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Background: This study aimed to investigate the clinical and molecular genetic characteristics of ten children with ornithine carbamoyltransferase deficiency (OTCD) in southeastern China, as well as the correlation between the genotype and phenotype of OTCD. Methods: A retrospective analysis was performed on the clinical manifestations, laboratory testing, and genetic test findings of ten children with OTCD admitted between August 2015 and October 2021 at Quanzhou Maternity and Children's Hospital of Fujian Province in China. Results: Five boys presented with early-onset symptoms, including poor appetite, drowsiness, groaning, seizures, and liver failure. In contrast, five patients (one boy and four girls) had late-onset gastrointestinal symptoms as the primary clinical manifestation, all presenting with hepatic impairment, and four with hepatic failure.Nine distinct variants of the OTC gene were identified, including two novel mutations: c.1033del(p.Y345Tfs*50) and c.167T > A(p.M56K). Of seven patients who died, five had early-onset disease despite active treatment. Three patients survived, and two of them underwent liver transplantation. Conclusions: The clinical manifestations of OTCD lack specificity. However, elevated blood ammonia levels serve as a crucial diagnostic clue for OTCD. Genetic testing aids in more accurate diagnosis and prognosis assessment by clinicians. In addition, we identified two novel pathogenic variants and expand the mutational spectrum of the gene OTC, which may contribute to a better understanding of the clinical and genetic characteristics of OTCD patients. [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]

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]

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]

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]

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: 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: 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: 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]

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial–temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial–temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night's sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual's sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e−100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e−100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2–10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses. [ABSTRACT FROM AUTHOR]

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]

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings. [ABSTRACT FROM AUTHOR]

One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems. [ABSTRACT FROM AUTHOR]

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Objectives: This study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE). Methods: Overnight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG. Results: In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep. Conclusion: We concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm. [ABSTRACT FROM AUTHOR]

The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals. [ABSTRACT FROM AUTHOR]

We propose a mathematical model of the human cardiovascular and respiratory systems. The cardiovascular segment of the model simulates the main heart rate, oscillating blood pressure, peripheral vascular resistance, and nonlinear dynamics of the autonomic control of circulation, namely α- and β- sympathetic and parasympathetic control. The respiratory part includes central pattern generator, lungs, and a control loop sensitive to the concentration of CO2 and O2 in the arterial blood. Both parts of the model are linked in a physiological manner. The adequacy of the model is demonstrated by comparing the simulated time series to the experimental records of healthy subjects from the SIESTA database using spectral analysis, statistical analysis, and nonlinear measures of directional coupling. The proposed model can become a useful tool for investigation of the cardiovascular dynamics during sleep. [ABSTRACT FROM AUTHOR]

The aim of this study is to detect changes in a functional state during the performance of monotonous psychomotor test. We propose that these changes represent the repeated episodes of short sleep and spontaneous awakenings from them. We also argue that spontaneous awakenings from sleep stage 2 are accompanied by high-amplitude slow oscillations (SO). 20 subjects participated in 27 experiments during daytime. They performed continuous psychomotor test in a supine position with eyes closed for 1 h. Expert scoring of SOs in the EEG, including K-complexes and other single slow waves, was performed in sleep episodes reaching sleep stage 2, defined as pauses in task performance longer than 3 s. 248 episodes with SOs occurred in time intervals without activity lasting from 3 s to 10 min. In 195 cases, at least one SO was recorded before the test performance was resumed. 248 sleep episodes with 1255 SOs were taken into analysis. It was shown that SOs occur much more frequently just before awakenings (SO1, 12 or less seconds before awakening) than within sleep episodes (12 of more seconds before awakening, SO2). Some SOs were recorded within a few seconds after behavioral awakening (SO3), which has not been previously reported. We propose the hypothesis that SOs which occurred just before resuming performance (SO1) are associated with the unconscious episodic memory that triggers awakening followed by recovery of conscious activity performed prior to falling asleep. We also describe the novel type of SO (SO3) which occurs just after awakening. [ABSTRACT FROM AUTHOR]

