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.

• Klíčová slova
• Open Data Ecosystem for the Neurosciences, ensemble methods, epilepsy, intracranial EEG, refractory epilepsy, seizure prediction,
• MeSH
• algoritmy * MeSH
• crowdsourcing MeSH
• elektroencefalografie MeSH
• elektrokortikografie metody   MeSH
• epilepsie parciální diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• prediktivní hodnota testů MeSH
• refrakterní epilepsie diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• senzitivita a specificita MeSH
• spánek MeSH
• strojové učení MeSH
• studie proveditelnosti MeSH
• záchvaty diagnóza   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH