Automated algorithms for seizure forecast: a systematic review and meta-analysis.
Automated seizure forecast
Epilepsy
Seizure likelihood
Seizures
Systematic review
mHealth
Journal
Journal of neurology
ISSN: 1432-1459
Titre abrégé: J Neurol
Pays: Germany
ID NLM: 0423161
Informations de publication
Date de publication:
06 Sep 2024
06 Sep 2024
Historique:
received:
20
06
2024
accepted:
18
08
2024
revised:
16
08
2024
medline:
6
9
2024
pubmed:
6
9
2024
entrez:
6
9
2024
Statut:
aheadofprint
Résumé
This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance. Two meta-analyses were performed, one evaluating area under the ROC curve (AUC) and another Brier Skill Score (BSS). Eighteen studies met the eligibility criteria, totaling 43 included algorithms. A total of 419 patients participated in the studies, and 19442 seizures were reported across studies. Of the analyzed algorithms, 23 were eligible for the meta-analysis with AUC and 12 with BSS. The overall mean AUC was 0.71, which was similar between the studies that relied solely on surrogate measures of the preictal state, on cyclic distributions of events, and on a combination of these. BSS was also similar for the three types of input data, with an overall mean BSS of 0.13. This study provides a characterization of the state of the art in seizure forecast algorithms along with their performances, setting a benchmark for future developments. It identified a considerable lack of standardization across study design and evaluation, leading to the proposal of guidelines for the design of seizure forecast solutions.
Identifiants
pubmed: 39240346
doi: 10.1007/s00415-024-12655-z
pii: 10.1007/s00415-024-12655-z
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Fundação para a Ciência e Tecnologia (FCT)
ID : 2022.12369.BD
Organisme : Fundação para a Ciência e Tecnologia (FCT)
ID : 2021.08297.BD
Organisme : Ministério da Ciência, Tecnologia e Ensino Superior
ID : UIDB/50008/2020
Informations de copyright
© 2024. The Author(s).
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