Daily resting-state intracranial EEG connectivity for seizure risk forecasts.
SEEG
machine learning
phase synchrony
probabilistic forecasting
seizure prediction
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
revised:
01
12
2022
received:
10
05
2022
accepted:
06
12
2022
pubmed:
10
12
2022
medline:
9
2
2023
entrez:
9
12
2022
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e23-e29Informations de copyright
© 2022 International League Against Epilepsy.
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