Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach

Abstract

Objectives: The aim of this study is to develop a model based seizure prediction method.

Methods: A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied.

Results: Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively.

Conclusions: The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction.

Significance: The present findings suggest that the model-based approach may aid prediction of seizures.

Keywords: Excitatory and inhibitory interaction; Focal epilepsy; Intracranial EEG; Neural mass model; Seizure prediction.

Fig. 1

Canonical neural mass model (A), and schematic diagram of the neural mass model (B) described by the state Eq. (1). ExIN, excitarory interneurons; IbIN, inhibitory interneurons; PC, pyramidal cells. See Table 2 for parameter description.

Fig. 2

Schematic diagram of the model-based seizure prediction system.

Fig. 3

Sensitivity versus false prediction rate for each patient (as indicated by patient number in Table 1) and for the grand average across patients (labeled in pink as grand average) using seizure occurrence periods of 30 min (A and C), and 50 min (B and D) and a seizure prediction horizon of 10 s. Results for the maximum sensitivity and maximum specificity strategies are shown in (A–B) and (C–D), respectively. For each patient, a box shows the range of variation of sensitivity versus false prediction rate obtained using different randomly selected reference windows. It is of note that a line (e.g. for patient 11) or a dot (e.g. for patient 4) represents the results where no variations in sensitivities or/and in false prediction rates were observed. 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 as described in the Supplementary Material are shown in all four panels (A–D). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Spectra for preictal and reference state, averaged over the electrodes within the epileptic zone and remote areas.