Objective: In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy. Methods: Five 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 316. h of iEEG data containing 49 seizures recorded in 11 patients with medically intractable focal neocortical epilepsy. Results: For seizure occurrence periods of 30 and 50. min 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. Conclusions: The 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. Significance: The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.
Bibliographical noteFunding Information:
We wish to thank Kaitlin Cassady and Marc Pisansky for proofreading of the manuscript. We also thank Eren Gultepe for critical reading of the manuscript. This work was supported in part by NIH EB007920 and EB006433 , and a grant from the Minnesota Partnership for Biotechnology and Medical Genomics .
Copyright 2012 Elsevier B.V., All rights reserved.
- Focal epilepsy
- Intracranial EEG
- Nonlinear dynamics
- Seizure prediction