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With the growing prominence of peripheral nerve stimulation technology, the clinical applications and potential neurophysiological mechanisms of external trigeminal nerve stimulation (eTNS) have garnered increasing attention. Despite its status as the sole neuromodulation method commonly employed in sleep, no studies have explored the effects of eTNS at varying frequencies on sleep activities. This study aims to investigate the regulatory effects of high-frequency and low-frequency eTNS on sleep activities using polysomnography. In this within-subjects experiment, 20 participants underwent a night of adaptation sleep, followed by 8-h sessions of sham, 120Hz-, and 2Hz-eTNS interventions in a randomized order in the sleep laboratory, with polysomnographic signals collected throughout. The results indicated that 120Hz-eTNS significantly improved sleep efficiency, increased N2 sleep proportion, and reduced sleep latency, without significantly affecting sleep stage transition probabilities, sleep duration, or sleep-specific wave activities. Conversely, while 2Hz-eTNS did not impact sleep efficiency or latency, it increased the proportion of N3 sleep, stabilizes N3 sleep, and enhanced the survival probability of N3 and REM sleep duration. Additionally, it increases the density of slow oscillations (SOs), improved the coupling ratio of SO-spindles, and enhanced coupling timing accuracy. These findings suggest that eTNS during sleep can indeed modulate sleep activities, with different frequencies exerting distinct regulatory effects. This may hold significant value for advancing the clinical application and efficacy of eTNS. • This study used overnight polysomnography to explore eTNS effects on sleep, revealing significant frequency-dependent modulations. • Findings demonstrated that 120Hz-eTNS improved sleep efficiency and latency while 2Hz-eTNS mainly influenced N3 stage sleep. • Additionally, 2Hz-eTNS enhanced slow oscillations' (SOs) density, improved SO-spindle coupling ratio and timing, effects not seen with 120Hz-eTNS. • This study highlights eTNS parameter effects, underscoring its potential for personalized modulation and clinical applications. [ABSTRACT FROM AUTHOR]

Introduction: This study evaluated the performance of a wrist-worn wearable, Verily Study Watch (VSW), in detecting key sleep measures against polysomnography (PSG). Methods: We collected data from 41 adults without obstructive sleep apnea or insomnia during a single overnight laboratory visit. We evaluated epoch-by-epoch performance for sleep vs. wake classification, sleep stage classification and duration, total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings (NAWK). Performance metrics included sensitivity, specificity, Cohen's kappa, and Bland-Altman analyses. Results: Sensitivity and specificity (95% CIs) of sleep vs. wake classification were 0.97 (0.96, 0.98) and 0.70 (0.66, 0.74), respectively. Cohen's kappa (95% CI) for 4-class stage detection was 0.64 (0.18, 0.82). Most VSW sleep measures had proportional bias. The mean bias values (95% CI) were 14.0 min (5.55, 23.20) for TST, −13.1 min (−21.33, −6.21) for WASO, 2.97% (1.25, 4.84) for SE, −1.34 min (−7.29, 4.81) for SOL, 1.91 min (−8.28, 11.98) for light sleep duration, 5.24 min (−3.35, 14.13) for deep sleep duration, and 6.39 min (−0.68, 13.18) for REM sleep duration. Mean and median NAWK count differences (95% CI) were 0.05 (−0.42, 0.53) and 0.0 (0.0, 0.0), respectively. Discussion: Results support applying the VSW to track overnight sleep measures in free-living settings. Registered at clinicaltrials.gov (NCT05276362). [ABSTRACT FROM AUTHOR]

Purpose: The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. Methods: The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. Results: The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. Conclusion: The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses. [ABSTRACT FROM AUTHOR]

In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics. [ABSTRACT FROM AUTHOR]

Sleep disorders are the most widespread mental disorders after stroke and hurt survivors' functional prognosis, response to restoration, and quality of life. This review will address an overview of the progress of research on the biological mechanisms associated with stroke-complicating sleep disorders. Extensive research has investigated the negative impact of stroke on sleep. However, a bidirectional association between sleep disorders and stroke exists; while stroke elevates the risk of sleep disorders, these disorders also independently contribute as a risk factor for stroke. This review aims to elucidate the mechanisms of stroke-induced sleep disorders. Possible influences were examined, including functional changes in brain regions, cerebrovascular hemodynamics, neurological deficits, sleep ion regulation, neurotransmitters, and inflammation. The results provide valuable insights into the mechanisms of stroke complicating sleep disorders. [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]

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ , θ , α , and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. [ABSTRACT FROM AUTHOR]

