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. Sleep | EEG | Machine learning | Sleep stages | DeviceCorrespondence: research@dreem.com
Introduction Several studies have shown slow wave sleep (SWS) is altered with ageing. However, most of these studies have been conducted in-lab and usually over a single night. In this study, we assessed the evolution of process S with ageing by analysing the dynamics of endogenous and auditory-evoked slow waves in a large population. Methods 300 participants (200 M, 20 - 70 y.o.) were selected from volunteers users wearing a sleep headband for at least 3 nights, meeting the criteria of high signal quality and having no subjective sleep complaints nor being shift-workers. The Dreem headband is a connected device able to monitor EEG signals as well as pulse and movement and performs sleep staging in real-time automatically. Slow waves were detected as large negative deflections on the filtered EEG signals during NREM sleep. The auditory evoked slow waves were done using a previously validated closed-loop procedure. Results In our study, age was strongly correlated with N3 sleep duration (r=-0.34, p<0.0001), slow wave amplitude (r=-0.25, p<0.0001), and slow wave density (r=-0.40, p<0.0001). The slope of the slow wave activity, representing the process S here, was significantly decreased (r=-0.32, p<0.0001). This effect was mainly due to changes in the density of slow waves in the first 2 hours of sleep (r=-0.41, p<0.0001). Finally, our results show a decrease in the probability of auditory evoked slow waves (r=-0.43, p<0.0001). Conclusion These results confirmed the in-lab studies showing a heterogeneous alteration of homoeostatic process S with age, as well as a general decrease of slow wave occurrences, that is observed in parallel of a decrease of the probability of evoking slow waves, suggesting a global change in the system responsible for slow wave generation. Support This study was supported by Dreem sas and ANR, FLAG ERA 2015, HPB SLOW-Dyn
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