Nearly one third of all epilepsy patients are not able to control their epileptic seizures with
standard epilepsy medication. For these patients, surgical treatment is the main alternative,
which consists on the resection or ablation of the Epileptogenic Zone. Despite the usage of
advanced methods for surgical evaluation like intracranial EEG or PET-MRI, at least half of
all intervened patients will end up recurring in the long term. Many computational approaches
have been taken in order to ...
Nearly one third of all epilepsy patients are not able to control their epileptic seizures with
standard epilepsy medication. For these patients, surgical treatment is the main alternative,
which consists on the resection or ablation of the Epileptogenic Zone. Despite the usage of
advanced methods for surgical evaluation like intracranial EEG or PET-MRI, at least half of
all intervened patients will end up recurring in the long term. Many computational approaches
have been taken in order to aid in the prediction of the outcome of epilepsy surgery but to this
day, none of them have managed to obtain a perfect accuracy. In this study, a novel approach is
taken in conceptualizing the interaction between the non-epileptogenic and epileptogenic brain
networks as a competition to dominate each other. By implementing Game Theory notions
in conjunction with a set of Machine Learning algorithms it was possible to observe how the
influence of the epileptogenic regions towards the non-epileptogenic ones grew over time until it
produced the onset of a seizure. Furthermore, a score was built in order quantitatively assess the
probability of obtaining a favorable surgical outcome. The score was able to fully discriminate
subjects with good and bad outcome in cohort of 21 patients. Lastly, using an adapted Genetic
Algorithm it was possible to find alternative resection sites that would improve the chances of
obtaining seizure freedom in 2 out of 7 patients that initially obtained a bad outcome.
+