Summary: Given the significant impact of sleep on overall health, radar technology offers a promising, non‐invasive, and cost‐effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)‐based classification. In this study, we employed an attention‐based bidirectional long short‐term memory (Attention Bi‐LSTM) model to accurately predict sleep stages using 60 GHz frequency‐modulated continuous‐wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid‐eye‐movement (REM) sleep, and non‐REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification. [ABSTRACT FROM AUTHOR]

Summary: Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter‐rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage‐specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea–hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non‐rapid eye movement stages. [ABSTRACT FROM AUTHOR]

Obesity and ageing are the most important risk factors for sleep apnoea syndrome (SAS); however, the role of body mass index (BMI) on sleep status in healthy older adults is unclear. To explore sleep parameters according to BMI among active older adults, we cross-sectionally examined the relationship between sleep-related parameters and BMI in 32 Japanese adults aged from 83 to 95 years without long-term care who were unaware of having SAS. Correlation and linear regression analyses were performed. Moderate or severe SAS prevalence was high in both those with low (68.8%) and high (68.8%) BMI. A higher increase in apnoea-hypopnoea index (AHI) was negatively correlated with sleep depth in the high-BMI group. In the low-BMI group, the number of awakenings and age were positively correlated with AHI. Older adults may have SAS regardless of their BMI, and the sleep status of patients with SAS may vary by BMI.

Sleep apnea is a serious sleep disorder that is characterized by abnormal pauses in breathing. It is related to neural activity that depends on the associated sleep stage of an apnea event. Electroencephalography (EEG) signal is widely used for neural activity analysis and can play a significant role along with sleep stage information in sleep apnea event detection. In this paper, a sleep stage adaptive deep neural network based apnea detection scheme, namely StApneaNet is proposed using single channel EEG signal. The proposed StApneaNet consists of two major blocks: joint model and decision model. In the joint model, multi-band EEG signals are used as input to a multi-kernel CNN block which gives time sequential inter-band related features through causal convolution. A residual squeeze and excitation based channel attention mechanism is then applied to the output feature channels which are further processed through a bi-directional long short term memory (Bi-LSTM) layer along with a temporal attention block. Both apnea prediction and sleep stage prediction are jointly optimized in the joint model that is initially pre-trained and later integrated as a non-trainable block with the trainable decision fusion block inside the decision model for the final apnea event detection. The whole trained network so built is capable of efficiently predicting apnea frames in the testing phase. Extensive experimentation is carried out on three publicly available EEG datasets for subjects-combined and subject-independent classification of apnea events and normal events of an apnea patient. It is found that the proposed StApneaNet offers 91.79% sensitivity, 88.13% specificity, and 89.96% accuracy for a 10-fold subjects-combined cross-validation scheme which are better than those of the existing methods.

As an increasing number of people suffer from sleep disorders, such as insomnia or sleep apnea, sleep monitoring and management using consumer devices have gained increasing attention from research communities. As sleep quality is closely related to sleep structure based on hypnograms, the classification of sleep stages over the course of the night is important for accurate sleep monitoring. We present sleep stage classification using a smartphone equipped with ultra-wideband (UWB) radar. We focused on the development of easily accessible sleep monitoring system for the general population by placing the smartphone on a table near a bed, which is commonly used during sleep. We collected 509 nights of UWB radar and nocturnal in-laboratory polysomnography (PSG) data from various participants, including patients with apnea, using a customized Samsung Galaxy smartphone with a UWB radar chip placed on a table near the bed. A combination of 1D convolutional neural network and transformer architecture was proposed in this study, and a domain adaptation technique was applied to train the model with both large-scale respiratory signals from open database PSGs and UWB radar data to boost the performance by overcoming the lack of UWB radar data. With 5-fold validation, an epoch-by-epoch comparison between the predicted and expert-annotated four sleep stages (Wake, REM sleep, light sleep, and deep sleep) resulted in 0.76 of accuracy and 0.64 of Cohen’s kappa. This study demonstrated that sleep stages can be monitored with substantial accuracy by simply placing a smartphone on a bedtable, making it highly usable and reliable in real use cases.

