PT  - JOURNAL ARTICLE
AU  - Pierrick J. Arnal
AU  - Valentin Thorey
AU  - Michael E. Ballard
AU  - Albert Bou Hernandez
AU  - Antoine Guillot
AU  - Hugo Jourde
AU  - Mason Harris
AU  - Mathias Guillard
AU  - Pascal Van Beers
AU  - Mounir Chennaoui
AU  - Fabien Sauvet
TI  - The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging
AID  - 10.1101/662734
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 662734
4099  - http://biorxiv.org/content/early/2019/06/10/662734.short
4100  - http://biorxiv.org/content/early/2019/06/10/662734.full
AB  - Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality 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 demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) 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 five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies.