ArticleEpilepsia2026
Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A non-randomized, single-center feasibility study.
Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Real-world outcomes of responsive neurostimulation in patients with Lennox-Gastaut syndrome: A multicenter retrospective study.Epilepsia open · 2026Observational
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
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7 authors.
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Abstract
objectiveThis study aimed to assess the performance of the Nelli seizure monitoring system in detecting and classifying seizures during sleep or while at rest in bed in patients with Lennox-Gastaut syndrome (LGS) and Dravet syndrome (DS).
methodsWe conducted a non-interventional, single-center feasibility study from August 2023 to March 2024, involving 20 patients aged ≥2 years diagnosed with DS or LGS. Participants used Nelli for home-based seizure monitoring during sleep or while at rest in bed for 4 weeks. Seizures were detected and classified by Nelli, and results were compared to epileptologist reviews and seizure diaries.
resultsOf 20 enrolled patients, 14 (70%) who experienced seizures at rest were included in the analyses. Among them, Nelli detected 368 seizures, with an accuracy of 97.8%, as confirmed by independent reviewers. Eight seizures (2.2%) detected by Nelli were false positives, identified as part of a single seizure episode. Of the 14 patients, only 35.7% reported experiencing seizures in their diaries, and only 26.1% of the seizures were documented. Seizure durations ranged from 6 to 396 s, with considerable variation. Nelli demonstrated high accuracy in seizure classification (Gwet agreement coefficient [AC1] = .81-1.00) in nine of 14 cases. However, in three of 14 patients, moderate accuracy (AC1 = .41-.60) was observed due to challenges in classifying seizures in patients with high seizure frequency or suboptimal device positioning. The average classification accuracy of Nelli for tonic-clonic seizures was .99 (150/152 seizures), tonic seizures .55 (102/186), clonic seizures 1.00 (3/3), focal motor seizures .89 (16/18), and myoclonic seizures 1.00 (1/1). SIGNIFICANCE: Nelli demonstrated high sensitivity and classification accuracy for detecting and categorizing seizures in bed in patients with DS and LGS, outperforming seizure diaries and providing a reliable tool for seizure monitoring in home settings.
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