Seizure classification using a multimodal seizure monitoring system (Nelli) in Dravet and Lennox-Gastaut syndromes: A...

This 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).

We 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.

Of 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).

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.