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

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, 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

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.

Keywords: EEG; device; machine learning; sleep; sleep stages.

© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society.

Figures

Figure 1.
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 2.
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 3.
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 4.
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 5.
Figure 5.
Bland Altman plots for each sleep variable measures by the DH versus the consensus sleep metrics computed for each record.

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