Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
GNT1160815
National Health and Medical Research Council - International
Epilepsy Foundation - International
PubMed
31883345
DOI
10.1111/epi.16418
Knihovny.cz E-resources
- Keywords
- Open Data Ecosystem for the Neurosciences, ensemble methods, epilepsy, intracranial EEG, refractory epilepsy, seizure prediction,
- MeSH
- Algorithms * MeSH
- Crowdsourcing MeSH
- Electroencephalography MeSH
- Electrocorticography methods MeSH
- Epilepsies, Partial diagnosis MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Predictive Value of Tests MeSH
- Drug Resistant Epilepsy diagnosis MeSH
- Reproducibility of Results MeSH
- Sensitivity and Specificity MeSH
- Sleep MeSH
- Machine Learning MeSH
- Feasibility Studies MeSH
- Seizures diagnosis MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
Areté Associates Arlington VA USA
Center for Neurorestoration and Neurotechnology U S Department of Veterans Affairs Providence RI USA
Department of Medicine St Vincent's Hospital The University of Melbourne Parkville Australia
Department of Neuroscience Brown University Providence RI USA
Department of Physics National University of Singapore Singapore Singapore
Faculty of Information Technology Monash University Clayton Australia
Irish Centre for Fetal and Neonatal Translational Research University College Cork Cork Ireland
See more in PubMed
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