Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem
This work addresses the challenge of real-time, patient-specific seizure prediction for epilepsy patients, but it appears incremental as it builds on existing research into synchronization patterns.
The authors tackled the problem of predicting epileptic seizures by analyzing EEG synchronization patterns, developing a graph-based model and classification algorithms that successfully highlighted changes in synchronization corresponding to the preictal state in scalp EEG signals.
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.