Bruxism is a sleep disorder which is manifested by unintentional grinding and clenching of teeth during sleep. An automated sleep bruxism recognition system using single channel EEG data is proposed in this paper which is based on Inverse Discrete Wavelet Transformed Reconstructed Band Limited (IDWT-RBL) signals. These band limited EEG signals are used for extracting various features. Instead of using handcrafted features, feature reduction is done by ranking using statistical test scoring combined with classifier testing. This technique finds optimal features, reduces model complexity, lowers computational burden and increases model interpretability. Abundant features from time, frequency and statistical domains are used primarily so that no significant feature is ignored. Choosing a good subset of features from a larger set is a challenge for data with low sample size. To meet this challenge, a Group wise Feature Ranking (GFR) technique is introduced to reduce feature dimension. After statistical ranking and group wise averaging the scores, most significant groups of features are chosen. The proposed scheme is validated on a publicly available dataset. This process is examined for both unlabeled and labeled sleep stage. For segments with unlabeled sleep stage, cubic Support Vector Machine (SVM) performed best for F3C3 channel using 6 features with an accuracy of 97.83%. For segments with labeled sleep stage, F3C3 and REM sleep stage using 10 features performed best with 98.39% accuracy. The accuracy of proposed method is superior to most recent bruxism detection techniques. Finally, the GFR technique is applied to detect sleep disordered breathing (SDB). The outstanding performance to detect SDB clearly demonstrates the versatility of the proposed GFR technique to solve binary classification problem using EEG signals. Moreover, the introduced GFR technique enhances confidence and pellucidity of the system as it is more explainable.

Abstract 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.

Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need tomanually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging. Compared with traditional deep learning methods, TPON uses a meta-learning algorithm, which generalizes the classifier to new classes that are not visible in the training set, and only have a few examples for each new class. We learn the prototypes of existing objects through meta-training, and capture the sleep features of new objects through the "learn to learn" method of meta-learning. The prototype distribution of the class is optimized and captured by using support set and unlabeled high confidence samples to increase the authenticity of the prototype. Compared with traditional prototype networks, TPON can effectively solve too few samples in few-shot learning and improve the matching degree of prototypes in prototype network. The experimental results on the public SleepEDF-2013 dataset show that the proposed algorithm outperform than most advanced algorithms in the overall performance. In addition, we experimentally demonstrate the feasibility of cross-channel recognition, which indicates that there are many similar sleep EEG features between different channels. In future research, we can further explore the common features among different channels and investigate the combination of universal features in sleep EEG. Overall, our method achieves high accuracy in sleep stage classification, demonstrating the effectiveness of this approach and its potential applications in other medical fields. [ABSTRACT FROM AUTHOR]

Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research. [ABSTRACT FROM AUTHOR]

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively. [ABSTRACT FROM AUTHOR]

Due to the high demands of competitive sports, the sleep architecture of adolescent athletes may be influenced by their regular training. To date, there is no clear evidence on how training characteristics (intensity, time of day, number of sessions) influence sleep quality and quantity. 53 male soccer players (M = 14.36 years, SD = 0.55) of Austrian U15 (n = 45) and U16 elite teams (n = 8) were tested on at least three consecutive days following their habitual training schedules. Participants completed daily sleep protocols (7 a.m., 8 p.m.) and questionnaires assessing sleep quality (PSQI), chronotype (D-MEQ), competition anxiety (WAI-T), and stress/recovery (RESTQ). Electrocardiography (ECG) and actigraphy devices measured sleep. Using sleep protocols and an ECG-based multi-resolution convolutional neural network (MCNN), we found that higher training intensity leads to more wake time, that later training causes longer sleep duration, and that one training session per day was most advantageous for sleep quality. In addition, somatic complaints assessed by the WAI-T negatively affected adolescent athletes' sleep. Individual training loads and longer recovery times after late training sessions during the day should be considered in training schedules, especially for adolescent athletes. MCNN modeling based on ECG data seems promising for efficient sleep analysis in athletes. [ABSTRACT FROM AUTHOR]

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs.

Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working on unseen new data due to individual differences. In this research, a developed LSTM-Ladder-Network (LLN) model is proposed for automatic sleep stage classification. Several features are extracted for each epoch and combined with the following epochs to form a cross-epoch vector. The long short-term memory (LSTM) network is added into the basic ladder network (LN) to learn the sequential information of adjacent epochs. The developed model is implemented based on a transductive learning scheme to avoid the issue of accuracy loss caused by individual differences. In this process, the labeled data pre-trains the encoder, and the unlabeled data re- fine the model parameters by minimizing the reconstruction loss. The proposed model is evaluated on the data from public database and hospital. Comparison experiments were conducted where the developed LLN model achieved rather satisfied performance while dealing with the unseen new data. The obtained results demonstrate the effectiveness of the proposed approach in addressing individual differences. This can improve the quality of automatic sleep staging when assessed on different individuals and has strong application potential as a computer aided approach for sleep staging.

Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working on unseen new data due to individual differences. In this research, a developed LSTM-Ladder-Network (LLN) model is proposed for automatic sleep stage classification. Several features are extracted for each epoch and combined with the following epochs to form a cross-epoch vector. The long short-term memory (LSTM) network is added into the basic ladder network (LN) to learn the sequential information of adjacent epochs. The developed model is implemented based on a transductive learning scheme to avoid the issue of accuracy loss caused by individual differences. In this process, the labeled data pre-trains the encoder, and the unlabeled data re- fine the model parameters by minimizing the reconstruction loss. The proposed model is evaluated on the data from public database and hospital. Comparison experiments were conducted where the developed LLN model achieved rather satisfied performance while dealing with the unseen new data. The obtained results demonstrate the effectiveness of the proposed approach in addressing individual differences. This can improve the quality of automatic sleep staging when assessed on different individuals and has strong application potential as a computer aided approach for sleep staging. [ABSTRACT FROM AUTHOR]

Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person’s physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. [ABSTRACT FROM AUTHOR]

Background: Donepezil had been recognized to have impact on sleep quality in demented patients. However, there was insufficient evidences about the actual effect of donepezil in the sleep architectures. Our meta-analysis aimed to evaluate the changes of sleep architectures related to donepezil use. Methods: Followed the PRISMA2020 and AMSTAR2 guidelines, electronic search had been performed on the databases of PubMed, Embase, ScienceDirect, ClinicalKey, Cochrane CENTRAL, ProQuest, Web of Science, and ClinicalTrials.gov. The outcome measurement was changes of sleep parameters detected by polysomnography. A random-effects meta-analysis was conducted. Results: Total twelve studies had been involved. The percentage of REM sleep would significantly increase after donepezil treatment (Hedges' g = 0.694, p

To investigate the effect of frequently reported between-individual (viz., age, gender, body mass index [BMI], and apnea-hypopnea index [AHI]) and within-individual (viz., sleep stage and sleep position) snoring sound-related factors on snoring sound parameters in temporal, intensity, and frequency domains. This study included 83 adult snorers (mean ± SD age: 42.2 ± 11.3 yrs; male gender: 59%) who underwent an overnight polysomnography (PSG) and simultaneous sound recording, from which a total of 131,745 snoring events were extracted and analyzed. Data on both between-individual and within-individual factors were extracted from the participants' PSG reports. Gender did not have any significant effect on snoring sound parameters. The fundamental frequency (FF; coefficient = −0.31; P = 0.02) and dominant frequency (DF; coefficient = −12.43; P < 0.01) of snoring sounds decreased with the increase of age, and the second formant increased (coefficient = 22.91; P = 0.02) with the increase of BMI. Severe obstructive sleep apnea (OSA; AHI ≥30 events/hour), non-rapid eye movement sleep stage 3 (N3), and supine position were all associated with more, longer, and louder snoring events (P < 0.05). Supine position was associated with higher FF and DF, and lateral decubitus positions were associated with higher formants. Within the limitations of the current patient profile and included factors, AHI was found to have greater effects on snoring sound parameters than the other between-individual factors. The included within-individual factors were found to have greater effects on snoring sound parameters than the between-individual factors under study. • Sleep stage and sleep position have significant effects on snoring sound. • Gender did not have significant effect on snoring sound. • Severe OSA, N3 stage, and supine position relate to more and louder snoring sounds. • Positional therapy should be considered for all snorers. [ABSTRACT FROM AUTHOR]

Abstract The objectives of this review were to explain the neurologic processes that control the human sleep–wake cycle as well as the pathophysiology of the human circadian clock. Non-rapid eye movement and rapid eye movement sleep are the two main phases of sleep. When triggered by circadian input from the anterior hypothalamus and sleep–wake homeostatic information from endogenous chemical signals (example, adenosine), the ventrolateral preoptic nucleus initiates the onset of sleep. Arousal in which there is a conscious monitoring of the surroundings and the ability to respond to external stimuli is known as wakefulness. It contrasts the state of sleep, in which receptivity to external stimuli is reduced. The higher the synchronous firing rates of cerebral cortex neurons, the longer the brain has been awake. Sleep–wake disturbances induced by endogenous circadian system disruptions or desynchronization between internal and external sleep–wake cycles are known as circadian rhythm sleep–wake disorder (CRSWD). Patients with CRSWD usually report chronic daytime drowsiness and/or insomnia, which interferes with their activities. CRSWD is diagnosed based on the results of some functional evaluations, which include measuring the circadian phase using core body temperature, melatonin secretion timing, sleep diaries, actigraphy, and subjective experiences (example, using the Morningness–Eveningness Questionnaire). CRSWD is classified as a dyssomnia in the second edition of the International Classification of Sleep Disorders, with six subtypes: advanced sleep phase, delayed sleep phase, irregular sleep–wake, free running, jet lag, and shift work types. CRSWD can be temporary (due to jet lag, shift work, or illness) or chronic (due to delayed sleep–wake phase disorder, advanced sleep–wake phase disorder, non-24-h sleep–wake disorder, or irregular sleep–wake rhythm disorder). The inability to fall asleep and wake up at the desired time is a common symptom of all CRSWDs.

