Sleep最新文献

Study objectives: Transient arousal from sleep has been shown to elicit a prolonged increase in genioglossus muscle activity that persists following the return to sleep and which may protect against subsequent airway collapse. We hypothesized that this increased genioglossal activity following return to sleep after an arousal is due to persistent firing of inspiratory-modulated motor units (MUs) that are recruited during the arousal.

Methods: Thirty-four healthy participants were studied overnight while wearing a nasal mask with pneumotachograph to measure ventilation and with 4 intramuscular genioglossus EMG electrodes. During stable N2 and N3 sleep, auditory tones were played to induce brief (3-15s) AASM arousals. Ventilation and genioglossus MUs were quantified before the tone, during the arousal and for 10 breaths after the return to sleep.

Results: A total of 1089 auditory tones were played and gave rise to 239 MUs recorded across arousal and the return to sleep in 20 participants (aged 23 ± 4.2 years and BMI 22.5 ± 2.2 kg/m2). Ventilation was elevated above baseline during arousal and the first post-arousal breath (p < .001). Genioglossal activity was elevated for five breaths following the return to sleep, due to increased firing rate and recruitment of inspiratory modulated MUs, as well as a small increase in tonic MU firing frequency.

Conclusions: The sustained increase in genioglossal activity that occurs on return to sleep after arousal is primarily a result of persistent activity of inspiratory-modulated MUs, with a slight contribution from tonic units. Harnessing genioglossal activation following arousal may potentially be useful for preventing obstructive respiratory events.

Daytime naps are used by elite athletes in both training and match-day settings. Currently, there are limited interventional studies on the efficacy of napping on physical performance in elite team-sport athletes. Therefore, the objective was to investigate the effect of a daytime nap (<1 hour) on afternoon performance of peak power, reaction time, self-reported wellness, and aerobic performance in professional rugby union athletes. A randomized cross-over design was carried out among 15 professional rugby union athletes. Athletes performed nap (NAP) and no nap (CON) conditions on two occasions, separated by 1 week. Baseline testing of reaction time, self-reported wellness, and a 6-second peak power test on a cycle ergometer were completed in the morning, followed by 2 × 45-minute training sessions, after which athletes completed the NAP or CON condition at 1200 hours. Following the nap period, baseline measures were retested in addition to a 30-minute fixed-intensity interval cycle and a 4-minute maximal effort cycling test. A significant group × time interaction was determined for 6-second peak power output (+157.6 W, p < 0.01, d = 1.53), perceived fatigue (-0.2 AU, p = 0.01, d = 0.37), and muscle soreness (-0.1 AU, p = 0.04, d = 0.75) in favor of the NAP condition. A significantly lower perceived exertion rating (-1.2 AU, p < 0.01, d = 1.72) was recorded for the fixed-intensity session in favor of NAP. This study highlights that utilizing daytime naps between training sessions on the same day improved afternoon peak power and lowered perceptions of fatigue, soreness, and exertion during afternoon training in professional rugby union athletes.

Study objectives: To evaluate at-home use of eye-mask and earplugs (EMEP) versus sleep hygiene advice leaflet (AL) on actigraphy-derived night sleep duration in sleep-deprived pregnant women.

Methods: A randomized controlled trial was conducted in the antenatal clinic of University Malaya Medical Centre from June 2021 to June 2022. Women at 34-36 weeks gestation with self-reported night sleep duration ≤6 hours were recruited. Participants wore an actigraphy device at night for seven consecutive nights (Observation/Baseline week). Only women whose actigraphy-derived night sleep duration was confirmed to be ≤360 minutes were randomized to use EMEP or AL. Actigraphy was continued for another week (Intervention week). Primary outcome was change in actigraphy-derived night sleep duration from observation to intervention week across trial arms. Secondary outcomes include participants' sleep quality, labor, and neonatal outcome. Comparisons were by Student t-test, Mann-Whitney U test, and chi-square test.

Results: A total of 210 women were randomized: 105 each to EMEP and AL. The increase in night sleep duration over baseline was significantly longer with both EMEP (mean ± SD) 23 ± 41 minutes, p < .001, and AL 10 ± 35 minutes, p = .007. Night sleep duration was longer by 12.9 (95% CI = 2.2 to 23.7) minutes, p = .019 with EMEP over AL. Sleep quality (Global Pittsburgh Sleep Quality Index score) was significantly improved with EMEP (mean ± SD) -2.0 ± 2.5, p < .001, but not with AL -0.3 ± 2.2, p = .246. Labor and neonatal outcomes were not significantly different.

