Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG.
Algorithms
Crowdsourcing
Drug Resistant Epilepsy
/ diagnosis
Electrocorticography
/ methods
Electroencephalography
Epilepsies, Partial
/ diagnosis
Feasibility Studies
Female
Humans
Machine Learning
Male
Middle Aged
Predictive Value of Tests
Reproducibility of Results
Seizures
/ diagnosis
Sensitivity and Specificity
Sleep
Young Adult
Open Data Ecosystem for the Neurosciences
ensemble methods
epilepsy
intracranial EEG
refractory epilepsy
seizure prediction
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
15
07
2019
revised:
08
12
2019
accepted:
09
12
2019
pubmed:
29
12
2019
medline:
11
7
2020
entrez:
29
12
2019
Statut:
ppublish
Résumé
Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e7-e12Subventions
Organisme : National Health and Medical Research Council
ID : GNT1160815
Pays : International
Organisme : Epilepsy Foundation
Pays : International
Informations de copyright
Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.
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