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

Authors: Mona Nasseri, Rachel E. Stirling, Pedro F. Viana, Jie Cui, Ewan Nurse, Philippa J. Karoly, Vaclav Kremen, Matthias Dümpelmann, Gregory A. Worrell, Dean R. Freestone, Mark P. Richardson and Benjamin H. Brinkmann
Epilepsia Standard
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Abstract

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.