Objective measures of activity recovery performance in monotonous psychomotor task after brief episodes of daytime sleep were investigated. We used a psychomotor test in which the partially sleep-deprived subjects had to press the button 10 times with alternating left and right hands for 70 min. The task-induced short episodes of sleep, after which subjects had to resume the task. EEG of the anterior and central lead areas was studied when resuming the task after awakening from the first stage of sleep. We compared the situations of resumption of the pattern with left and right hands. The power of beta- and gamma-EEG oscillations during preparation and beginning of psychomotor activity was found to be higher when the subject starts to press the button with the left (nondominant) hand which may be a manifestation of sleep inertia. We attribute this to the fact that in this case the strategy for resuming activity interrupted by the short sleep is suboptimal and energy intensive, requiring greater cognitive activity. We also assume that this situation is preceded by a lower level of consciousness, in which the subject correctly remembers the instruction, but does reliably start with the dominant hand. However, the subject fully recovers and correctly performs the motor and synchronized cognitive account task immediately after awakening. This study contributes to an objective assessment of fluctuations in the human condition in monotonous work, which can lead to loss of concentration, reduced response time, drowsiness and negatively affect work safety. [ABSTRACT FROM AUTHOR]

Sleep is quantitatively described by subdividing polysomnographic records into intervals each of which is allocated to one of the just 5 all-or-nothing variables called "sleep–wake stages". What are the mechanisms governing the establishment of such 5 relatively stable stages and rapid transitions between them? We modeled these stages as resulting from the competing interactions between the mutually inhibiting drives for wake, NREM sleep, and REM sleep that are proposed to work in a similar way as two-, two-, and one-way switch, respectively. The electromechanical counterparts of the stages were visualized as 5 variants of an electrical circuit connecting these switches with three lamps. During W and transient state N1, two sleep switches are switched off, and three lamps are turned off. During other transitions, one of these lamps is turning on after changing in on–off state of one or two of three switches. During transitions to N2, one (N2) lamp is turning on. During the following transitions to N3, one more (N3) lamp is turning on. During transitions from N2 to R, the N2 lamp is turning off, while the R lamp is turning on. Estimates of stage-specific scores on the 1st and 2nd principal components of the electroencephalographic spectra provided empirical evidence for such on–off states of these switches. [ABSTRACT FROM AUTHOR]

Awakening is a transitional process from sleep to wakefulness. There is a certain restructuring in the work of brain structures during this period, which further allows a person to perform conscious activity. The study of interhemispheric asymmetry of amplitude–amplitude couplings of EEG rhythms was carried out. A psychomotor test was used in experiments. This test allows us to observe the moments of falling asleep and waking up when performing monotonous work. Multichannel EEG was recorded simultaneously with the test. The data of 14 subjects who reached the second stage of sleep during the experiment were analyzed. The selected 20 s EEG segments prior to awakening were processed using a continuous wavelet transform based on the "mother" complex Morlet wavelet. The Kendall correlation coefficient was used to evaluate the measure of EEG rhythms coupling. Quantitative changes in the couplings of EEG rhythms were revealed as the moment of awakening approached: in the two most distant time intervals, the total number of asymmetric connections was five to three, and in the two closest segments, there was only one coupling of EEG rhythms. This is not just a decrease in the number of connections, but also a change of EEG rhythms coupling throughout the entire 20-s segment. The dynamic nature of the EEG rhythms coupling in the hemispheres before awakening to resume the task is shown, which may indicate the dynamic relationship of structural and functional associations of the brain. [ABSTRACT FROM AUTHOR]

