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The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging
Authors:Pierrick J Arnal  Valentin Thorey  Eden Debellemaniere  Michael E Ballard  Albert Bou Hernandez  Antoine Guillot  Hugo Jourde  Mason Harris  Mathias Guillard  Pascal Van Beers  Mounir Chennaoui  Fabien Sauvet
Institution:1. Dreem, Science Team, New York, NY;2. Dreem, Algorithm Team, Paris, France;3. French Armed Forces Biomedical Research Institute (IRBA), Fatigue and Vigilance Unit, Bretigny-sur-Orge, France;4. EA 7330 VIFASOM, Paris Descartes University, Paris, France
Abstract:Study ObjectivesThe development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-electroencephalographic (EEG) device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by five sleep experts.MethodsA total of 25 subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. We assessed (1) similarity of measured EEG brain waves between the DH and the PSG; (2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG; and (3) the performance of the DH’s automatic sleep staging according to American Academy of Sleep Medicine guidelines versus PSG sleep experts manual scoring.ResultsThe mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of α was 15 ± 3.5%, 16 ± 4.3% for β, 16 ± 6.1% for λ, and 10 ± 1.4% for θ frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm, and 3.2 ± 0.6%, respectively. Automatic sleep staging reached an overall accuracy of 83.5 ± 6.4% (F1 score: 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the 5 sleep experts.ConclusionsThese results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for, large-scale, longitudinal sleep studies.Clinical Trial RegistrationNCT03725943.
Keywords:sleep  EEG  machine learning  sleep stages  device
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