A rule-based seizure prediction method for focal neocortical epilepsy

Abstract

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.

Fig. 1

Schematic diagram of the seizure prediction system.

Fig. 2

Thresholding results obtained using the mean (μ) and standard deviation (σ) computed over the feature values within the reference window. Spatial Feature Pattern Vector (SFPV) has been created using the sample seizure. D indicates a decrease in the median of the thresholded feature values within the preictal period with respect to that observed in the reference state. RM: remote channels; EP: epileptic channels.

Fig. 3

Schematic diagram of the rule-based decision making stage.

Fig. 4

Schematic diagram of the algorithm used in Spatial Combiner.

Fig. 5

Schematic diagram of the algorithm used in Feature Integrator I.

Fig. 6

Schematic diagram of the algorithm used in Feature Integrator II.

Fig. 7

Sensitivity versus false prediction rate for each patient (as indicated by patient number seen in Table 1) and for the grand average across patients (labeled in pink as Grand Average) for the seizure occurrence period of (a) 30 and (b) 50 min and SPH=10sec. For each patient, the box or line shows the range of variation of sensitivity versus false prediction rate when different randomly selected reference windows are used. Plus and minus signs used with the patients’ number denote the upper and lower bounds of the corresponding ranges of variation. The performances of the random and periodical prediction methods show values much lower than those found using our system.

Fig. 8

Connectivity maps characterizing the preictal strength of synchronization and the direction of interaction. The thickness of the arrows reflects the strength of interactions. For the sake of simplicity, we projected all the contacts on the same cross-sectional view. In these maps, numbers 1–3 and 4–6 refer to the electrodes implanted in the epileptogenic zone and remote areas, respectively.