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Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

STUDY OBJECTIVES: Recently, criteria have been drawn up for large muscle group movements during sleep (LMM), defined as movements lasting for 3-45 seconds in adults, which are often accompanied by changes in sleep stage, arousals, and increases in heart rate. The aim of this study was to characterize LMM in restless legs syndrome (RLS) in order to better evaluate their impact on the neurophysiology of the disorder and, therefore, the possible clinical implications. METHODS: Consecutive, drug-free patients diagnosed with RLS and controls, aged 18 years or more, were retrospectively enrolled. Leg movement activity-short-interval (SILMS), periodic (PLMS), and isolated (ISOLMS) leg movements during sleep-and LMM were detected and scored. RESULTS: In total, 100 patients and 67 controls were recruited. All movement measures were significantly higher in RLS. A significant positive correlation was found between LMM and ISOLMS index but not PLMS index in both groups. LMM index showed a significant negative correlation with total sleep time, sleep efficiency, and percentage of sleep stages N3 and R, as well as a significant positive correlation with the number of awakenings, and percentage of sleep stages N1 and N2 only in patients with RLS. No significant correlation was found between either LMM or PLMS index and RLS severity. CONCLUSIONS: Different types of movements, including SILMS, ISOLMS, and LMM, play somewhat distinct roles in sleep neurophysiology in RLS. Notably, LMM, a newly recognized category of movements, demonstrates associations with sleep architecture instability and fragmentation, arousals, and awakenings, suggesting potential clinical implications.

The aim of this study was to investigate the relationship between sleep stages and neural microstructure – measured using diffusion tensor imaging – of the posterior limb of the internal capsule and corticospinal tract in preterm infants. A retrospective cohort of 50 preterm infants born between 24 + 4 and 29 + 3 weeks gestational age was included in the study. Sleep stages were continuously measured for 5–7 consecutive days between 29 + 0 and 31 + 6 weeks postmenstrual age using an in-house-developed, and recently published, automated sleep staging algorithm based on routinely measured heart rate and respiratory rate. Additionally, a diffusion tensor imaging scan was conducted at term equivalent age as part of standard care. Region of interest analysis of the posterior limb of the internal capsule was performed, and tractography was used to analyze the corticospinal tract. The association between sleep and white matter microstructure of the posterior limb of the internal capsule and corticospinal tract was examined using a multiple linear regression model, adjusted for potential confounders. The results of the analyses revealed an interaction effect between sleep stage and days of invasive ventilation on the fractional anisotropy of the left and right posterior limb of the internal capsule (β = 0.04, FDR-adjusted p = 0.001 and β = 0.04, FDR-adjusted p = 0.02, respectively). Furthermore, an interaction effect between sleep stage and days of invasive ventilation was observed for the radial diffusivity of the mean of the left and right PLIC (β = −4.1e-05, FDR-adjusted p = 0.04). Previous research has shown that, in very preterm infants, invasive ventilation has a negative effect on white matter tract maturation throughout the brain. A positive association between active sleep and white matter microstructure of the posterior limb of the internal capsule, may indicate a protective role of sleep in this vulnerable population. • Active sleep positively influences white matter microstructure in preterm infants. • Interaction between sleep stages and days of invasive ventilation affects brain development. • Active sleep shows a protective role in preterm infants with longer invasive ventilation days. • Increased AS is associated with higher fractional anisotropy in the PLIC. [ABSTRACT FROM AUTHOR]

Aromatase inhibitors (AIs) are associated with sleep difficulties in breast cancer (BC) patients. Sleep is known to favor memory consolidation through the occurrence of specific oscillations, i.e., slow waves (SW) and sleep spindles, allowing a dialogue between prefrontal cortex and the hippocampus. Interestingly, neuroimaging studies in BC patients have consistently shown structural and functional modifications in these two brain regions. With the aim to evaluate sleep oscillations related to memory consolidation during AIs, we collected polysomnography data in BC patients treated (AI+, n = 17) or not (AI-, n = 17) with AIs compared to healthy controls (HC, n = 21). None of the patients had received chemotherapy and radiotherapy was finished since at least 6 months, that limit the confounding effects of other treatments than AIs. Fast and slow spindles were detected during sleep stage 2 at centro-parietal and frontal electrodes respectively. SW were detected at frontal electrodes during stage 3. Here, we show lower frontal SW densities in AI + patients compared to HC. These results concord with previous reports about frontal cortical alterations in cancer following AIs administration. Moreover, AI + patients tended to have lower spindle density at C4 electrode. Regression analyses showed that, in both patient groups, spindle density at C4 electrode explained a large variance of memory performances. Slow spindle characteristics did not differ between groups and sleep oscillations characteristics of AI- patients did not differ significantly from those of both AI + patients and HC. Overall, our results add to the compelling evidence of the systemic effects of AIs previously reported in animals, with deleterious effects on cortical activity during sleep and associated memory consolidation in the current study. There is thus a need to further investigate sleep modifications during AIs administration. Longitudinal studies are needed to confirm these findings and investigation in other cancers on this topic should be conducted. • Frontal slow wave density is lower during aromatase inhibitors treatment. • Seemly, centro-parietal spindle density tend to be lower. • Spindle density is related to memory performance. • Aromatase inhibitors for breast cancer modify sleep-related memory consolidation. • Current patients' results confirm previous reports in animal models. [ABSTRACT FROM AUTHOR]

