ArticleEpilepsia2023
Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.
Article in Epilepsia, 2023. 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 21 papers.
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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
21 citing papers in PubMed, 44 citations in OpenAlex.
- Article
- Circular mixture model-based characterization of time-of-day seizure patterns during presurgical monitoring.Scientific reports · 2026Article
- A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting: Protocol for the ATMOSPHERE Study.JMIR research protocols · 2026Article
- Seizure detection using ultra-long-term subcutaneous electroencephalography: A deep learning CNN-BiLSTM approach.Epilepsia · 2026Article
- Real-world epilepsy monitoring with ultra-long-term subcutaneous electroencephalography: A 15-month prospective study.Epilepsia · 2025Observational
- Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.Epilepsia · 2025Article
- Seizure prediction and forecasting: a scoping review.Current opinion in neurology · 2025Article
- Long-Term Ambulatory Intracranial EEG.Stereotactic and functional neurosurgery · 2025Review
- A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study).JMIR research protocols · 2024Article
- User experience of a seizure risk forecasting app: A mixed methods investigation.Epilepsy & behavior : E&B · 2024Article
- Addressing data limitations in seizure prediction through transfer learning.Scientific reports · 2024Article
- Addressing data limitations in seizure prediction through transfer learning.Scientific reports · 2024Article
- Article
- Comparison between epileptic seizure prediction and forecasting based on machine learning.Scientific reports · 2024Article
- Prospective validation of a seizure diary forecasting falls short.medRxiv : the preprint server for health sciences · 2024Article
- Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction.Frontiers in neuroscience · 2024Article
- Editorial: Seizure forecasting tools, biomarkers and devices.Frontiers in neuroscience · 2024Article
- Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function.Frontiers in neuroscience · 2024Review
- Seizure forecasting: Where do we stand?Epilepsia · 2023Article
- Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.Epilepsia · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 7 institutions in 6 countries.
Funding
Abstract
objectiveOne of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG.
methodsWe analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation.
resultsDepending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
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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.