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Detecting features of epileptogenesis in EEG after TBI using unsupervised diffusion component analysis

  • * Corresponding author: Dominique Duncan

    * Corresponding author: Dominique Duncan 
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  • 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.

    Mathematics Subject Classification: 68T10, 65F15, 92B99, 92C20, 92C55, 00A69.


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  • Figure 1.  Raw EEG data showing epileptiform spike activity at two time points

    Figure 2.  The corresponding embedding using Unsupervised Diffusion Component Analysis and eigenvectors 2, 3, and 5

    Figure 3.  An example of raw EEG data from 4 electrode contacts from 1 patient with epileptiform activity at one time point

    Figure 4.  The corresponding embedding using Unsupervised Diffusion Component Analysis (color represents time), and the yellow/orange points are separated from the other embedded points

    Figure 5.  An example of EEG data from 3 electrode contacts from another patient with more subtle spikes that are not clear from examining the raw data

    Figure 6.  The corresponding embedding using Unsupervised Diffusion Component Analysis

    Figure 7.  An example of raw EEG data from 3 electrode contacts from 1 patient with no epileptiform activity

    Figure 8.  The corresponding embedding using Unsupervised Diffusion Component Analysis

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