To properly identify and treat sleep disorders, the stages of sleep must be successfully classified. Both manual and automatic sleep-scoring processes may be affected by such problems, but automatic techniques are more likely to be affected. Electroencephalogram (EEG) is a key factor in diagnosing sleep disorders instead of other biological signs. Historically, this time-consuming, arduous, and subject to subjective mistake task has been carried out manually by professionals through thorough observation. This process can be made significantly easier with automatic sleep "stage scoring" that performs scoring manually, making monitoring sleep disorders at work easier. A novel efficient "sleep stage Scoring" model is proposed using a deep learning approach to meet the requirement. The proposed model comprises four phases: Signal collection, decomposition, Dimensionality reduction and Scoring. Firstly, the raw EEG signals are collected via standard datasets. It is further used for signal decomposition, achieved by Empirical Mode Decomposition (EMD). Further, the dimensionality is reduced by Principal Component Analysis (PCA). Finally, this reduced decomposed signal is fed into the Scoring model as a "Serial Cascaded One-Dimensional Convolutional Neural Network with Bidirectional Long Short-Term Memory (SC1D-BiLSTM) layer". Using this proposed methodology, an average accuracy of 93.17% is achieved. Various performance metrics are utilized to assess the model, and the values are presented in the results section. The datasets used in this study are the Sleep-EDF Database Expanded v1.0.0 and St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from Physionet, and the results are presented in the results section. [ABSTRACT FROM AUTHOR]

Introduction: ECG Derived Respiration (EDR) are a set of methods used for extracting the breathing rate from the Electrocardiogram (ECG). Recent studies revealed a tight connection between breathing rate and more specifically the breathing patterns during sleep and several related pathologies. Yet, while breathing rate and more specifically the breathing pattern is recognised as a vital sign it is less employed than Electroencephalography (EEG) and heart rate in sleep and polysomnography studies. Methods: This study utilised open-access data from the ISRUC sleep database to test a novel spectral-based EDR technique (scEDR). In contrast to previous approaches, the novel method emphasizes spectral continuity and not only the power of the different spectral peaks. scEDR is then compared against a more widely used spectral EDR method that selects the frequency with the highest power as the respiratory frequency (Max Power EDR). Results: scEDR yielded improved performance against the more widely used Max Power EDR in terms of accuracy across all sleep stages and the whole sleep. This study further explores the breathing rate across sleep stages, providing evidence in support of a putative sleep stage "REM0" which was previously proposed based on analysis of the Heart Rate Variability (HRV) but not yet widely discussed. Most importantly, this study observes that the frequency distribution of the heart rate during REM0 is closer to REM than other NREM periods even though most of REM0 was previously classified as NREM sleep by sleep experts following either the original or revised sleep staging criteria. Discussion: Based on the results of the analysis, this study proposes scEDR as a potential low-cost and non-invasive method for extracting the breathing rate using the heart rate during sleep with further studies required to validate its accuracy in awake subjects. In this study, the autonomic balance across different sleep stages, including REM0, was examined using HRV as a metric. The results suggest that sympathetic activity decreases as sleep progresses to NREM3 until it reaches a level similar to the awake state in REM through a transition from REM0. [ABSTRACT FROM AUTHOR]

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems. [ABSTRACT FROM AUTHOR]

Summary Wearable electroencephalography devices emerge as a cost‐effective and ergonomic alternative to gold‐standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty‐centred machine learning pipeline, U‐PASS, to automate sleep staging in a challenging real‐world dataset of single‐channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five‐stage sleep‐scoring accuracy of a state‐of‐the‐art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea–hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data‐processing pipeline enhanced with uncertainty estimation. [ABSTRACT FROM AUTHOR]

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self‐screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24‐h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non‐cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart‐device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning‐based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication. [ABSTRACT FROM AUTHOR]

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels. [ABSTRACT FROM AUTHOR]

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep–wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep–wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period. [ABSTRACT FROM AUTHOR]

Introduction: Poor sleep quality has been associated with inflammatory bowel disease (IBD) activity, although studies incorporating actigraphy suggest that the perception of sleep differs rather than objective difference in sleep quality. Short sleep duration has been associated with increased pro-inflammatory cytokines that have been implicated in the pathogenesis of IBD. Methods: An observational study incorporated home-based polysomnography that was conducted within twelve weeks of an objective assessment of IBD activity such as calprotectin, colonoscopy, or MRI. Participants completed a survey on subjective measures of sleep quality, clinical IBD activity, depression, and anxiety. Polysomnography results were normalized by standardized results for a healthy population matched by gender and age. Results: Twenty participants were included in the final analysis. Those with objective evidence of active IBD had shorter stage 2 sleep duration, leading to shorter NREM sleep and total sleep time. Sleep latency was also longer in those with active IBD, leading to worse sleep efficiency—despite no difference in time available for sleep between the two groups. These changes persisted after normalization of polysomnography results by health population age and gender matched norms. Depression scores correlated with sleep latency and stage 2 sleep duration and were associated with objectively active IBD. Conclusions: Objectively confirmed active IBD was associated with shorter sleep duration. Observed sleep changes may, in part, relate to coexistent depression. Further research should consider the utility of changes in sleep duration and quality as a means of longitudinally assessing objective IBD activity. [ABSTRACT FROM AUTHOR]

In mid-later life adults, early-onset and late-onset (i.e., onset ≥50 years) depression appear to be underpinned by different pathophysiology yet have not been examined in relation to autonomic function. Sleep provides an opportunity to examine the autonomic nervous system as the physiology changes across the night. Hence, we aimed to explore if autonomic profile is altered in mid-later life adults with remitted early-onset, late-onset and no history of lifetime depression. Participants aged 50–90 years (n = 188) from a specialised clinic underwent a comprehensive clinical assessment and completed an overnight polysomnography study. General Linear Models were used to examine the heart rate variability differences among the three groups for four distinct sleep stages and the wake after sleep onset. All analyses controlled for potential confounders – age, sex, current depressive symptoms and antidepressant usage. For the wake after sleep onset, mid-later life adults with remitted early-onset depression had reduced standard deviation of Normal to Normal intervals (SDNN; p =.014, d = −0.64) and Shannon Entropy (p =.004, d = −0.46,) than those with no history of lifetime depression. Further, the late-onset group showed a reduction in high-frequency heart rate variability (HF n.u.) during non-rapid eye movement sleep stage 2 (N2; p =.005, d = −0.53) and non-rapid eye movement sleep stage 3 (N3; p =.009, d = −0.55) when compared to those with no lifetime history. Causality between heart rate variability and depression cannot be derived in this cross-sectional study. Longitudinal studies are needed to examine the effects remitted depressive episodes on autonomic function. The findings suggest differential autonomic profile for remitted early-onset and late-onset mid-later life adults during sleep stages and wake periods. The differences could potentially serve as peripheral biomarkers in conjunction with more disease-specific markers of depression to improve diagnosis and prognosis. • Autonomic dysfunction is a key risk factor and symptom of major depressive disorder. • No known studies have examined the relationship between HRV and history of depression. • History of depression is associated with the integrity of autonomic functioning in mid-later life adults. • HRV has the potential to be a biomarker of depression. [ABSTRACT FROM AUTHOR]

