ArticleEpilepsia2025
Tailoring antiseizure treatment with a wearable device: A proof-of-concept study in absence epilepsy.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Clinical utility of an ambulatory absence seizure detection system.Epilepsia open · 2025Article
- Tailoring antiseizure treatment with a wearable device: A proof-of-concept study in absence epilepsy.Epilepsia · 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
5 authors.
Funding
Abstract
objectiveTypical absence seizures are underreported. We aimed to improve patient care using a wearable electroencephalograph (wEEG) at home and assess a machine learning (ML) pipeline for absence detection.
methodsPatients with typical absences used a wEEG device 12-24 h 1 week after antiseizure medication (ASM) adjustments. Three-hertz generalized spike-wave discharges (SWDs) ≥ 3 s were used as absence surrogates. After manual inspection, we used the results to guide medical treatment. The outcomes were seizure freedom, number of consecutive measurements without relapse, and side effects. Afterward, we used the ML pipeline on the recordings, and a neurologist reviewed the output. Review time and diagnostic performance were compared with manual inspection.
resultsNineteen patients (12 female, median age = 24 years) were followed for a median of 5 months (range = 1-12). The median recording time for each session was 21.3 h (range = 10-24). Fifteen patients (79%) were seizure-free during the last measurement, including seven of 11 (63%) diagnosed with refractory epilepsy. Ten patients relapsed after a median of 1-2 recordings (range = 1-6) without 3-Hz SWDs. Side effects occurred in 21% of patients. Manual file inspection identified 806 3-Hz SWDs of ≥3 s. The ML pipeline reduced a neurologist's median review time for 24-h wEEG from 27 (range = 10-45) to 4.3 min (range = .1-10), with a sensitivity, precision, F1-score, and false positives per hour of .8, .95, .87, and .007, respectively. SIGNIFICANCE: Home-based wEEG allows patient monitoring after ASM adjustments, improving absence seizure management. The ML-based pipeline performed well and was crucial in reducing review time.
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.