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Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG

C. Reuben, P. Karoly, DR. Freestone, A. Temko, A. Barachant, F. Li, G. Titericz, BW. Lang, D. Lavery, K. Roman, D. Broadhead, G. Jones, Q. Tang, I. Ivanenko, O. Panichev, T. Proix, M. Náhlík, DB. Grunberg, DB. Grayden, MJ. Cook, L. Kuhlmann,

. 2020 ; 61 (2) : e7-e12. [pub] 20191228

Language English Country United States

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc20025224

Grant support
GNT1160815 National Health and Medical Research Council - International
Epilepsy Foundation - International

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.

References provided by Crossref.org

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$a Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
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$a Kuhlmann, Levin $u Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Parkville, Australia. Faculty of Information Technology, Monash University, Clayton, Australia.
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