Daily resting‐state intracranial EEG connectivity for seizure risk forecasts. Issue 2 (21st December 2022)

• Record Type: Journal Article
• Authors: Cousyn, Louis; Messaoud, Rémy Ben; Lehongre, Katia; Frazzini, Valerio; Lambrecq, Virginie; Adam, Claude; Mathon, Bertrand; Navarro, Vincent; Chavez, Mario
• Is Part Of: Epilepsia. Volume 64:Issue 2(2023)
• Page Start: e23
• Page End: e29
• Publication Date: 2022-12-21
• Subjects: machine learning -- phase synchrony -- probabilistic forecasting -- SEEG -- seizure prediction; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.17480
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long‐term electroencephalographic (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 distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) 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 the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.