A rule-based seizure prediction method for focal neocortical epilepsy

Abstract

ObjectiveIn the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy. MethodsFive univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-s segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316h of iEEG data containing 49 seizures recorded in 11 patients with medically intractable focal neocortical epilepsy. ResultsFor seizure occurrence periods of 30 and 50min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors. ConclusionsThe nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study. SignificanceThe present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.