We performed a wavelet analysis of oscillatory dynamics in brain activity of patients with obstructive sleep apnoea (OSA) ( N = 10 , age 52.8 ± 13 years, median 49 years; male/female ratio: 73), compared with a group of apparently healthy participants ( N = 15 , age 51.5 ± 29.5 years, median 42 years; malefemale ratio: 87), based on the calculation of patterns from electroencephalographic (EEG) signals of ***nighttime polysomnography (PSG) recordings. It was shown that there were no statistical differences in the number and duration of nocturnal sleep stages in patients of the two groups. The distributions of the number N and duration T of oscillatory wavelet patterns of EEG signals in bands Δ f i = [ i ; i + 2 ] , where i takes values from 2 to 38, have been estimated. Statistically significant differences in the characteristics of the distributions of the number and duration of patterns for the high-frequency bands Δ f 17 – Δ f 19 (32 – 38 Hz) are shown. It is demonstrated that estimation of the coordinates of the height and the value of the maximum point of the distribution of the considered quantitative characteristics of the patterns allows clustering of the EEG processing results and demonstrates the separation of the nocturnal sleep characteristics of OSA patients and healthy volunteers. Evaluation based on the Mann–Whitney U-test shows statistically significant differences between N and T patterns assessed from nocturnal EEG recordings. The number and duration of high-frequency patterns are significantly reduced in the EEG of OSA patients compared to essentially healthy participants. It is possible that such a change in high-frequency activity is related to known structural changes in the brain. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a very serious and multifactorial sleep disorder that is closely related to the disruption of sleep homeostasis. It is known that such sleep encephalographic (EEG) phenomenon as sleep spindles (SSs) supports sleep stability and may display a sleep protective function. Thus, it is of particular interest to assess the SS pattern in OSA patients, which is the aim of the present research. We investigated whether SS activity could be altered in patients with moderate degree of OSA compared with non-OSA subjects. Twenty-nine meddle-aged OSA patients (26 male and 3 female) and 26 controls underwent full-night polysomnography (PSG). SSs were automatically detected during stage 2 (N2) of non-rapid eye movements. The SS activity characteristics involved total number, mean density, mean maximum amplitude, and mean frequency. All differences were considered statistically significant at p < 0.05. We noted a significant decrease in the density and number of central SSs in patients with OSA compared to controls; however, the amplitude is significantly higher in OSA subjects. To summarize, our results show that OSA leads to significant disruption of SS density, reduction of their number, and frequency in N2 sleep stages. These findings can be evidence of the extinction of a brain protective mechanism against exciting stimuli during apnea episodes in OSA patients with a long duration of sleep disturbances. [ABSTRACT FROM AUTHOR]

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an F 1 score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels. [ABSTRACT FROM AUTHOR]

Summary: Several lines of evidence suggest that cluster headache is related to chronobiology and sleep. Nevertheless, the nature of such a relationship is unclear. In this view, the objective evaluation of sleep in cluster headache has strong theoretical and clinical relevance. Here, we provide an in‐depth narrative review of the literature on objective sleep assessment in cluster headache. We found that only a small number of studies (N = 12) focused on this topic. The key research aims were directed to assess: (a) the relationship between cluster headache and sleep breathing disorders; (b) the temporal relationship between sleep stages/events and cluster headache attacks; (c) sleep macrostructure in patients with cluster headache. No studies considered sleep microstructure. The reviewed studies are heterogeneous, conducted by a few research groups, and often characterised by relevant methodological flaws. Results are substantially inconclusive considering the main hypothesis. We outline several methodological points that should be considered for future research, and suggest that evaluating sleep microstructure, local sleep electrophysiology and actigraphic measures may strongly increase knowledge on the relationship between sleep and cluster headache. [ABSTRACT FROM AUTHOR]

Summary: Excessive fragmentary myoclonus (EFM) is a frequent finding during routine video‐polysomnography (VPSG). We aimed to automatically measure the prevalence of EFM according to current American Academy of Sleep Medicine (AASM) criteria, and the fragmentary myoclonus index (FMI) in sleep stage N1, N2, N3, rapid eye movement (REM) sleep and wake in a large patient population. A total of 500 VPSG recordings of patients admitted to the Sleep Laboratory, Department of Neurology, Medical University of Innsbruck, Austria, between May 1, 2022 and February 28, 2023, were included. EFM according to AASM criteria and FMI were computed by applying a previously validated algorithm. EFM was automatically detected in 121 of the 500 Sleep Laboratory patients (24.2%, 95% confidence interval 20.1%–28.9%). FMI increased with age, male gender, apnea–hypopnea‐index (AHI), oxygen desaturation index (ODI), and periodic leg movements of sleep (PLMS) index. FMI was highest in REM sleep behaviour disorder (RBD), followed by neurodegenerative and internal medicine diseases, but the increase in the FMI was not explained by the disease itself but rather by the age and sex of the patients. Almost a quarter of our patient population had EFM. However, the prevalence of EFM does not allow the drawing of any conclusions about the pathophysiology of EFM or even the determination of a pathological FMI cut‐off value. Associations of the FMI with age, sex, AHI, ODI and PLMS are in line with previous studies, but the FMI needs to be evaluated in different disease entities to learn more about its pathophysiology. [ABSTRACT FROM AUTHOR]

Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall performance, they often exhibit low per-class performance for N1-stage, necessitating an adjustment of loss function. However, the efficacy of such adjustment is constrained by the training process. In this study, a pioneering training approach called separating training is introduced, alongside a novel model, to enhance performance. The developed model comprises 15 CNN models with varying loss function weights for feature extraction and 1 BiLSTM for sequence classification. Due to its architecture, this model cannot be trained using an end-to-end approach, necessitating separate training for each component using the Sleep-EDF dataset. Achieving an overall accuracy of 87.02%, MF1 of 82.09%, Kappa of 0.8221, and per-class F1-socres (W 90.34%, N1 54.23%, N2 89.53%, N3 88.96%, and REM 87.40%), our model demonstrates promising performance. Comparison with sleep technicians reveals a Kappa of 0.7015, indicating alignment with reference sleep stags. Additionally, cross-dataset validation and adaptation through training with the SHHS dataset yield an overall accuracy of 84.40%, MF1 of 74.96% and Kappa of 0.7785 when tested with the Sleep-EDF-13 dataset. These findings underscore the generalization potential in model architecture design facilitated by our novel training approach. [ABSTRACT FROM AUTHOR]

Sleep stage classification is essential in evaluating sleep quality. Sleep disorders disrupt the periodicity of sleep stages, especially the common obstructive sleep apnea (OSA). Many methods only consider how to effectively extract features from physiological signals to classify sleep stages, ignoring the impact of OSA on sleep staging. We propose a structured sleep staging network (SSleepNet) based on OSA to solve the above problem. This research focused on the effect of sleep apnea patients with different severity on sleep staging performance and how to reduce this effect. Considering that the transfer relationship between sleep stages of OSA subjects is different, SSleepNet learns comprehensive features and transfer relationships to improve the sleep staging performance. First, the network uses the multi-scale feature extraction (MSFE) module to learn rich features. Second, the network uses a structured learning module (SLM) to understand the transfer relationship between sleep stages, reducing the impact of OSA on sleep stages and making the network more universal. We validate the model on two datasets. The experimental results show that the detection accuracy can reach 84.6% on the Sleep-EDF-2013 dataset. The detection accuracy decreased slightly with the increase of OSA severity on the Sleep Heart Health Study (SHHS) dataset. The accuracy of healthy subjects to severe OSA subjects ranged from 79.8 to 78.4%, with a difference of only 1.4%. It shows that the SSleepNet can perform better sleep staging for healthy and OSA subjects. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Sleep inertia is the transition state during which alertness and cognitive performance are temporarily impaired after awakening. Magnitude and time course of sleep inertia are characterized by high individual variability with large differences between the cognitive functions affected. This period of impairment is of concern to pilots, who take sleep or nap periods during on-call work hours or in-flight rest, then need to perform safety-critical tasks soon after waking. This review analyzes literature related to sleep inertia and countermeasures applicable for aviation. METHODS: The large part of scientific literature that focuses on sleep inertia is based on studies in patients with chronic sleep inertia. We analyzed 8 narrative reviews and 64 papers related to acute sleep inertia in healthy subjects. DISCUSSION: Sleep inertia is a multifactorial, complex process, and many different protocols have been conducted, with a low number of subjects, in noncontrolled laboratory designs, with questionnaires or cognitive tests that have not been replicated. Evidence suggests that waking after sleep loss, or from deeper stages of sleep, can exacerbate sleep inertia through complex interactions between awakening and sleep-promoting brain structures. Nevertheless, no meta-analyses are possible and extrapolation to pilots’ performances is hypothetical. Studies in real life or simulated operational situations must be conducted to improve the description of the impact of sleep inertia and kinetics on pilots’ performances. Taking rest or sleep time remains the main method for pilots to fight against fatigue and related decreases in performance. We propose proactive strategies to mitigate sleep inertia and improve alertness. [ABSTRACT FROM AUTHOR]