CoChat
Sign Up
Cited by 394EEG and Brain-Computer InterfacesNon-Invasive Vital Sign MonitoringSleep and related disorders

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging

Open access
Arnal, Pierrick J, Thorey, Valentin, Debellemaniere, Eden, Ballard, Michael E, Bou Hernandez, Albert, Guillot, Antoine, Jourde, Hugo, Harris, Mason, Guillard, Mathias, Van Beers, Pascal, Chennaoui, Mounir, Sauvet, Fabien·2020·SLEEP
DOI 10.1093/sleep/zsaa097PMC PMC7751170

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/sleep/zsaa097

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. 20-s samples of raw signals recorded by DH (pink) and PSG (black) on the same record during each sleep stages (N1, N2, N3, REM, and Wake). The derivations are F7–O1 for the DH and F3–O1 for the PSG. The signals are presented between −150 and 150 μV.
Figure 1. 20-s samples of raw signals recorded by DH (pink) and PSG (black) on the same record during each sleep stages (N1, N2, N3, REM, and Wake). The derivations are F7–O1 for the DH and F3–O1 for the PSG. The signals are presented between −150 and 150 μV.CC BY-NC-ND · PubMed Central
Figure 2. Relative spectral power (α, β, λ, and θ frequencies, AU), heart rate (beats per minute, BPM), breathing frequency (cycles per minute, CPM), and respiratory rate variability (RRV, %) for a representative record (i.e. with a MPE similar to the mean of the group). These signals are presented for a whole record for both the DH (pink) and PSG (black).
Figure 2. Relative spectral power (α, β, λ, and θ frequencies, AU), heart rate (beats per minute, BPM), breathing frequency (cycles per minute, CPM), and respiratory rate variability (RRV, %) for a representative record (i.e. with a MPE similar to the mean of the group). These signals are presented for a whole record for both the DH (pink) and PSG (black).CC BY-NC-ND · PubMed Central
Figure 3. Confusion matrix for the DH versus PSG scoring consensuses (top) and the overall confusion matrix for scorers versus the other scorers’ consensuses (bottom). Values are normalized by row with the number of epochs in parentheses.
Figure 3. Confusion matrix for the DH versus PSG scoring consensuses (top) and the overall confusion matrix for scorers versus the other scorers’ consensuses (bottom). Values are normalized by row with the number of epochs in parentheses.CC BY-NC-ND · PubMed Central
Figure 4. Hypnograms for the five first participants showing both the consensuses of the four top-ranked scorers (gray) and the DH automated sleep stage classifications. Accuracies are presented as average obtained by the five scorers on the consensus hypnogram, and scores obtained for the DH versus the consensuses.
Figure 4. Hypnograms for the five first participants showing both the consensuses of the four top-ranked scorers (gray) and the DH automated sleep stage classifications. Accuracies are presented as average obtained by the five scorers on the consensus hypnogram, and scores obtained for the DH versus the consensuses.CC BY-NC-ND · PubMed Central
Figure 5. Bland Altman plots for each sleep variable measures by the DH versus the consensus sleep metrics computed for each record.
Figure 5. Bland Altman plots for each sleep variable measures by the DH versus the consensus sleep metrics computed for each record.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 394

Related

See all

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
ORCID
Venue
SLEEP
Publisher
Oxford University Press (OUP)
Published
2020-05-20
Volume
43
Issue
11
ISSN
  • 0161-8105 (ISSN-L)
  • 1550-9109
DOI
10.1093/sleep/zsaa097
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review