Background: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results: The experimental results have shown that when the number of subperiods is 30, the a (8–13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system. [ABSTRACT FROM AUTHOR]

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs. [ABSTRACT FROM AUTHOR]

BACKGORUND. Obstructive sleep apnea syndrome (OSAS) is a major health problem. The complications of this condition decrease the quality of life. Patients with OSAS present an increased risk of cardiovascular diseases: arterial hypertension, heart failure, myocardial ischemia, myocardial infarction, stroke. MATERIAL AND METHODS. The patients included in the study were divided into 4 groups, depending on the value of the apnea-hypopnea index (AHI): Control Group A with AHI<5; Group B – mild apnea, 530. AHI and heart rate at different sleep stages were correlated. RESULTS. 133 patients were analyzed. The average age was 47 years±15 years, and the gender distribution shows women 21.1% and men 78.9%. The average value of oxygen saturation was 90.45±4.99%. The average value of heart rate during sleep was 104±67.7 beats/minute. The heart rate was variable depending on the sleep stage, with p = 0.0089 regardless of the sleep stage. CONCLUSION. It can be argued that sleep apnea is a factor that interferes with the adaptive mechanisms of heart rate during sleep. Polysomnographic evaluation of sleep stages and clinical examination of patients with OSAS allow the initiation of treatments to prevent the occurrence of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this study on Spinal Cord Injury (SCI) patients with cervical or thoracic lesion was to assess whether disturbances of ANS control, according to location, might differently affect vagal and sympatho-vagal markers during sleep and orthostatic challenge. We analyzed with linear and nonlinear techniques beat-by-beat RR and arterial pressure (and respiration) variability signals, extracted from a polysomnographic study and a rest–tilt test. We considered spontaneous or induced sympathetic excitation, as obtained shifting from non-REM to REM sleep or from rest to passive tilt. We obtained evidence of ANS cardiac (dys)regulation, of greater importance for gradually proximal location (i.e., cervical) SCI, compatible with a progressive loss of modulatory role of sympathetic afferents to the spinal cord. Furthermore, in accordance with the dual, vagal and sympathetic bidirectional innervation, the results suggest that vagally mediated negative feedback baroreflexes were substantially maintained in all cases. Conversely, the LF and HF balance (expressed specifically by normalized units) appeared to be negatively affected by SCI, particularly in the case of cervical lesion (group p = 0.006, interaction p = 0.011). Multivariate analysis of cardiovascular variability may be a convenient technique to assess autonomic responsiveness and alteration of functionality in patients with SCI addressing selectively vagal or sympathetic alterations and injury location. This contention requires confirmatory studies with a larger population. [ABSTRACT FROM AUTHOR]

Huijun Wang,1– 3,&ast; Guodong Lin,4,&ast; Yanru Li,1– 3 Xiaoqing Zhang,1– 3 Wen Xu,1– 3 Xingjun Wang,4 Demin Han1– 3 1Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People’s Republic of China; 3Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People’s Republic of China; 4Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Demin HanBeijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People’s Republic of ChinaTel +86-010-58269335Fax +86-010-58269331Email deminhan_ent@hotmail.comXingjun WangTsinghua Shenzhen International Graduate School, University Town of Shenzhen, Nanshan District, Shenzhen, 518055, People’s Republic of ChinaTel +86-18038153071Email wangxingjun@tsinghua.edu.cnPurpose: To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB).Patients and Methods: Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen’s Kappa coefficient (&kgreen;). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model.Results: The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (&kgreen;=0.7817) in 5-stages, and the accuracy was 96.76% (&kgreen;=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P> 0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively.Conclusion: This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.Keywords: sleep-disordered breathing, SDB, deep learning, sleep stage, children

IntroductionAccurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However, the occupancy of different sleep stages is unbalanced throughout the sleep process, which makes the EEG datasets of different sleep stages have a class imbalance, which will eventually affect the automatic assessment of sleep stages.MethodIn this paper, we propose a Residual Dense Block and Deep Convolutional Generative Adversarial Network (RDB-DCGAN) data augmentation model based on the DCGAN and RDB, which takes two-dimensional continuous wavelet time–frequency maps as input, expands the minority class of sleep EEG data and later performs sleep staging by Convolutional Neural Network (CNN).Results and discussionThe results of the CNN classification comparison test with the publicly available dataset Sleep-EDF show that the overall sleep staging accuracy of each stage after data augmentation is improved by 6%, especially the N1 stage, which has low classification accuracy due to less original data, also has a significant improvement of 19%. It is fully verified that data augmentation by improving the DCGAN model can effectively improve the classification problem of the class imbalance sleep dataset.