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep. [ABSTRACT FROM AUTHOR]

Summary: We investigated arousal scoring agreement within full‐night polysomnography in a multi‐centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second‐by‐second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04–0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second‐by‐second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07–0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes. [ABSTRACT FROM AUTHOR]

Summary: The present study aimed to: (1) investigate sleep architecture in response to an overload training and taper periods among endurance runners; and (2) assess the sleep benefits of a high‐heat‐capacity mattress topper. Twenty‐one trained male endurance runners performed a 2‐week usual training regimen (baseline) followed by 2‐week overload and taper periods. From overload to the end of the taper period, they were assigned into two groups based on the mattress topper used: high‐heat‐capacity mattress topper (n = 11) or low‐heat‐capacity mattress topper (n = 10). Training load was assessed daily using the session rating of perceived exertion. Following each period, sleep was monitored by polysomnography, and nocturnal core body temperature was recorded throughout the night. Irrespective of the group, awakening episodes > 5 min decreased following overload compared with baseline (−0.48, p = 0.05). Independently of mattress topper, each 100 A.U. increase in 7‐day training load prior to polysomnographic recording was associated with higher slow‐wave sleep proportion (β = +0.13%; p = 0.05), lower sleep‐onset latency (β = −0.49 min; p = 0.05), and a reduction in the probability of transition from N1 sleep stage to wakefulness (β = −0.12%; p = 0.05). Sleeping on a high‐heat‐capacity mattress topper did not affect any sleep variable compared with a low‐heat‐capacity mattress topper. Increased training loads promote slow‐wave sleep and sleep propensity, highlighting the adaptative nature of sleep to diurnal activity and the role of sleep in physiological recovery. Further studies are required on the potential benefits of high‐heat‐capacity mattress toppers on sleep architecture among athletes. [ABSTRACT FROM AUTHOR]

Summary: Acute exercise has been shown to affect long‐term memory and sleep. However, it is unclear whether exercise‐induced changes in sleep architecture are associated with enhanced memory. Recently, it has been shown that exercise followed by a nap improved declarative memory. Whether these effects transfer to night sleep and other memory domains has not yet been studied. Here, we investigate the influence of exercise on nocturnal sleep architecture and associations with sleep‐dependent procedural and declarative memory consolidation. Nineteen subjects (23.68 ± 3.97 years) were tested in a balanced cross‐over design. In two evening sessions, participants either exercised (high‐intensity interval training) or rested immediately after encoding two memory tasks: (1) a finger tapping task and (2) a paired‐associate learning task. Subsequent nocturnal sleep was recorded by polysomnography. Retrieval was conducted the following morning. High‐intensity interval training lead to an increased declarative memory retention (p = 0.047, d = 0.40) along with a decrease in REM sleep (p = 0.012, d = 0.75). Neither procedural memory nor NREM sleep were significantly affected. Exercise‐induced changes in N2 showed a positive correlation with procedural memory retention which did not withstand multiple comparison correction. Exploratory analyses on sleep spindles and slow wave activity did not reveal significant effects. The present findings suggest an exercise‐induced enhancement of declarative memory which aligns with changes in nocturnal sleep architecture. This gives additional support for the idea of a potential link between exercise‐induced sleep modifications and memory formation which requires further investigation in larger scaled studies. [ABSTRACT FROM AUTHOR]

Summary: Although understanding the physiological mechanisms of obstructive sleep apnea (OSA) is important for treating OSA, limited studies have examined OSA patients' sleep architecture at the epoch‐by‐epoch level and analysed the impact of sleep position and stage on OSA pathogenesis. The epoch‐labelled polysomnogram was analysed multidimensionally to investigate the effect of sleep position on the sleep architecture and risk factors of apnea in patients with OSA. This retrospective multicentric case–control study reviewed full‐night diagnostic polysomnography of 6983 participants. The difference in the proportion of time spent supine during non‐rapid eye movement (NREM) and REM stages, and the mean duration of respiratory events per body position were evaluated. The frequency of sleep stage transition per body position shift type was computed. Further subgroup analysis was performed based on OSA severity and positional dependency. Supine time in patients with OSA varied across sleep stages, with lower proportions in N3 and REM, and shorter durations with severity. Patients with OSA spent less time in supine positions during N3 and REM, and experienced longer apnea events in both positions compared to the control group. The frequency of all sleep stage transitions increased with OSA severity and was higher among non‐positional OSA than positional OSA and the control group, regardless of body position shift type. The sleep stage transition from N3 and REM to wakefulness was notably heightened during position shift. Understanding the sleep architecture of patients with OSA requires analysing various sleep characteristics including sleep position simultaneously, with future studies focusing on position detection to predict sleep stages and respiratory events. [ABSTRACT FROM AUTHOR]

