Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.

Objective: Seizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long-term use. This study presents the first validation of a seizure-forecasting system using ultra-long-term, non-invasive wearable data.

Methods: Eleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist-worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models-combining machine learning and cycle-based methods-were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons.

Results: The Seizure Warning System (SWS), designed for forecasting near-term seizures, and the Seizure Risk System (SRS), designed for forecasting long-term risk, both outperformed traditional models. In addition, the SRS reduced high-risk time by 29% while increasing sensitivity by 11%.

Significance: These improvements mark a significant advancement in making seizure forecasting more practical and effective.