Dreem, Science Team, New York, NY.
Dreem, Algorithm Team, Paris, France.
Sleep. 2020 Nov 12;43(11). doi: 10.1093/sleep/zsaa097.
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.
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.
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.
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.
NCT03725943.
开发能够长期监测睡眠期间脑活动的动态技术对于推进睡眠科学至关重要。本研究的目的是评估与由五名睡眠专家评分的金标准多导睡眠图（PSG）相比，一种简化型干脑电图（EEG）设备（Dreem 头带，DH）的信号采集和自动睡眠分期算法的性能。
共纳入 25 名受试者，他们在睡眠中心同时佩戴 PSG 和 DH 完成一夜睡眠研究。我们评估了（1）DH 和 PSG 之间测量的 EEG 脑波的相似性；（2）DH 和 PSG 之间心率、呼吸频率和呼吸率变异性（RRV）的一致性；（3）DH 自动睡眠分期根据美国睡眠医学学会指南与 PSG 睡眠专家手动评分的性能。
DH 采集的 EEG 信号与 PSG 监测α波的信号之间的平均百分比误差为 15±3.5%，β波为 16±4.3%，λ波为 16±6.1%，θ波为 10±1.4%。心率、呼吸频率和 RRV 的平均绝对误差分别为 1.2±0.5 bpm、0.3±0.2 cpm 和 3.2±0.6%。与平均 86.4±8.0%（F1 评分：86.3±7.4）相比，DH 的自动睡眠分期达到 83.5±6.4%（F1 评分：83.8±6.3）的整体准确性。
这些结果表明 DH 既能够监测与睡眠相关的生理信号，又能够准确地将其处理为睡眠阶段。该设备为大规模的纵向睡眠研究铺平了道路。
NCT03725943。
