ArticleEpilepsia2025
Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07012148 (FORESITE), which is not on this map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
FORESITE (FOREcasting Seizures to Initiate ThErapy): Optimizing Management of Drug-Resistant Epilepsy Using Seizure Forecasts Via Subscalp EEG and Wearables
Who cites it
10 citing papers in PubMed.
- Advancing the Paradigm of Temporal Lobe Epilepsy as a Network Disease: The Promise of Biomarkers and Targeted Disease Modification.International journal of molecular sciences · 2026Review
- The future of epilepsy care in the United Kingdom: A roadmap for technology-enabled transformation.Epilepsia open · 2026Review
- Article
- Article
- Cardiorespiratory cross-frequency coupling biomarker for sudden unexpected death in epilepsy.Epilepsia · 2026Article
- Review
- Out of the lab, into real life: Evaluating at-home EEG self-monitoring.Epilepsia open · 2026Article
- Seizure burden and treatment efficacy evaluation in anti-LGI1 autoimmune encephalitis patients using wearable device: a pilot feasibility study.BMJ neurology open · 2026Article
- Investigation of stress hormones across multiday seizure cycles.Brain communications · 2026Article
- Rhythms in Longitudinal Thalamic Recordings are Linked to Seizure Risk.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectiveSeizure 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.
methodsEleven 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.
resultsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.