Seizure Prediction Is Possible–Now Let's Make It Practical

People livingwith uncontrolled epilepsy face the constant fear of seiprediction/forecasting is a reality, and that there aremanypotential suczures. The mental toll of anticipation and uncertainty surrounding the random occurrence of seizures may actually be more stressful than the embarrassment, injury, or death caused by the seizure itself. In a recent US poll of patients and caregivers by the Epilepsy Foundation, the unpredictability of seizures was the most impactful aspect of epilepsy, and there is great interest in providing warning to patients when seizures are more likely—a “seizure prediction” device (Dumanis et al., 2017). However, whether such a device is feasible, both scientifically and practically, has been an unanswered question. Seizure prediction has been the goal of many researchers ever since digital EEG arrived in the 1990's. Early work presented amyriad of individual algorithms but had concerns about validation, then progressed greatly once centers began sharing data and developed methods to assure statistical rigor (Mormann et al., 2007; Snyder et al., 2008). Those new guidelines culminated in a clinical trial in Melbourne Australia, in which patients had continuous EEG recorded from indwelling intracranial electrodes over several months, attached to a portable device designed to signal the risk of imminent seizures (Cook et al., 2013). That trial demonstrated that seizure prediction was possible in some people, though the trial and the sponsor company (NeuroVista) both terminated, in large part because therewere several patients inwhom the device did notworkwell using the initial prediction algorithms.While thismay appear like a failed trial, it provided the crucial groundwork for the field to progress. One new development is in terminology: the strategy is really better described as “forecasting” rather than prediction, as identifying periods of increased risk is more physiological than true prediction of a seizure event (especially since a patient may take measures to prevent a seizure). The next stepwas further optimization and aworldwide seizure prediction competition using data from epileptic dogs and two patients from the Melbourne study, which demonstrated remarkable success (Brinkmann et al., 2016). A follow up competition on Kaggle. com recently completed with many algorithms that clearly beat a chance predictor, this timewith data from the patients thatwere unsuccessful in the original Melbourne trial. These results prove that seizure