Seizure forecasting: Where do we stand?
monitoring devices
multimodal monitoring
network theory of epilepsy
quality of life
seizure control
seizure cycles
seizure prediction
seizure risk
wearables
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
02
02
2023
accepted:
06
02
2023
medline:
25
12
2023
pubmed:
14
2
2023
entrez:
13
2
2023
Statut:
ppublish
Résumé
A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.
Identifiants
pubmed: 36780237
doi: 10.1111/epi.17546
pmc: PMC10423299
mid: NIHMS1884171
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
S62-S71Subventions
Organisme : NIH HHS
ID : NIH NS109062
Pays : United States
Organisme : Medical Research Council
ID : MR/N026063/1
Pays : United Kingdom
Organisme : NINDS NIH HHS
ID : R01 NS109062
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS094399
Pays : United States
Organisme : NIH HHS
ID : NIH NS094399
Pays : United States
Informations de copyright
© 2023 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.
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