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In Sleep

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-EEG device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by 5 sleep experts.

METHODS : Twenty-five 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 AASM guidelines vs. 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 alpha was 15±3.5%, 16±4.3% for beta, 16±6.1% for delta, and 10±1.4% for theta 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.

CONCLUSION : 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.

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-May-20

Device, EEG, Machine learning, Sleep, Sleep stages