Performance of an Ambulatory Dry-EEG Device for Auditory Closed-Loop Stimulation of Sleep Slow Oscillations in the Home Environment

Debellemanière, Eden; Chambon, Stanislas; Pinaud, Clémence; Thorey, Valentin; Dehaene, David; Léger, Damien; Chennaoui, Mounir; Arnal, Pierrick J.; Galtier, Mathieu

doi:10.3389/fnhum.2018.00088

Cited by 79 publications

(72 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Acoustic stimulation significantly increased SO by ~15% and SWA by ~11% in stim ON compared to sham ON intervals, similar to the enhancement seen in cognitively healthy older adults . Additionally, there was a trend for an increase in SWA during ON intervals throughout the first four cycles of sleep, particularly in cycle 2 and 3 of sleep showing a similar trend observed in young adults . Similarly, the lack of a difference in SWA in cycle 1 may might be due to variability or to ceiling effects.…”

Section: Discussionsupporting

confidence: 67%

Acoustic enhancement of sleep slow oscillations in mild cognitive impairment

Papalambros

Weıntraub

Chen

et al. 2019

Ann Clin Transl Neurol

View full text Add to dashboard Cite

Objective Slow‐wave activity (SWA) during sleep is reduced in people with amnestic mild cognitive impairment (aMCI) and is related to sleep‐dependent memory consolidation. Acoustic stimulation of slow oscillations has proven effective in enhancing SWA and memory in younger and older adults. In this study we aimed to determine whether acoustic stimulation during sleep boosts SWA and improves memory performance in people with aMCI. Methods Nine adults with aMCI (72 ± 8.7 years) completed one night of acoustic stimulation (stim) and one night of sham stimulation (sham) in a blinded, randomized crossover study. Acoustic stimuli were delivered phase‐locked to the upstate of the endogenous sleep slow‐waves. Participants completed a declarative recall task with 44 word‐pairs before and after sleep. Results During intervals of acoustic stimulation, SWA increased by >10% over sham intervals (P < 0.01), but memory recall increased in only five of the nine patients. The increase in SWA with stimulation was associated with improved morning word recall (r = 0.78, P = 0.012). Interpretation Acoustic stimulation delivered during slow‐wave sleep over one night was effective for enhancing SWA in individuals with aMCI. Given established relationships between SWA and memory, a larger or more prolonged enhancement may be needed to consistently improve memory in aMCI.

show abstract

Section: Discussionsupporting

confidence: 67%

Acoustic enhancement of sleep slow oscillations in mild cognitive impairment

Papalambros

Weıntraub

Chen

et al. 2019

Ann Clin Transl Neurol

View full text Add to dashboard Cite

show abstract

“…Moreover, the Dreem headband is able to measure breathing, and such pattern detection methods could be adapted to detect breathing events linked to apnea. The analysis described in this study has been developed to be used in a real-time setup to allow the Dreem headband to support biofeedback and neurofeedback-based applications (19). These results, together with the price, ease of use, precision and reliability, and the collection of raw EEG and other relevant physiological data, make the Dreem headband an ideal candidate for high-quality large-scale longitudinal sleep studies in the home or laboratory environment.…”

Section: Resultsmentioning

confidence: 99%

“…The correlation was computed on the resulting smoothed signals. To maximize the time of the night with good signal quality on the DH, we developed a procedure to select a virtual channel which corresponds to the EEG frontal-occipital channel (FpZ-O1, FpZ-O2, F7-01, or F8-O2) with the best quality signal at any given epoch throughout the night; previously described in (19). We assessed spectral power on the virtual channel for the DH and compared it to a similar derivation on the PSG: F3-O1.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, a new group of devices has emerged for home sleep monitoring that uses EEG electrodes to measure brain activity. These include headbands (19)(20)(21)(22) and devices placed around the ear (23,24). Unlike traditional PSG, these more compact devices are usually cheaper, less burdensome, designed to be worn for multiple nights at home to enable longitudinal data collection, and require minimal or no expert supervision.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Thorey²,

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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

show abstract

“…Although the majority of closed-loop auditory stimulation studies are still conducted in lab environments, recently, the first systems for unsupervised closed-loop enhancement of slowwave activity have been developed (e.g., Debellemaniere et al 2018;Ferster et al 2019) with the goal to boost memory consolidation for prolonged periods and in clinical populations. Given the importance of local sleep characteristics for use-dependent neuroplasticity and considering the translational trend in the auditory closed-loop stimulation field, it is important to investigate whether slow waves, theta and sigma oscillations and their interaction can be manipulated selectively in a particular brain region.…”

Section: Discussionmentioning

confidence: 99%

Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

Krugliakova

Volk

Jaramillo

et al. 2019

Preprint

View full text Add to dashboard Cite

The activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes. Based on this finding, we tested whether closed-loop auditory stimulation targeting the up-phase of slow-waves over the right sensorimotor area might affect power in delta, theta and sigma bands and coupling between these oscillations within the circumscribed region. We demonstrate that while closed-loop auditory stimulation globally enhances power in delta, theta and sigma bands, changes in cross-frequency coupling of these oscillations were more spatially restricted. In particular, stimulation induced a significant decrease of delta-theta coupling in frontal channels, within the area of the strongest baseline coupling between these frequency bands. In contrast, a significant increase in delta-sigma coupling was observed over the right parietal area, located directly posterior to the target electrode. These findings suggest that closed-loop auditory stimulation locally modulates coupling between delta phase and sigma power in a targeted region, which could be used to manipulate sleep-dependent memory formation within the brain network of interest.

show abstract

Performance of an Ambulatory Dry-EEG Device for Auditory Closed-Loop Stimulation of Sleep Slow Oscillations in the Home Environment

Cited by 79 publications

References 40 publications

Acoustic enhancement of sleep slow oscillations in mild cognitive impairment

Acoustic enhancement of sleep slow oscillations in mild cognitive impairment

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

Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

Contact Info

Product

Resources

About