Background: Donepezil had been recognized to have impact on sleep quality in demented patients. However, there was insufficient evidences about the actual effect of donepezil in the sleep architectures. Our meta-analysis aimed to evaluate the changes of sleep architectures related to donepezil use.Methods: Followed the PRISMA2020 and AMSTAR2 guidelines, electronic search had been performed on the databases of PubMed, Embase, ScienceDirect, ClinicalKey, Cochrane CENTRAL, ProQuest, Web of Science, and ClinicalTrials.gov. The outcome measurement was changes of sleep parameters detected by polysomnography. A random-effects meta-analysis was conducted.Results: Total twelve studies had been involved. The percentage of REM sleep would significantly increase after donepezil treatment (Hedges' g = 0.694, p < 0.001). Compared to placebo/controls, subjects with donepezil would had significantly increased percentage of REM sleep stage (Hedges' g = 0.556, p = 0.018). Furthermore, donepezil was also associated with the decreased stage 2 sleep percentage, sleep efficiency, or total sleep time in different analysis conditions.Conclusion: Our meta-analysis provided detailed changes of sleep architectures related to donepezil treatment. Further larger sample size studies with stricter control of potential moderators are needed to clarify these issues. [ABSTRACT FROM AUTHOR]

Daily monitoring is important, even for healthy children, because sleep plays a critical role in their development and growth. Polysomnography is necessary for sleep monitoring. However, measuring sleep requires specialized equipment and knowledge and is difficult to do at home. In recent years, smartwatches and other devices have been developed to easily measure sleep. However, they cannot measure children's sleep, and contact devices may disturb their sleep.A non-contact method of measuring sleep is the use of video during sleep. This is most suitable for the daily monitoring of children’s sleep, as it is simple and inexpensive. However, the algorithms have been developed only based on adult sleep, whereas children’s sleep is known to differ considerably from that of adults.For this reason, we conducted a non-contact estimation of sleep stages for children using video. The participants were children between the ages of 0–6 years old. We estimated the four stages of sleep using the body movement information calculated from the videos recorded. Six parameters were calculated from body movement information. As children’s sleep is known to change significantly as they grow, estimation was divided into two groups (0–2 and 3–6 years).The results show average estimation accuracies of 46.7 ± 6.6 and 49.0 ± 4.8% and kappa coefficients of 0.24 ± 0.11 and 0.28 ± 0.06 in the age groups of 0–2 and 3–6 years, respectively. This performance is comparable to or better than that reported in previous adult studies.

The interactions of the principal circadian clock with the homeostatic sleep process create the time-sensitive window for easy falling asleep in the evening, which is affected by a thermoregulatory process. It has been hypothesized that the changes in skin and core body temperatures before the sleep onset might play a direct role in sleep regulation. To determine this time window, we recorded from 20 healthy participants (11 women and 9 men), aged 26–58 years, one overnight own-home ambulatory polysomnography and measured continuously wrist skin temperature with a wrist-worn accelerometer containing a skin temperature thermometer. Wrist skin temperatures which were read out from the thermometer of the accelerometers were modeled using linear mixed models, and the linear effect of time before the sleep onset on wrist temperature was analyzed using a mixed model with the sex and age as the covariates. We found that wrist skin temperatures increased on average by 0.6° (of Celcius) in 10 min prior to the sleep onset and could be tracked robustly along a slope of time (p = 0.004). Our current findings may be useful in further characterizing the window of time and its boundaries for easy falling asleep. [ABSTRACT FROM AUTHOR]

Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

Sleep quality and duration are critical indicators of individual health. Traditional methods of monitoring them either were limited to the application environment or resulted in discomfort. To overcome these existing limitations, we propose a noncontact sleep monitoring system placed under the mattress, named SleepMatrix, to provide users with a comprehensive picture of their real-time to long-term health status. SleepMatrix is a sleep monitoring system that consists of two parts: a front-end sensor array and a cloud-edge data processing system. The data-driven system, based on the Internet of Things, can be flexibly deployed in environments from homes to transportation, and a variety of centralized control and monitoring functions can be flexibly deployed under different application scenarios. At the same time, users can view real-time and historical data through supporting applications and understand the sleep monitoring results. SleepMatrix is a complete and systematic sleep monitoring tool that can help people understand their health status and its evolution from a new perspective and therefore may contribute greatly to informing users of potential illnesses.

