What question did this study set out to answer?

This research aims to validate the Waveband device as an effective method for capturing sleep patterns in individuals with narcolepsy and hypersomnolence.

May 10, 2026

0687 Validation of Waveband for Capturing Sleep in Hypersomnia Disorders

Puntos clave

This research aims to validate the Waveband device as an effective method for capturing sleep patterns in individuals with narcolepsy and hypersomnolence.
Seventy-five participants with narcolepsy type 1 and suspected hypersomnolence underwent six nights of at-home Waveband sleep recordings.
Concomitant polysomnography and multiple sleep latency testing were conducted in-clinic after initial recordings.
Data from Waveband were analyzed using the updated Waveband sleep-staging algorithm for accuracy assessment.
Waveband recordings demonstrated high compliance with ≥4 hours of wear time per night across all participants.
The updated sleep-staging algorithm showed strong agreement with PSG results in narcolepsy participants (mean Cohen’s Kappa: 0.81).
Waveband reliably detected EEG features, showcasing sensitivities of 74% and specificities of 100% for sleep-onset REM periods.

Resumen

Abstract Introduction Narcolepsy type 1 (NT1) is a rare neurological disorder linked to orexin deficiency. It is characterized by excessive daytime sleepiness, cataplexy, hallucinations, sleep paralysis, disrupted nighttime sleep, and cognitive symptoms. Diagnosis and therapeutic evaluation rely on in-clinic polysomnography (PSG) and multiple sleep latency testing (MSLT), which can be resource-intensive, burdensome, and inconsistent. NCT06531876 evaluated Waveband, an FDA-cleared at-home dry-electrode electroencephalogram (EEG), in individuals with NT1 and suspected central disorders of hypersomnolence (CDH). Following data collection, a new Waveband sleep-staging algorithm (WSSA) was implemented under an FDA-authorized Predetermined Change Control Plan. Methods Seventy-five participants (50 NT1; 25 suspected CDH) undertook six nights of at-home Waveband sleep recording and one 24-hour recording, followed by one or two nights of in-clinic PSG and a single MSLT, each with concurrent Waveband recording. Wear-time compliance and data quality were assessed via existing algorithms. PSG/MSLT data were scored by three registered PSG technologists, and Waveband data were scored using the WSSA. Results All participants completed ≥4 overnight recordings with ≥4 hours of wear time (mean SD: 7.9 1.8 hours/night, 98% 5% of data deemed scorable). For the 24-hour period, 93% of participants had ≥17 hours of wear time (mean SD: 22.2 2.7 hours, 99% 1% of data deemed scorable). The updated WSSA demonstrated high agreement with PSG in NT1 participants (mean Cohen’s Kappa: 0.81) and positive agreement across sleep stages: wake (87%), NREM1 (59%), NREM2 (87%), NREM3 (82%), and REM (90%) sleep. It accurately identified sleep-onset REM periods during PSG/MSLT with 74/76% sensitivity and 100/97% specificity. Concordance (ICC) between Waveband and PSG macrofeatures was strong, including sleep efficiency (0.94), wake after sleep onset (0.95), sleep onset latency (0.89 PSG, 0.90 MSLT), and REM within 120 minutes of sleep onset (0.87). Comparable performance was seen in participants with suspected CDH. Conclusion Waveband enables accurate, scalable at-home sleep evaluation in hypersomnia with high compliance and data quality. The updated WSSA outperforms the prior algorithm and reliably detects EEG features of narcolepsy. These results support the use of Waveband in NT1 clinical trials and for real-world clinical evaluations of cases with suspected hypersomnolence disorders. Support (if any) Funded by Takeda and Beacon Biosignals.

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