Study objectives: The International Classification of Sleep Disorders categorized catathrenia as a respiratory disorder, but there are doubts whether episodes appear during rapid eye movement (REM) sleep or the non-rapid eye movement (NREM), their duration, and symptoms. The main objectives were to identify the most common features and relations of catathrenia. Methods: PubMed, Embase, and Web of Science were searched according to the PRISMA 2020 guidelines. The Joanna Briggs Institute and the ROBINS-I tools were chosen to assess the risk of bias. Results: A total of 288 records were identified, 31 articles were included. The majority of the studies had a moderate risk of bias. 49.57% of episodes occurred during the NREM sleep, while 46% took place during REM. In 60.34% females, catathrenia was more common in the NREM, while in 59.26% of males was in REM sleep (p < 0.05). Females and obese individuals were found to have shorter episodes (p < 0.05). Age was inversely correlated with minimal episodes duration (r = − 0.34). The continuous positive airway pressure (CPAP) therapy was inversely correlated with the maximal episode duration (r = − 0.48). Conclusions: Catathrenia occurs with similar frequency in both genders. The most frequent symptoms embraced groaning, awareness of disturbing bedpartners, and daytime somnolence—not confirmed by the Epworth Sleepiness Scale. The episodes occur more frequently in NREM than in REM sleep. Catathrenia may be considered as a sex-specific condition. The effects of CPAP treatment leading to shortening episodes duration, which may indicate the respiratory origin of catathrenia. [ABSTRACT FROM AUTHOR]

Summary Previous research has linked rapid eye movement sleep to emotional processing, particularly stress. Lab studies indicate that rapid eye movement sleep deprivation and fragmentation heighten emotional reactivity and stress response. This relationship extends to natural settings, where poor‐quality sleep among college students correlates with increased academic stress and lower academic performance. However, there is a lack of research into how specific sleep stages, like rapid eye movement, affect real‐life stress development. This study investigated whether habitual rapid eye movement sleep in college students can predict the future development of real‐life stress symptoms associated with final exams. Fifty‐two participants (mean age = 19 years, 62% females) monitored their sleep for a week during the academic semester using a mobile electroencephalogram device, and then completed self‐evaluations measuring test anxiety and other relevant factors. They completed the same evaluations again just prior to final exams. We found that rapid eye movement sleep was the most dominant factor predicting changes in participants' test anxiety. However, contrasting with our predictions, habitual rapid eye movement sleep was associated with an increase rather than decrease in anxiety. We discuss these results in terms of the rapid eye movement recalibration hypothesis, which suggests rapid eye movement sleep modulates activity in stress‐encoding areas in the brain, leading to both decreased sensitivity and increased selectivity of stress responses. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep–wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large‐scale and long‐term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep–wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20–30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep–wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high‐throughput genetic or pharmacological methods for sleep research. [ABSTRACT FROM AUTHOR]

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [ABSTRACT FROM AUTHOR]

Summary CDKL5 deficiency disorder is a rare genetic disease caused by mutations in the CDKL5 gene. Central apneas during wakefulness have been reported in patients with CDKL5 deficiency disorder. Studies on CDKL5‐knockout mice, a CDKL5 deficiency disorder model, reported sleep apneas, but it is still unclear whether these events are central (central sleep apnea) or obstructive (obstructive sleep apnea) and may be related to alterations of brain circuits that modulate breathing rhythm. This study aimed to discriminate central sleep apnea and obstructive sleep apnea in CDKL5‐knockout mice, and explore changes in the somatostatin neurons expressing high levels of neurokinin‐1 receptors within the preBötzinger complex. Ten adult male wild‐type and 12 CDKL5‐knockout mice underwent electrode implantation for sleep stage discrimination and diaphragmatic activity recording, and were studied using whole‐body plethysmography for 7 hr during the light (resting) period. Sleep apneas were categorised as central sleep apnea or obstructive sleep apnea based on the recorded signals. The number of somatostatin neurons in the preBötzinger complex and their neurokinin‐1 receptors expression were assessed through immunohistochemistry in a sub‐group of animals. CDKL5‐knockout mice exhibited a higher apnea occurrence rate and a greater prevalence of obstructive sleep apnea during rapid eye movement sleep, compared with wild‐type, whereas no significant difference was observed for central sleep apnea. Moreover, CDKL5‐knockout mice showed a reduced number of somatostatin neurons in the preBötzinger complex, and these neurons expressed a lower level of neurokinin‐1 receptors compared with wild‐type controls. These findings underscore the pivotal role of CDKL5 in regulating normal breathing, suggesting its potential involvement in shaping preBötzinger complex neural circuitry and controlling respiratory muscles during sleep. [ABSTRACT FROM AUTHOR]

Theoretical models conventionally portray the consolidation of memories as a slow process that unfolds during sleep. According to the classical Complementary Learning Systems theory, the hippocampus (HPC) rapidly changes its connectivity during wakefulness to encode ongoing events and create memory ensembles that are later transferred to the prefrontal cortex (PFC) during sleep. However, recent experimental studies challenge this notion by showing that new information consistent with prior knowledge can be rapidly consolidated in PFC during wakefulness and that PFC lesions disrupt the encoding of congruent events in the HPC. The contributions of the PFC to memory encoding have therefore largely been overlooked. Moreover, most theoretical frameworks assume random and uncorrelated patterns representing memories, disregarding the correlations between our experiences. To address these shortcomings, we developed a HPC--PFC network model that simulates interactions between the HPC and PFC during the encoding of a memory (awake stage), and subsequent consolidation (sleeping stage) to examine the contributions of each region to the consolidation of novel and congruent memories. Our results show that the PFC network uses stored memory "schemas" consolidated during previous experiences to identify inputs that evoke congruent patterns of activity, quickly integrate it into its network, and gate which components are encoded in the HPC. More specifically, the PFC uses GABAergic long-range projections to inhibit HPC neurons representing input components correlated with a previously stored memory "schema," eliciting sparse hippocampal activity during exposure to congruent events, as it has been experimentally observed. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Sleep‐related bruxism (SB) is the habit of grinding or clenching the teeth during sleep, mediated by the non‐peripheral central nervous system.The objectives of this cross‐sectional study were to evaluate associations between SB, microarousals and oxyhaemoglobin desaturations and to compare the frequency of SB and microarousals in sleep stages, in an apnoeic population.Two hundred and forty individuals composed the sample, who underwent a single full‐night polysomnography. Self‐reports and clinical inspections were not considered for assessing SB. The polysomnographic assessment of SB was performed using electrodes placed on masseter muscles and chin. SB was defined as more than two events of rhythmic masticatory muscle activity per hour of sleep. Microarousals were considered when there were abrupt changes in electroencephalogram frequencies, without complete awakening, lasting from 3 to 15 s. Oxyhaemoglobin desaturations were defined as significant drops (≥3%) in basal oxygen saturations. With these data, SB, microarousals and oxyhaemoglobin desaturations were evaluated and submitted to statistical analysis.Statistically significant differences were observed between bruxers and non‐bruxers when comparing the rates of microarousals (p < .001) and oxyhaemoglobin desaturations (p = .038). There was a higher number of SB and microarousals in NREM (non‐rapid eye movement) two sleep stage (p < 0.001). Bruxers had a greater risk of higher numbers of microarousals (OR = 1.023; p = .003), which did not occur for oxyhaemoglobin desaturations (OR = 0.998; p = .741).A higher number of microarousals presents relationship with SB; associations between SB and oxyhaemoglobin desaturations remained inconclusive; higher frequency of SB and microarousals was observed in NREM 2 sleep stage. [ABSTRACT FROM AUTHOR]

