Seizure detection and forecasting competitions were run on Kaggle.com using open access chronic ambulatory intracranial EEG (iEEG) from 5 canines with naturally occurring epilepsy and two humans undergoing prolonged wide-bandwidth iEEG monitoring. The competitions were sponsored by the National Institutes of Health, the American Epilepsy Society, and the Epilepsy Foundation.
The seizure detection contest ran from May to August 2014. Ambulatory iEEG data clips 1 second in duration were provided from seizures and from interictal, seizure-free epochs. Data clips were extracted from chronic ambulatory recordings from four canines with naturally occurring epilepsy, and eight patients undergoing intracranial monitoring as part of their presurgical evaluation for epilepsy. Contestants provided two classifications for the clips: seizure vs. interictal, and early seizure vs. interictal or late seizure. Submissions were ranked based on the area under the ROC curve.
The seizure forecasting contest ran from August to November 2014. Data was provided to participants as 10-minute interictal and preictal clips, with approximately half of the 60Gb data bundle labeled (interictal/Preictal) for algorithm training and half unlabeled for evaluation. In total 654 participants submitted 17,856 classifications of the unlabeled test data. The contestants developed custom algorithms and uploaded their classifications (interictal/Preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leaderboard. After the competition ended the top placing contestants were invited to run their algorithms on unlabeled held-out data from four of the canines, in order to assess the robustness and broader applicability of these algorithms.