Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop

Abstract The Epilepsy Innovation Institute, Ei 2 , is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei 2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei 2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei 2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists to basic science researchers and regulators for a state of the science of assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables and biosensors as parameters for a seizure forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure forecasting algorithms. Significance Statement Unpredictability of seizures is a top issue for those living with epilepsy regardless of seizure frequency and type. There is the fear of not knowing when a seizure will start and not knowing what triggers the seizure onset. In August, the Epilepsy Innovation Institute (Ei 2 ) convened a diverse group of stakeholders to assess the state of the science on seizure forecasting algorithms. Seizure forecasting shifts away from categorical seizure prediction assessments of whether a seizure will or will not occur and instead focuses on identifying the brain state wherein there is a high probability of a seizure occurrence. Here, we discuss the outcomes of those discussions and next steps.