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

Chip Reuben, Philippa Karoly, Dean R. Freestone, Andriy Temko, Alexandre Barachant, Feng Li, Gilberto Titericz Jr., Brian W. Lang, Daniel Lavery, Kelly Roman, Derek Broadhead, Gareth Jones, Qingnan Tang, Irina Ivanenko, Oleg Panichev, Timothée Proix, Michal Náhlík, Daniel B. Grunberg, David B. Grayden, Mark J. CookLevin Kuhlmann

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)7-12
Number of pages6
Issue number2
Publication statusPublished - Feb 2020


  • ensemble methods
  • epilepsy
  • intracranial EEG
  • Open Data Ecosystem for the Neurosciences
  • refractory epilepsy
  • seizure prediction

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