Introduction: Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods: This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion: The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the randomforest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models. [ABSTRACT FROM AUTHOR]

The sleeping stage classification plays an important role in the medical science because it helps the diagnosis of the mental health diseases. The conventional approach for performing the sleeping stage classification is based on the electroencephalograms (EEGs). However, it is worth noting that the EEGs reflect the brain activities. Nevertheless, the brain activities are very complicated even though the subjects are sleeping. Hence, performing the sleeping stage classification via the EEGs may yield the low classification accuracy. On the other hand, the electrooculograms (EOGs) are the voltages between the front eyes and the back eyes which are related to the eye ball movement. As it can directly reflect the various sleeping stages, it can achieve a higher classification accuracy. Therefore, this paper employs the two channel EOGs for performing the sleeping stage classification. The major contribution of this paper is to 1) employ the singular spectrum analysis (SSA) to exploit the latent intrinsic high dimensional dynamics of the one dimensional EOGs for performing the sleeping stage classification, 2) employ the approximate entropy as the features for performing the sleeping stage classification, and 3) assign the same features of different SSA components of different channels of the epochs of the EOGs into the same group and perform the principal component analysis (PCA) on each group of the feature vectors so that the properties of each type of the features are preserved. The results show that our proposed method yields the five sleeping stage classification accuracy at 93.73% and the sensitivity of the stage one non-rapid eye movement (S1) at 78.44%, which achieves the significant improvements compared to the existing methods. Therefore, our proposed method could be used to reduce the workload of the medical officers. [ABSTRACT FROM AUTHOR]

The original conceptualization of REM sleep as paradoxical sleep was based on its EEG resembling wakefulness and its association with dreaming. Over time, the concept of paradox was expanded to include various associations with REM sleep, such as dream exclusivity, high recall, and pathophysiology. However, none of these associations are unique to REM sleep; they can also occur in other sleep states. Today, after more than fifty years of focused research, two aspects of REMS clearly retain paradoxical exclusivity. Despite the persistent contention that the EEG of human REMS consists of wake-like, low-voltage, non-synchronous electrical discharges, REMS is based on and defined by the intracranial electrical presence of 5–8 Hz. theta, which has always been the marker of REMS in other animals. The wake-like EEG used to define REMS on human polysomnography is secondary to a generalized absence of electrophysiological waveforms because the strong waves of intracranial theta do not propagate to scalp electrodes placed outside the skull. It is a persistent paradox that the theta frequency is restricted to a cyclical intracranial dynamic that does not extend beyond the lining of the brain. REMS has a persistent association with narratively long and salient dream reports. However, the extension of this finding to equate REMS with dreaming led to a foundational error in neuroscientific logic. Major theories and clinical approaches were built upon this belief despite clear evidence that dreaming is reported throughout sleep in definingly different physiologic and phenomenological forms. Few studies have addressed the differences between the dreams reported from the different stages of sleep so that today, the most paradoxical aspect of REMS dreaming may be how little the state has actually been studied. An assessment of the differences in dreaming between sleep stages could provide valuable insights into how dreaming relates to the underlying brain activity and physiological processes occurring during each stage. The brain waves and dreams of REMS persist as being paradoxically unique and different from waking and the other states of sleep consciousness. [ABSTRACT FROM AUTHOR]

Summary Breathing and sleep state are tightly linked. The traditional approach to evaluation of breathing in rapid eye movement sleep has been to focus on apneas and hypopneas, and associated hypoxia or hypercapnia. However, rapid eye movement sleep breathing offers novel insights into sleep physiology and pathology, secondary to complex interactions of rapid eye movement state and cardiorespiratory biology. In this review, morphological analysis of clinical polysomnogram data to assess respiratory patterns and associations across a range of health and disease is presented. There are several relatively unique insights that may be evident by assessment of breathing during rapid eye movement sleep. These include the original discovery of rapid eye movement sleep and scoring of neonatal sleep, control of breathing in rapid eye movement sleep, rapid eye movement sleep homeostasis, sleep apnea endotyping and pharmacotherapy, rapid eye movement sleep stability, non‐electroencephalogram sleep staging, influences on cataplexy, mimics of rapid eye movement behaviour disorder, a reflection of autonomic health, and insights into cardiac arrhythmogenesis. In summary, there is rich clinically actionable information beyond sleep apnea encoded in the respiratory patterns of rapid eye movement sleep. [ABSTRACT FROM AUTHOR]

Background: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings. Methods: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores. Results: The performance of the model was poor (AUC = 0.55–0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation—dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80. Conclusions: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards. [ABSTRACT FROM AUTHOR]

