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The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging
Open accessPublished version of a preprint
Abstract
STUDY OBJECTIVES: The 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: These 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 REGISTRATION: NCT03725943.
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“In addition, AI can help with automated sleep stage classifiers and EEG pattern detectors with easy access to huge sleep datasets, such as the National Sleep Research Resource [243].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [243]
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Metadata
- Authors
- Arnal, P. J., Thorey, V., Debellemaniere, E., Ballard, M. E., Bou Hernandez, A., Guillot, A., Jourde, H., Harris, M., Guillard, M., Van Beers, P., Chennaoui, M., & Sauvet, F.
- Affiliations
- Dreem, Science Team, New York, NY
- Dreem, Algorithm Team, Paris, France
- Université Paris Descartes (FR)
- French Armed Forces Biomedical Research Institute (IRBA), Fatigue and Vigilance Unit, Bretigny-sur-Orge, France
- Corresponding author
- Arnal, Pierrick J
- Venue
- SLEEP
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-05-20
- Volume
- 43
- Issue
- 11
- ISSN
- 0161-8105 (ISSN-L)
- 1550-9109
- PMC
- PMC7751170
- OpenAlex
- W3026648951
- Cited by
- 394
- References
- 40
- Citations by year
- 2026: 63 · 2025: 74 · 2024: 77 · 2023: 72 · 2022: 57 · 2021: 36 · 2020: 12
- FWCI
- 23.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- SLEEP (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Dreem sas
- Dreem, Inc
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- automatic sleep staging, polysomnography comparison, EEG signal acquisition, sleep monitoring, heart rate
- MeSH terms
- Algorithms, Electroencephalography, Sleep, Sleep Stages, Polysomnography
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