Abstract Study Objectives This study (Octave-3) aimed to validate the performance of the Waveband dry-EEG sensor device for automatic sleep staging and sleep parameter estimation as compared to gold-standard in-lab polysomnography (PSG). Methods 45 participants were enrolled and 38 completed simultaneous PSG and Waveband recordings. PSG data was scored by 6 human technologists while Waveband data was scored algorithmically. Agreement between sleep staging results and derived sleep parameters (total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), and time spent in each sleep stage) was measured using the Intraclass Correlation Coefficient (ICC) and Overall Agreement (OA). Waveband OA was compared to each human rater using the leave one out consensus of the remaining 5 human experts. Results Average OA between Waveband vs the leave one out consensuses was 87.3+/-5.4%, equivalent to the average OA for individual human experts of 85.9+/-7.6% (p.1). Waveband and humans had better OA over the second half of the night, but Waveband had superior OA. ICCs for TST, SE, LPS, and WASO exceeded 0.9, indicating excellent agreement between automated Waveband and human PSG scoring. Lower agreement was found for time spent in N1, N3, and REM, with ICCs ranging from 0.65 to 0.73. Conclusions Waveband provides accurate sleep staging and estimation of TST, SE, SOL, LPS, and WASO, with comparable performance to human expert staging of PSG. Its reduced form-factor and good performance should make it a valuable tool for automated assessment of sleep in patients with disturbed sleep.
Savietto et al. (2026) studied this question.