Positional OSA (POSA) is a recognized subtype of OSA that exhibits distinct endotypic characteristics when compared with nonpositional OSA (NPOSA). The basis for the disparity in endotypes between these subtypes remains poorly understood. (1) Do individuals with NPOSA and POSA have different underlying OSA endotypes? (2) Which endotypic characteristics are critical in determining NPOSA and POSA severity? Within the Shanghai Sleep Health Study cohort, individuals with OSA were recruited and classified as having POSA or NPOSA. Endotypes were calculated using polysomnography. Endotype analysis was conducted in 1,036 individuals with OSA. Compared with individuals with NPOSA, those with POSA had lower loop gain calculated during all sleep stages and all sleep positions (0.55; interquartile range [IQR], 0.46-0.66 vs 0.68, IQR, 0.52-0.90; P <.001), lower arousal threshold calculated during all sleep stages and all sleep positions (ArTH All) (138.67; IQR, 118.94-180.87 percentage of the eupneic ventilation [%Veupnea] vs 189.00; IQR, 129.71-257.76 %Veupnea; P <.001), lower pharyngeal collapsibility calculated during all sleep stages and all sleep positions (Vpassive All) (91.85; IQR, 83.13-95.15 %Veupnea vs 76.38; IQR, 23.77-92.08 %Veupnea; P <.001), and higher muscle compensation calculated during all sleep stages and all sleep positions (6.50; IQR, −6.77 to 16.39 %Veupnea vs 3.65; IQR, −10.47 to 12.14 %Veupnea; P =.003). Logistic regression analyses indicated that higher Vpassive All was associated with increased odds of POSA vs NPOSA. In NPOSA, fully adjusted linear regression analyses indicated that Vpassive All (β = −0.55; 95% CI, −0.68 to −0.42; P <.001) and lower loop gain calculated during all sleep stages and all sleep positions (β = 0.19; 95% CI, 0.08-0.30; P <.001) were significant independent predictors of the apnea hypopnea index, with Vpassive All being the most critical factor. In contrast, in POSA, collapsibility appeared to be less influential (β = −0.09; 95% CI, −0.21 to 0.03; P =.138). Nonanatomic endotypic characteristics (LG All : β = 0.29; 95% CI, 0.18-0.41; P <.001; arousal threshold in all sleep stages and all sleep positions: β = 0.15; 95% CI, 0.01-0.28; P =.031; muscle compensation in all sleep stages and all sleep positions: β = −0.21; 95% CI, −0.29 to −0.12; P <.001) were significant in determining the severity of POSA, with loop gain being the most crucial factor. This study highlights the differences in endotypes between NPOSA and POSA. In Chinese individuals, anatomic factors were more significant in determining the severity of NPOSA, whereas nonanatomic traits were more likely to determine the severity of POSA. Future research should focus on developing personalized management strategies for individuals with NPOSA and POSA based on their endotypes. Chinese Clinical Trial Registry; No.: ChiCTR1900025714; URL: https://www.chictr.org.cn/indexEN.html. [ABSTRACT FROM AUTHOR]

Objective: Recent evidence shows that during slow‐wave sleep (SWS), the brain is cleared from potentially toxic metabolites, such as the amyloid‐beta protein. Poor sleep or elevated cortisol levels can worsen amyloid‐beta clearance, potentially leading to the formation of amyloid plaques, a neuropathological hallmark of Alzheimer disease. Here, we explored how nocturnal neural and endocrine activity affects amyloid‐beta fluctuations in the peripheral blood. Methods: We acquired simultaneous polysomnography and all‐night blood sampling in 60 healthy volunteers aged 20–68 years. Nocturnal plasma concentrations of amyloid‐beta‐40, amyloid‐beta‐42, cortisol, and growth hormone were assessed every 20 minutes. Amyloid‐beta fluctuations were modeled with sleep stages, (non)oscillatory power, and hormones as predictors while controlling for age and participant‐specific random effects. Results: Amyloid‐beta‐40 and amyloid‐beta‐42 levels correlated positively with growth hormone concentrations, SWS proportion, and slow‐wave (0.3–4Hz) oscillatory and high‐band (30–48Hz) nonoscillatory power, but negatively with cortisol concentrations and rapid eye movement sleep (REM) proportion measured 40–100 minutes previously (all t values > |3|, p values < 0.003). Older participants showed higher amyloid‐beta‐40 levels. Interpretation: Slow‐wave oscillations are associated with higher plasma amyloid‐beta levels, whereas REM sleep is related to decreased amyloid‐beta plasma levels, possibly representing changes in central amyloid‐beta production or clearance. Strong associations between cortisol, growth hormone, and amyloid‐beta presumably reflect the sleep‐regulating role of the corresponding releasing hormones. A positive association between age and amyloid‐beta‐40 may indicate that peripheral clearance becomes less efficient with age. ANN NEUROL 2024;96:46–60 [ABSTRACT FROM AUTHOR]

The aim was to investigate brain responses to non-painful tactile stimuli applied to the non-dominant hand during sleep. 21 healthy subjects participated in the study (11 female, mean age ± SD: 20.67 ± 1.91 years). A 40-channel polysomnography system and a pneumatic tactile stimulator unit were used. Stimuli were applied to the participants' non-dominant hand. Evoked potential components of the CZ electrode were examined in four sleep stages (N1, N2, N3, and REM). Repeated measures ANOVA was used in statistical analyses. Brain responses, categorized as early (P50, N100, and P200), mid-early (N300, P450, and N550), and late (P900 and Nlate), were detected all sleep stages. No notable variances were found in the amplitude and latency of early brain responses when analyzed across different sleep stages. Differences in both amplitude and latency were observed across different sleep stages for the N300, P450, P900, and Nlate response components. This study presents a pioneering exploration into the responses of the non-dominant hand throughout all sleep stages, encompassing eight distinct response components. This novel investigation contributes to the existing literature by shedding light on previously unexplored aspects. The observed early responses are identified as sensory, while middle to late responses align with cognitive processes within the realm of sleep research. Notably, N300, P450, P900, and Nlate components display variations across diverse sleep stages, marked by alterations in both amplitude and latency. These findings offer valuable insights into the dynamic nature of hand responses throughout the sleep continuum. [ABSTRACT FROM AUTHOR]

