title: How well can epileptic seizures be predicted? An evaluation of a nonlinear method.
abstract: The unpredictability of the occurrence of epileptic seizures 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 previous study this method was reported to identify 'preictal 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 specificity 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 significant preictal dimension drop. In a second analysis, the relation between dimension drops within time windows 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 significant differences between interictal and preictal data sets.
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D004569:Electroencephalography / Q000379:methods; D004828:Epilepsies, Partial / Q000175:diagnosis / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted