Bicoherence of Intracranial EEG: A Novel Precursor of Seizure Activity in Canine Epilepsy

Seizure forecasting could improve the quality of life of patients with refractory epilepsy. Seizure prediction relies on the adequate identification of seizure activity precursors from electroencephalography (EEG). However, no single feature is currently capable of characterizing brain dynamics prio...

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Bibliographic Details
Published in2018 IEEE Life Sciences Conference (LSC) pp. 93 - 96
Main Authors Gagliano, Laura, Assi, Elie Bou, Sawan, Mohamad, Nguyen, Dang K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Analysis of variance
bicoherence
Dogs
Electroencephalography
Epilepsy
Feature extraction
Forecasting
Frequency measurement
higher order spectra
preictal period
Seizure forecasting
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Summary:Seizure forecasting could improve the quality of life of patients with refractory epilepsy. Seizure prediction relies on the adequate identification of seizure activity precursors from electroencephalography (EEG). However, no single feature is currently capable of characterizing brain dynamics prior to seizures. This work evaluates the suitability of the bicoherence, a measure of non-linear phase-phase coupling interactions based on the higher order spectra (HOS), for seizure prediction in canine epilepsy. Quantitative features were extracted from the bicoherence of 30-sec segments and 2 statistical tests assessed the existence of significant differences between preictal and interictal HOS features. Results of ANOVA tests displayed a statistically significant change during the preictal period as tracked by quantitative bicoherence-extracted measures. Interestingly, over 50% of seizures displayed a significant HOS change in both hemispheres for all 3 dogs diagnosed with focal epilepsy which suggests the possibility that preictal activity may be detectable in remote regions distanced from the seizure onset zone. This work demonstrates the existence of a preictal period characterized by a statistically significant change in bicoherence, highlighting the feasibility of seizure forecasting based on HOS features.
DOI:10.1109/LSC.2018.8572206