictal and preictal data sets