Title: Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem,
Abstract: Epilepsy is a neurological disorder arising from anomalies of the electrical
activity in the brain, affecting about 0.5--0.8\% of the world population.
Several studies investigated the relationship between seizures and brainwave
synchronization patterns, pursuing the possibility of identifying interictal,
preictal, ictal and postictal states. In this work, we introduce a graph-based
model of the brain interactions developed to study synchronization patterns in
the electroencephalogram (EEG) signals. The aim is to develop a
patient-specific approach, also for a real-time use, for the prediction of
epileptic seizures' occurrences. Different synchronization measures of the EEG
signals and easily computable functions able to capture in real-time the
variations of EEG synchronization have been considered. Both standard and
ad-hoc classification algorithms have been developed and used. Results on scalp
EEG signals show that this simple and computationally viable processing is able
to highlight the changes in the synchronization corresponding to the preictal
state.