In Epilepsia
Forecasting seizure risk aims to detect pro-ictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term EEG recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure - during a vigilance-controlled period - to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow to distinguish interictal (no seizure) from preictal (seizure within the next 24hrs) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide best prediction performances (AUC≥0.7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier and skill Brier scores of 0.11 and 0.74, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.
Cousyn Louis, Ben Messaoud Rémy, Lehongre Katia, Frazzini Valerio, Lambrecq Virginie, Adam Claude, Mathon Bertrand, Navarro Vincent, Chavez Mario
2022-Dec-08
SEEG, Seizure prediction, machine learning, phase synchrony, probabilistic forecasting
