  Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain
injury (TBI) and is a major public health problem that has an estimated
incidence that ranges from 2%-50%, depending on the severity of the TBI.
Currently, the pathomechanism that in-duces epileptogenesis in TBI patients is
unclear, and one of the most challenging goals in the epilepsy community is to
predict which TBI patients will develop epilepsy. In this work, we used
diffusion-weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy
Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)to measure and
analyze fractional anisotropy (FA), obtained from tract-based spatial statistic
(TBSS) analysis. Then we used these measurements to train two support vector
machine (SVM) models to predict which TBI patients have developed epilepsy. Our
approach, tested on these 14 patients with a leave-two-out cross-validation,
allowed us to obtain an accuracy of 0.857 $\pm$ 0.18 (with a 95% level of
confidence), demonstrating it to be potentially promising for the early
characterization of PTE.
