Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.
Decision curve analysis
Drug resistant epilepsy
Machine learning
Surgical outcome prediction
Temporal lobe resection
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Sep 2024
18 Sep 2024
Historique:
received:
23
07
2024
accepted:
05
09
2024
medline:
19
9
2024
pubmed:
19
9
2024
entrez:
18
9
2024
Statut:
epublish
Résumé
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.
Identifiants
pubmed: 39294238
doi: 10.1038/s41598-024-72249-7
pii: 10.1038/s41598-024-72249-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
21771Informations de copyright
© 2024. The Author(s).
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