The objectives of this review were to explain the neurologic processes that control the human sleep-wake cycle as well as the pathophysiology of the human circadian clock. Non-rapid eye movement and rapid eye movement sleep are the two main phases of sleep. When triggered by circadian input from the anterior hypothalamus and sleep-wake homeostatic information from endogenous chemical signals (example, adenosine), the ventrolateral preoptic nucleus initiates the onset of sleep. Arousal in which there is a conscious monitoring of the surroundings and the ability to respond to external stimuli is known as wakefulness. It contrasts the state of sleep, in which receptivity to external stimuli is reduced. The higher the synchronous firing rates of cerebral cortex neurons, the longer the brain has been awake. Sleep-wake disturbances induced by endogenous circadian system disruptions or desynchronization between internal and external sleep-wake cycles are known as circadian rhythm sleep-wake disorder (CRSWD). Patients with CRSWD usually report chronic daytime drowsiness and/or insomnia, which interferes with their activities. CRSWD is diagnosed based on the results of some functional evaluations, which include measuring the circadian phase using core body temperature, melatonin secretion timing, sleep diaries, actigraphy, and subjective experiences (example, using the Morningness-Eveningness Questionnaire). CRSWD is classified as a dyssomnia in the second edition of the International Classification of Sleep Disorders, with six subtypes: advanced sleep phase, delayed sleep phase, irregular sleep-wake, free running, jet lag, and shift work types. CRSWD can be temporary (due to jet lag, shift work, or illness) or chronic (due to delayed sleep-wake phase disorder, advanced sleep-wake phase disorder, non-24-h sleep-wake disorder, or irregular sleep-wake rhythm disorder). The inability to fall asleep and wake up at the desired time is a common symptom of all CRSWDs. [ABSTRACT FROM AUTHOR]

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s. [ABSTRACT FROM AUTHOR]

Study Objectives: Catathrenia has been classified as a sleep-related breathing disorder variant in the third edition of the International Classification of Sleep Disorders , but its validity remains unverified. We analyzed the clinical descriptive variables and polysomnographic findings of catathrenia and discussed the similarities and differences to those of obstructive sleep apnea (OSA), non-REM parasomnias, and sleep bruxism (SB)., Methods: A retrospective analysis was conducted on 47 patients diagnosed with nocturnal groaning through polysomnography. We examined sex, body mass index, age at symptom onset, weekly symptom frequency, and presence/absence of comorbidities, including OSA, periodic limb movement disorder, non-REM parasomnia, and SB. The groaning event (GE) index was calculated according to sleep position and sleep stage., Results: The distribution of patients with catathrenia did not show sex difference (male/female = 20:27), body mass index was 20.6 ± 3.0 kg/m 2 , and age of onset was 18.2 ± 7.4 years. The GE index was higher in stages N1 and R than in stage N3 and in the supine position than in the lateral position. There were no cases complicated with non-REM parasomnia, but the complication of SB was observed in 30% of the participants, and SB events appeared immediately before or during the interictal period of the GE episodes in these cases., Conclusions: Given the clinical background, posture- and sleep stage-dependent appearance of GEs, and the relatively high complication rate of SB, catathrenia pathogenesis may be heterogeneous or comprise elements of different sleep disorders., (© 2024 American Academy of Sleep Medicine.)

Study Objectives: Arousal burden (AB) is defined as the cumulative duration of arousals during sleep divided by the total sleep time. However, in-depth analysis of AB related to sleep characteristics is lacking. Based on previous studies addressing the arousal index (ArI), we hypothesized that the AB would peak in the supine sleeping position and during non-rapid eye movement stage 1 (N1) and show high variability between scorers., Methods: Nine expert scorers analyzed polysomnography recordings of 50 participants, the majority with an increased risk for obstructive sleep apnea. AB was calculated in different sleeping positions and sleep stages. A generalized estimating equation was utilized to test the association between AB and sleeping positions, sleep stages, and scorers. The correlation between AB and ArI was tested with Spearman's rank-order correlation., Results: AB significantly differed between sleeping positions ( p <0.001). The median AB in the supine sleeping position was 47-62% higher than in the left and right position. The AB significantly differed between the sleep stages ( p <0.001); the median AB was more than 200% higher during N1 than during other sleep stages. In addition, the AB differed significantly between scorers ( p <0.001) and correlated strongly with ArI ( r =0.935, p <0.001)., Conclusions: AB depends on the sleeping position, sleep stage, and scorer as hypothesized. AB behaved similarly as the ArI, but the high variability in the ABs between scorers indicates a potential limitation caused by subjective manual scoring. Thus, the development of more accurate techniques for scoring arousals is required before AB can be reliably utilized., (© 2024 American Academy of Sleep Medicine.)

