Editorial: Seizure Forecasting and Detection: Computational Models, Machine Learning, and Translation Into Devices

Abstract

For the 50 million people with epilepsy worldwide, seizures are seemingly unpredictable events that can be associated with significant morbidity and mortality. Reliable methods to identify and anticipate seizures could enable powerful new therapeutic strategies. Owing to the paroxysmal nature of seizures and the challenges associated with long-term monitoring of brain activity, however, such methods have been elusive-until now. Recent advances in computational methods and device technology have reshaped the epilepsy landscape and made accurate detection and forecasting of seizures a reality. This Research Topic was launched with the aims of highlighting the latest of these advances and portraying the current state of seizure detection and forecasting through the viewpoints of patients and researchers. The 18 articles in this special issue comprise three Perspectives, three Reviews, and 12 Original Research articles. This collection begins with a first-person account from [Moss et al.]() of one family's experience with drug-resistant epilepsy, including their appraisal of current seizure detection devices; challenges encountered related to privacy, stigma, and comfort; and the need for greater collaboration between patients and researchers, [Grzeskowiak and Dumanis]() survey a large sample of caregivers and adults living with epilepsy to assess directly their perspectives on seizure forecasting, including the optimal forecast horizon, the types of information that would be most useful for day-to-day planning, and the potential risks of forecasting tools. [Hubbard et al.]() and [Brinkmann et al.]() review the current status, technical challenges, and performance characteristics of contemporary seizure detection devices. Both groups highlight the critical need for research on several fronts: robustly designed clinical validation studies; algorithms to detect seizure types in addition to convulsive seizures (CS); better incorporation of meaningful clinical outcomes-such as morbidity, mortality, and quality of life-into algorithm evaluation; and more emphasis by developers on patient-centered considerations prior to beta-testing of new algorithms.