Conclusion: EMEP significantly lengthened night sleep duration in sleep-deprived women in late pregnancy and is superior to AL.

Clinical trial information: "Use of eye masks and earplugs compared with standard advice to improve sleep in pregnancy". https://doi.org/10.1186/ISRCTN19061849. Registered with ISRCTN on 07 May 2021, trial identification number: ISRCTN19061849.

Study objectives: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments.

Methods: All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset.

Results: We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance.

Conclusions: Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.

The frequent appearance of newly learned information in dreams suggests that dream content is influenced by memory consolidation. Many studies have tested this hypothesis by asking whether dreaming about a learning task is associated with improved memory, but results have been inconsistent. We conducted a meta-analysis to determine the strength of the association between learning-related dreams and post-sleep memory improvement. We searched the literature for studies that (1) trained participants on a pre-sleep learning task and then tested their memory after sleep, and (2) associated post-sleep memory improvement with the extent to which dreams incorporated learning task content. Sixteen studies qualified for inclusion, which together reported 45 effects. Integrating across effects, we report a strong and statistically significant association between task-related dreaming and memory performance (SMD = 0.51 [95% CI 0.28, 0.74], p < 0.001). Among studies using polysomnography, this relationship was statistically significant for dreams collected from non-rapid eye movement (NREM) sleep (n = 10) but not for dreams collected from rapid eye movement (REM) sleep (n = 12). There was a significant association between dreaming and memory for all types of learning tasks studied. This meta-analysis provides further evidence that dreaming about a learning task is associated with improved memory performance, suggesting that dream content may be an indication of memory consolidation. Furthermore, we report preliminary evidence that the relationship between dreaming and memory may be stronger in NREM sleep compared to REM.

Whole-night sleep electroencephalogram (EEG) is plagued by several types of large-amplitude artifacts. Common approaches to remove them are fraught with issues: channel interpolation, rejection of noisy intervals, and independent component analysis are time-consuming, rely on subjective user decisions, and result in signal loss. Artifact Subspace Reconstruction (ASR) is an increasingly popular approach to rapidly and automatically clean wake EEG data. Indeed, ASR adaptively removes large-amplitude artifacts regardless of their scalp topography or consistency throughout the recording. This makes ASR, at least in theory, a highly-promising tool to clean whole-night EEG. However, ASR crucially relies on calibration against a subset of relatively clean "baseline" data. This is problematic when the baseline changes substantially over time, as in whole-night EEG data. Here we tackled this issue and, for the first time, validated ASR for cleaning sleep EEG. We demonstrate that ASR applied out-of-the-box, with the parameters recommended for wake EEG, results in the dramatic removal of slow waves. We also provide an appropriate procedure to use ASR for automatic and rapid cleaning of whole-night sleep EEG data or any long EEG recording. Our procedure is freely available in Dusk2Dawn, an open-source plugin for EEGLAB.

Study objectives: Over 80% of people with tetraplegia have sleep-disordered breathing (SDB), but whether this is predominantly obstructive or central is unclear. This study aimed to estimate the prevalence of central sleep apnea (CSA) in tetraplegia and the contributions of central, obstructive, and hypopnea respiratory events to SDB summary indices in tetraplegia.

Methods: Research and clinical data from 606 individuals with tetraplegia and full overnight polysomnography were collated. The proportions of different respiratory event types were calculated; overall and for mild, moderate, and severe disease. The prevalence of Predominant CSA (Central Apnea Index [CAI] ≥ 5 and more central than obstructive apneas) and Any CSA (CAI ≥ 5) was estimated. Prevalence of sleep-related hypoventilation (SRH) was estimated in a clinical sub-cohort.

Results: Respiratory events were primarily hypopneas (71%), followed by obstructive (23%), central (4%), and mixed apneas (2%). As severity increased, the relative contribution of hypopneas and central apneas decreased, while that of obstructive apneas increased. The prevalence of Predominant CSA and Any CSA were 4.3% (26/606) and 8.4% (51/606) respectively. Being male, on opiates and having a high tetraplegic spinal cord injury were associated with CSA. SRH was identified in 26% (26/113) of the clinical sub-cohort.

Conclusions: This is the largest study to characterize SDB in tetraplegia. It provides strong evidence that obstructive sleep apnea is the predominant SDB type; 9-18 times more prevalent than CSA. The prevalence of CSA was estimated to be 4%-8%, significantly lower than previously reported.