ArticleEpilepsia2025
Validation of a discrete electrographic seizure detection algorithm for extended-duration, reduced-channel wearable EEG.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Assessing a reduced-channel algorithm for end-to-end seizure detection on multiday EEG using inter-rater agreement with epileptologists.Scientific reports · 2026Article
- Assessing the performance of artificial intelligence in detecting electrographic status epilepticus when human experts often disagree.Frontiers in neurology · 2026Article
- Validation of a discrete electrographic seizure detection algorithm for extended-duration, reduced-channel wearable EEG.Epilepsia · 2025Article
- Artificial intelligence in wearable seizure detection devices: current technologies and future directions.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveReduced-channel wearable electroencephalography (EEG) may overcome the accessibility and patient comfort limitations of traditional ambulatory electrographic seizure monitoring during extended-duration use. Automated algorithms are necessary for review of extended-duration reduced-channel EEG, yet current clinical support software is designed only for full-montage recordings.
methodsThe performance of a novel automated seizure detection algorithm for reduced-channel EEG (Epitel) was evaluated in a clinical validation study involving 50 participants (31 with seizures) with diverse demographic and seizure representation.
resultsThe algorithm demonstrated an event-level sensitivity of 86.2% (95% confidence interval [CI] = 79.5%-93.2%) and a false detection rate of .162 per hour (95% CI = .116-.221), which is comparable to the performance of current clinical software for full-montage EEG. Performance varied by electrographic seizure type, with 91.4% sensitivity for focal evolving to generalized seizures, 86.7% for generalized seizures, and 77.3% for focal seizures. The algorithm maintained robust performance in both pediatric participants aged 6-21 years (83% sensitivity) and adults aged 22+ years (90% sensitivity), as well as in ambulatory (80%) and epilepsy monitoring unit (EMU) monitoring environments (87.5%). The false detection rate in ambulatory monitoring environments (.290 false positive [FP] detections/h), all of which involved pediatric participants, was notably higher than in the EMU (.136 FP/h), indicating an area with clear need for improvement for unrestricted at-home monitoring. The algorithm's supplemental Confidence metric, designed to engender trust in the algorithm, showed a strong correlation with detection precision. SIGNIFICANCE: These results suggest that this algorithm can provide crucial support for review of extended-duration reduced-channel wearable EEG, enabling electrographic seizure monitoring with no restrictions on a person's daily life.
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