DETECTING FEATURES OF EPILEPTOGENESIS IN EEG AFTER TBI USING UNSUPERVISED DIFFUSION COMPONENT ANALYSIS.

Dominique Duncan,Paul Vespa,Arthur W Toga

doi:10.3934/dcdsb.2018010

Abstract

Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms, and the development of antiepileptogenic interventions could potentially prevent or cure these epilepsies [3, 13]. The discovery of potential antiepileptogenic treatments is currently a high research priority. Clinical validation would require a means to identify populations of patients at particular high risk for epilepsy after a potential epileptogenic insult to know when to treat and to document prevention or cure. We investigate the development of post-traumatic epilepsy (PTE) following traumatic brain injury (TBI), because this condition offers the best opportunity to know the time of onset of epileptogenesis in patients. Epileptogenesis is common after TBI, and because much is known about the physical history of PTE, it represents a near-ideal human model in which to study the process of developing seizures. Using scalp and depth EEG recordings for six patients, the goal of our analysis is to find a way to quantitatively detect features in the EEG that could potentially help predict seizure onset post trauma. Unsupervised Diffusion Component Analysis [5], a novel approach based on the diffusion mapping framework [4], reduces data dimensionality and provides pattern recognition that can be used to distinguish different states of the patient, such as seizures and non-seizure spikes in the EEG. This method is also adapted to the data to enable the extraction of the underlying brain activity. Previous work has shown that such techniques can be useful for seizure prediction [6]. Some new results that demonstrate how this algorithm is used to detect spikes in the EEG data as well as other changes over time are shown. This nonlinear and local network approach has been used to determine if the early occurrences of specific electrical features of epileptogenesis, such as interictal epileptiform activity and morphologic changes in spikes and seizures, during the initial week after TBI predicts the development of PTE.

Highlights

The mechanisms underlying human acquired epileptogenesis remain poorly understood, and an innovative approach to study the process from inception to manifest clinical epilepsy is needed
Epileptogenesis is common after moderate-severe traumatic brain injury (TBI) [1] and begins early, offering a window of opportunity to test the efficacy of potential antiepileptogenic (AEG) drugs [10, 24, 25]
We present a recently developed algorithm that we modify for EEG data to detect spikes that may be indicators of epileptogenesis after TBI

Summary

Introduction

The mechanisms underlying human acquired epileptogenesis remain poorly understood, and an innovative approach to study the process from inception to manifest clinical epilepsy is needed. We have selected post-traumatic epilepsy (PTE) as a model to pursue this understanding because the timing of the potential epileptogenic insult is known, and the period of epileptogenesis can be determined. Epileptogenesis is common after moderate-severe traumatic brain injury (TBI) [1] and begins early, offering a window of opportunity to test the efficacy of potential antiepileptogenic (AEG) drugs [10, 24, 25]. In order to design economically feasible antiepileptogenic clinical trials, it is necessary to identify reliable biomarkers that: 1) predict later PTE, to enrich the subject population; 2) stage the epileptogenic process, to determine the timing of intervention; and 3) diagnose epilepsy, to provide biomarkers. Our hypothesis is that early post-traumatic epileptic EEG activity indicates the presence of an epileptogenic process in patients after moderate-severe TBI. The secondary hypothesis is that epileptogenesis can be diagnosed during the latent period, prior to the establishment of PTE, creating a potential window for preventative or disease modifying therapies

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