Adults with restless sleep disorder (RSD) have never been studied clinically and polysomnographically. This study aimed to describe the clinical manifestation, duration, and distribution of sleep-related movements in adult patients with restless sleep disorder. Patients who had performed VPSG from Jan 2021 to Jan 2022 and met the diagnosis criteria of RSD were enrolled in the study. Patients' bed partners were also interviewed or telephoned in identifying this disorder. Scoring of movements during sleep was according to the diagnosis criteria of RSD and scoring of large muscle group movements during sleep proposed by the International RLS Study Group in 2020 and 2021, respectively. The clinical manifestation, the distribution of sleep stage as well as the types and duration of the movements were carefully recorded and analyzed. We included ten patients in the study with a mean age of 27.6 years (range 22–38). There was a male prevalence in adults with RSD. The study highlighted the findings from video-polysomnography, which indicated frequent sleep-related movements occurring throughout the Night. These movements were most prominent during N1 and N2 sleep stage, followed by REM sleep, while fewer movements were observed during N3 sleep. Adults with RSD experienced significant daytime functioning impairments, including non-refreshing sleep, daytime fatigue/sleepiness, and mood disturbance. Two of the patients in the study were diagnosed with anxiety and depression, further underscoring the impact of RSD on mental health. Adult patients also suffer from severe RSD, and the RSD that originates in childhood tends to persist into adulthood. In these cases, longer duration of the disease and poor sleep quality may be associated with an increased risk of developing psychiatric comorbidities. Our cases represent an objectively documented type of RSD in younger adult patients. [ABSTRACT FROM AUTHOR]

Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971). If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified. [ABSTRACT FROM AUTHOR]

Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables. • Proposal and evaluation of a smartwatch-based solution for sleep staging. • Experimental evaluation on a large and diverse dataset of 1522 sleep recordings. • Solution based on neural networks trained on accelerometer and PPG sensor data. • 71.6 % of balanced accuracy for classification of 4 sleep stages compared with PSG. • Low correlation of performance with demographic variables and sleep apnea disorder. [ABSTRACT FROM AUTHOR]

Rett syndrome (RTT) is a rare neurological disorder primarily associated with mutations in the methyl-CpG-binding protein 2 (MECP2) gene. The syndrome is characterized by cognitive, social, and physical impairments, as well as sleep disorders and epilepsy. Notably, dysfunction of the autonomic nervous system is a key feature of the syndrome. Although Heart Rate Variability (HRV) has been used to investigate autonomic nervous system dysfunction in RTT during wakefulness, there is still a significant lack of information regarding the same during sleep. Therefore, our aim was to investigate cardiovascular autonomic modulation during sleep in subjects with RTT compared to an age-matched healthy control group (HC). A complete overnight polysomnographic (PSG) recording was obtained from 11 patients with Rett syndrome (all females, 10 ± 4 years old) and 11 HC (all females, 11 ± 4 years old; p = 0.48). Electrocardiogram and breathing data were extracted from PSG and divided into wake, non-REM, and REM sleep stages. Cardiac autonomic control was assessed using symbolic non-linear heart rate variability analysis. The symbolic analysis identified three patterns: 0 V% (sympathetic), 2UV%, and 2LV% (vagal). The 0 V% was higher in the RTT group than in the HC group during wake, non-REM, and REM stages (p < 0.01), while the 2LV and 2UV% were lower during wake and sleep stages (p < 0.01). However, the 0 V% increased similarly from the wake to the REM stage in both RTT and HC groups. Therefore, the sympatho-vagal balance shifted towards sympathetic predominance and vagal withdrawal during wake and sleep in RTT, although cardiac autonomic dynamics were preserved during sleep. [ABSTRACT FROM AUTHOR]

To investigate the relationship between both self-reported and objective sleep variables and low-grade inflammation in children and adolescents with major depressive disorder (MDD) of moderate to severe symptom severity. In this cross-sectional study, we examined twenty-nine children and adolescents diagnosed with MDD and twenty-nine healthy controls (HC). Following a one-week actigraphy assessment, comprehensive sleep evaluations were conducted, including a one-night sleep EEG measurement and self-reported sleep data. Plasma high-sensitivity C-reactive protein (hsCRP) was employed as a marker to assess low-grade inflammation. No significant difference in hsCRP levels was observed between participants with MDD and HC. Furthermore, after adjusting for sleep difficulties, hsCRP exhibited no correlation with the severity of depressive symptoms. In HC, levels of hsCRP were not linked to self-reported and objective sleep variables. In contrast, depressed participants showed a significant correlation between hsCRP levels and increased subjective insomnia severity (Insomnia Severity Index; r = 0.41, p < 0.05), increased time spent in the N2 sleep stage (r = 0.47, p < 0.01), and decreased time spent in slow-wave sleep (r = − 0.61, p < 0.001). Upon additional adjustments for body mass index, tobacco use and depression severity, only the inverse association between hsCRP and time spent in slow-wave sleep retained statistical significance. Moderation analysis indicated that group status (MDD vs. HC) significantly moderates the association between slow-wave sleep and hsCRP. Our findings suggest that alterations in the architecture of slow-wave sleep may have a significant influence on modulating low-grade inflammatory processes in children and adolescents with MDD. • Evidence for a relationship between sleep and inflammation in adolescents with MDD. • Inverse association between C-reactive protein and slow-wave sleep in MDD patients. • In healthy controls, low-grade inflammation is not linked to self-reported and objective sleep. [ABSTRACT FROM AUTHOR]

