ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2022
Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06855901 (Home Ultra-longterm EEG Monitoring for Rare Epilepsies and Developmental and Epileptic Encephalopathies. an Open Label Nonpharmacological Interventional Prospective Study by Means of Minimally Invasive Wearable EEG Device), which is not on this map. Cited by 17 papers.
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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.
Home Ultra-longterm EEG Monitoring for Rare Epilepsies and Developmental and Epileptic Encephalopathies. an Open Label Nonpharmacological Interventional Prospective Study by Means of Minimally Invasive Wearable EEG Device
Who cites it
17 citing papers in PubMed, 30 citations in OpenAlex.
- A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.Epilepsia open · 2026Trial
- When seizure counts are not seizures: Measurement error and its implications for epilepsy management and driving policy.Epilepsia · 2026Article
- Monitoring Antiseizure Medication Change Using Ultra Long-Term Electroencephalogram: A Multicenter Study.Annals of neurology · 2026Article
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- Subdermal Insertable Sensors for Health Monitoring: Applications, Challenges, and Emerging Technologies.Annals of biomedical engineering · 2026Review
- How Much Does the Reduced EEG Montage Matter for Seizure Detection?: A Large-Cohort Simulation Study.medRxiv : the preprint server for health sciences · 2026Article
- Seizure detection using ultra-long-term subcutaneous electroencephalography: A deep learning CNN-BiLSTM approach.Epilepsia · 2026Article
- Ultra long-term EEG monitoring for developmental and epileptic encephalopathies: protocol for a prospective study using subscalp EEG.Clinical neurophysiology practice · 2026Article
- Unilateral ultra long-term subcutaneous EEG monitoring in drug-refractory idiopathic generalized epilepsy.Epilepsia · 2026Article
- Real-world epilepsy monitoring with ultra-long-term subcutaneous electroencephalography: A 15-month prospective study.Epilepsia · 2025Observational
- Validation of a discrete electrographic seizure detection algorithm for extended-duration, reduced-channel wearable EEG.Epilepsia · 2025Article
- Real world testing and cost-effectiveness analysis of subcutaneous EEG (REAL-ASE): Protocol for a prospective multicentre interventional trial.Epilepsia open · 2025Article
- Epileptiform Activity and Seizure Risk Follow Long-Term Non-Linear Attractor Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Seizure Detection Devices.Journal of clinical medicine · 2025Review
- The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.Frontiers in neurology · 2024Review
- Out-of-hospital multimodal seizure detection: a pilot study.BMJ neurology open · 2023Article
- Getting Under the Skin of Seizure Monitoring: A Subcutaneous EEG Tool to Keep a Tally Over the Long Haul.Epilepsy currentsArticle
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
Abstract
objectiveUltra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm.
methodsA multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts.
resultsData reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0).
conclusionsOur findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SIGNIFICANCE: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.
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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.