Seizure prediction: making mileage on the long and winding road

This scientific commentary refers to ‘Crowdsourcing reproducible seizure forecasting in human and canine epilepsy’, by Brinkmann et al. (doi:10.1093/brain/aww045). Epilepsy is characterized by recurrent spontaneous seizures that seem to occur without warning, like a bolt from the blue. Most patients with epilepsy spend over 99.9% of their time in a perfectly normal brain state. But even though epileptic seizures constitute less than 0.1% of their lifetime, it is the apparent unpredictability of these events that puts an immense psychological burden on patients and substantially compromises their quality of life. A reliable method of forecasting seizure occurrence would thus greatly diminish psychological stress for patients. Moreover, it could lead to novel therapeutic options such as on-demand treatment and ultimately even closed-loop interventional devices to prevent impending seizures from occurring (Morrell and Halpern, 2016). In this issue of Brain , Brinkmann and co-workers have used the concept of crowdsourcing to tackle the problem of seizure prediction (Brinkmann et al. , 2016). They hosted epochs of continuous multi-day recordings of intracranial EEG (iEEG) activity from both humans and canines on a website (www.kaggle.com), and started a competition open to the public in which the aim was to correctly characterize these data clips as preictal (indicative of an impending seizure) or interictal (in this study, at least 1 week away from any seizure). Participating teams were provided with a set of training data consisting of clips that were labelled as being preictal or interictal. Using these data clips, they could train and optimize their algorithms before running them on the other clips provided—the testing data. For each of these remaining clips, the candidate algorithms had to estimate the probability that the clip was preictal. Receiver operating …