BACKGROUND: The transition from alertness to drowsiness can cause considerable changes in the respiratory system, providing an opportunity to detect driver drowsiness. OBJECTIVE: The aim of this study was to determine which respiratory features indicate driver drowsiness and then use these features to classify the level of drowsiness and alertness. METHODS: Twenty male students (mean age 25.6±2.41 years) participated in the study using a driving simulator, and eight features, including expiration duration (ED), inspiration duration (ID), peak-to-peak amplitude (PA), inspiration-to-expiration time ratio (I/E ratio), driving, timing, respiration rate (RR), and yawning, were extracted from the respiratory signal generated by abdominal motions using a belt equipped with a force sensor. RESULTS: All eight features were statistically significant at the significance level of 0.05. Drowsiness can be detected using respiratory features with 88% accuracy, 82% precision, 86% recall, and an 90% F1 score. CONCLUSION: The findings of this study may be useful in the development of driver drowsiness monitoring systems based on less intrusive respiratory signal analysis, particularly for specific process automation applications when vehicle control is not in the hands of the driver. [ABSTRACT FROM AUTHOR]

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Olivia Sjöland,1,2 Thomas Svensson,1– 3 Kaushalya Madhawa,1 Hoang NT,1 Ung-Il Chung,1,3,4 Akiko Kishi Svensson1,2,5 1Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan; 2Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; 3Graduate School of Health Innovation, Kanagawa University of Human Services, Kawasaki-Ku, Kawasaki-Shi, Kanagawa, Japan; 4Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, Tokyo, Japan; 5Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo, JapanCorrespondence: Thomas Svensson, Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan, Tel +81-3-5841-4737, Email thomas.svensson@med.lu.seBackground: Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa).Methods: This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary’s Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models.Results: The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (± 7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS − 0.18; 95% CI − 0.61, 0.24) and HR NREMS (HR NREMS − 0.23; 95% CI − 0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS − 0.21 95% CI − 0.27, − 0.15) and HR NREMS (HR NREMS − 0.21 95% CI − 0.27, − 0.14) after final adjustments for covariates.Conclusion: The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.Keywords: sleep quality, heart rate, sleep stages, REMS, NREMS, wearable device

Qian Yang,1 Lingfeng Liu,1 Jing Wang,2 Ying Zhang,2 Nan Jiang,3 Meiyun Zhang2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China; 3School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People’s Republic of ChinaCorrespondence: Meiyun Zhang, Email zmy22202@aliyun.comObjective: To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder.Methods: Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared.Results: The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001).Conclusion: Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.Keywords: insomnia disorder, sleep stages, wavelet entropy, polysomnography

Rapid eye movement (REM) sleep, also referred to as paradoxical sleep for the striking resemblance of its electroencephalogram (EEG) to the one observed in wakefulness, is characterized by the occurrence of transient events such as limb twitches or facial and rapid eye movements. Here, we investigated the local activity of the primary somatosensory or barrel cortex (S1) in naturally sleeping head-fixed male mice during REM. Through local field potential recordings, we uncovered local appearances of spindle waves in the barrel cortex during REM concomitant with strong delta power, challenging the view of a wakefulness-like activity in REM. We further performed extra-and intracellular recordings of thalamic cells in head-fixed mice. Our data show highfrequency thalamic bursts of spikes and subthreshold spindle oscillations in approximately half of the neurons of the ventral posterior medial nucleus which further confirmed the thalamic origin of local cortical spindles in S1 in REM. Cortical spindle oscillations were suppressed, while thalamus spike firing increased, associated with rapid mouse whisker movements and S1 cortical activity transitioned to an activated state. During REM, the sensory thalamus and barrel cortex therefore alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage. [ABSTRACT FROM AUTHOR]

Background: Tinnitus affects approximately 740 million adults globally, involving hearing, emotion, and sleep systems. However, studies using polysomnography and pure-tone audiometry (PTA) are limited. We aimed to assess the correlation between tinnitus and hearing, sleep quality, characteristics, and depression using polysomnography and PTA. Methods: In this cross-sectional study, we divided participants into tinnitus and non-tinnitus groups. We included 100 outpatients (65 with tinnitus, 35 without) from a medical center in Taiwan, who underwent polysomnography and completed rating scales including the Patient Health Questionnaire-9 (PHQ-9), Chinese version of the Pittsburgh Sleep Quality Index (PSQI), and Chinese-Mandarin version of the Tinnitus Handicap Inventory (THI-CM). We analyzed correlations, conducted group comparisons, assessed factors related to THI-CM scores, constructed ROC curves to predict depression in the tinnitus group, and performed multinomial and logistic regression to explore associations. Results: Descriptive statistics identified a cohort with mean age 53.9 ± 12.80 years, 63% exhibited PHQ-9 scores ≥ 10, and 66% had Apnea–Hypopnea Index (AHI) > 5. The ratio of rapid eye movement and deep sleep to stage 1 + 2 sleep was relatively low and non-significant. Likewise, leg movements was higher in the tinnitus group but not statistically significant. In the tinnitus group, 63.08% had depression, and 81.54% had AHI > 5. Univariate logistic regression linked tinnitus to AHI > 5 (Odds ratio (OR) 2.67, p = 0.026) and male sex (OR 2.49, p = 0.034). A moderate positive correlation was found between the THI-CM score and PHQ-9 score (rs = 0.50, p < 0.001). Further adjustment for obstructive sleep apnea showed associations between PHQ-9 (total score) or depression and THI-CM Grade 3–5 (OR = 1.28; OR = 8.68). Single- and multifactor regression analyses highlighted significant associations of PSQI scores > 13 (OR 7.06, p = 0.018) and THI-CM scores > 47 (OR 7.43, p = 0.002) with depression. Conclusions: Our study recruited tinnitus participants with slight or mild hearing loss and mild tinnitus handicap. Depression was identified as a predominant factor in tinnitus-related handicap. The mild tinnitus handicap in tinnitus participants may explain the lack of significant differences in depression, sleep quality, and polysomnographic sleep characteristics between tinnitus and non-tinnitus groups. Further extensive and prospective studies are needed to elucidate the complex links among depression, sleep, and tinnitus. [ABSTRACT FROM AUTHOR]