Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG

Abstract

Objective: Identification of consistent distinguishing features between preictal and interictal periods in the EEG is an essential step towards performing seizure prediction. We propose a novel method to separate preictal and interictal states based on the analysis of the high frequency activity of intracerebral EEGs in patients with mesial temporal lobe epilepsy.

Methods: Wavelet energy and entropy were computed in sliding window fashion from preictal and interictal epochs. A comparison of their organization in a 2 dimensional space was carried out using three features quantifying the similarities between their underlying states and a reference state. A discriminant analysis was then used in the features space to classify epochs. Performance was assessed based on sensitivity and false positive rates and validation was performed using a bootstrapping approach.

Results: Preictal and interictal epochs were discriminable in most patients on a subset of channels that were found to be close or within the seizure onset zone.

Conclusions: Preictal and interictal states were separable using measures of similarity with the reference state. Discriminability varies with frequency bands.

Significance: This method is useful to discriminate preictal from interictal states in intracerebral EEGs and could be useful for seizure prediction.