ArticleScientific reports2021
Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06593951 (Registry and Natural History Study for Progressive Myoclonus Epilepsy Type 1), which is not on this map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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.
Registry and Natural History Study for Progressive Myoclonus Epilepsy Type 1 (EPM1)
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study.Journal of medical Internet research · 2026Article
- Circular mixture model-based characterization of time-of-day seizure patterns during presurgical monitoring.Scientific reports · 2026Article
- Timing is everything: Expert opinion on researching epilepsy rhythms by the ILAE Task Force on Chronobiology.Epilepsia · 2026Review
- A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting: Protocol for the ATMOSPHERE Study.JMIR research protocols · 2026Article
- The research progress of wearable digital health technologies in epilepsy management.Acta epileptologica · 2026Review
- Article
- Real-world epilepsy monitoring with ultra-long-term subcutaneous electroencephalography: A 15-month prospective study.Epilepsia · 2025Observational
- Wearable Artificial Intelligence for Epilepsy: Scoping Review.Journal of medical Internet research · 2025Article
- Autonomic biosignals, seizure detection, and forecasting.Epilepsia · 2025Review
- Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.Epilepsia · 2025Article
- An algorithm for seizure detection in rodents.Epilepsia open · 2025Article
- Ambient technology in epilepsy clinical practice.Epilepsia open · 2025Article
- Seizure prediction and forecasting: a scoping review.Current opinion in neurology · 2025Article
- ECG-based epileptic seizure prediction: Challenges of current data-driven models.Epilepsia open · 2025Article
- Improving epilepsy monitoring using long-term, in-home-bi-modal seizure monitoring device: clinical utilities and obstacles from a pilot study.Frontiers in neurology · 2025Article
- Necessary for seizure forecasting outcome metrics: Seizure frequency and benchmark model.Epilepsy research · 2024Article
- Epileptic seizure forecasting with wearable-based nocturnal sleep features.Epilepsia open · 2024Article
- User experience of a seizure risk forecasting app: A mixed methods investigation.Epilepsy & behavior : E&B · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 5 countries.
Funding
Abstract
The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72-0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.
Indexed as
Identifiers
What OpenQuestion holds
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.