Biology subjects

Arnal, P. J.

Publications and source records attributed to Arnal, P. J..

2 recordsLinked to original sources

bioRxiv2019

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

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 DHs 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.

neuroscience↗

bioRxiv2017

PERFORMANCE OF AN AMBULATORY DRY-EEG DEVICE FOR AUDITORY CLOSED-LOOP STIMULATIONS IN THE HOME ENVIRONMENT

ObjectiveRecent research has shown that auditory closed-loop stimulations can enhance sleep slow oscillations (SO) to improve N3 sleep quality and cognition. Previous studies have been conducted in a lab environment and on a small sample size. The present study aimed at validating and assessing the performance of a novel ambulatory wireless dry-EEG device (WDD), for auditory closed-loop stimulations of SO during N3 sleep at home.\n\nMaterial and MethodsThe performance of the WDD to detect N3 sleep automatically and to send auditory closed-loop stimulations on SO were tested on 20 young healthy subjects who slept with both the WDD and a miniaturized polysomnography (part 1) in both stimulated and sham nights within a double blind, randomized and crossover design.\n\nThe electrophysiological effects of auditory closed-loop stimulation on delta power increase were assessed after 1 and 10 nights of stimulations on an observational pilot study in the home environment including 90 middle-aged subjects (part 2).\n\nResultsThe sensitivity and specificity of the WDD to automatically detect N3 sleep in real-time were 0.70 and 0.90, respectively. The stimulation accuracy of the SO ascending-phase targeting was 45{+/-}52{degrees}. The stimulation protocol induced an increase of 39.5 % of delta power after the stimulations. The increase of SO response to auditory stimulations remained at the same level after 10 consecutive nights.\n\nConclusionThe WDD shows good performances to automatically detect in real-time N3 sleep and to send auditory closed-loop stimulations on SO accurately. These stimulations increased the SO amplitude during N3 sleep without any adaptation effect after 10 consecutive nights. This tool provides new perspectives to figure out novel sleep EEG biomarkers in longitudinal studies and can be interesting to conduct broad studies on the effects of auditory stimulations during sleep.

bioengineering↗