Revealing epilepsy type using a computational analysis of interictal EEG

Marinho A Lopes,Marc Goodfellow,Suejen Perani,Mark P Richardson,Siti N Yaakub,John R Terry

doi:10.1038/s41598-019-46633-7

Abstract

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.

Highlights

Epilepsy is a neurological disorder characterized by recurrent seizures[1]
We extracted a total number of 623 functional networks using the Phase Locking Factor (PLF)[21,22,23] each derived from a 20 second EEG segment
We explored whether generalized and focal epilepsies can be differentiated using interictal EEG

Summary

Introduction

Epilepsy is a neurological disorder characterized by recurrent seizures[1]. According to the International League Against Epilepsy (ILAE), the diagnosis of epilepsy comprises three levels[2]: the identification of seizure type[3], the classification of epilepsy type[2], and diagnosis of epilepsy syndrome, if possible. The diagnosis of generalized and focal epilepsy is based on clinical grounds, supported by EEG findings. Van Diessen et al.[11] used resting-state EEG to build a multivariable decision tree based on functional network properties that was capable of distinguishing children with focal epilepsy from healthy children. Verhoeven et al.[16] implemented an automated diagnosis tool to lateralize TLE based on apparently normal EEG Moving from these observational studies, we developed a framework to study the mechanisms by which network alterations lead to pathological activity[17]. We showed that a computational biomarker based on clinical resting-state EEG could support diagnosis of generalized epilepsies[17,18]

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