Study Objectives: Despite frequent sleep disruption in the paediatric intensive care unit (PICU), bedside sleep monitoring in real-time is currently not available. Supervised machine learning (ML) applied to electrocardiography (ECG) data may provide a solution, since cardiovascular dynamics are directly modulated by the autonomic nervous system (ANS) during sleep., Methods: Retrospective study using hospital-based polysomnography (PSG) recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years and 13-18 years. Features were derived in time, frequency and non-linear domain from pre-processed ECG data. Sleep classification models were developed for two, three, four and five state using logistic regression (LR), random forest (RF) and XGBoost (XGB) classifiers during five-fold nested cross-validation. Models were additionally validated across age categories., Results: A total of 90 non-critically ill children were included with a median (Q1, Q3) recording length of 549.0 (494.8, 601.3) minutes. The three models obtained AUROC 0.72 - 0.78 with minimal variation across classifiers and age categories. Balanced accuracies were 0.70 - 0.72, 0.59 - 0.61, 0.50 - 0.51 and 0.41 - 0.42 for two, three, four and five state. Generally, the XGB model obtained the highest balanced accuracy (p < 0.05), except for five state where LR excelled (p = 0.67)., Conclusions: ECG-based ML models are a promising and non-invasive method for automated sleep classification directly at the bedside of non-critically ill children aged 6 months to 18 years. Models obtained moderate-to-good performance for two and three state classification., (© 2024 American Academy of Sleep Medicine.)

Sleep is important part for human health and quality of life in the daily routine basis. However, numerous individuals face sleep problems due to rapid changes occurred in both social and professional lifestyles. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. Machine learning methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main objective of this study is to propose a high-effective and high-accuracy based multiple sleep staging classification model based on single-channel electroencephalogram (EEG) signals using machine learning (ML) model. The proposed automated sleep staging system followed four basic stages: signal preprocessing, feature extraction and screening, classification algorithms, and performance evaluation. In this research work, a novel method is applied for signal preprocessing, feature screening and classification models. In signal preprocessing we obtain the wiener filter techniques for removing the different types of artifacts from input sleep recordings. In feature extraction, we obtain a total of 28 features based on both time and frequency domain features and non-linear features. The relevant features are screened through ReliefF weight feature selection algorithm, and eliminating the redundant features using Pearson correlation coefficients. The important contribution of this research work is establishes two layers an ensembling learning stacking model for classifying the multiple sleep stages. Three different subgroups of ISRUC-Sleep (SG-I/SG-II/SG-III) subjects sleep recordings having different health condition obtained for our proposed experimental work. Comparing with the recent contributions on sleep staging performances using single-channel EEG signals, it has found that our proposed ensemble learning stacking model was reported excellent in sleep staging classification accuracy performance for five sleep stages classification task (CT-5). The overall classification accuracy reported as 99.34%, 90.8%, and 98.50% for SG-I, SG-II, and SG-III dataset, respectively. [ABSTRACT FROM AUTHOR]

Delta activity on electroencephalogram (EEG) is considered a biomarker of homeostatic sleep drive. Delta power is often associated with sleep duration and intensity. Here, we reviewed the literature to explore how sleep quality was influenced by changes in delta power. However, we found that both the decrease and increase in delta power could indicate a higher sleep quality due to the various factors below. First, the differences in changes in delta power in patients whose sleep quality is lower than that of the healthy controls may be related to the different diseases they suffered from. We found that the patients mainly suffered from borderline personality disorder, and Rett syndrome may have a higher delta power than healthy individuals. Meanwhile, patients who are affected by Asperger syndrome, respiratory failure, chronic fatigue, and post-traumatic stress disorder have lower delta power. Second, if the insomnia patients received the therapy, the difference may be caused by the treatment method. Cognitive or music therapy shows that a better therapeutic effect is associated with decreased delta power, whereas in drug treatment, there is an opposite change in delta power. Last, for healthy people, the difference in delta change may be related to sleep stages. The higher sleep quality is associated with increased delta power during the NREM period, whereas a deceased delta change accompanies higher sleep quality during the REM period. Our work summarizes the effect of changes in delta power on sleep quality and may positively impact the monitoring and intervention of sleep quality. [ABSTRACT FROM AUTHOR]

Delta activity on electroencephalogram (EEG) is considered a biomarker of homeostatic sleep drive. Delta power is often associated with sleep duration and intensity. Here, we reviewed the literature to explore how sleep quality was influenced by changes in delta power. However, we found that both the decrease and increase in delta power could indicate a higher sleep quality due to the various factors below. First, the differences in changes in delta power in patients whose sleep quality is lower than that of the healthy controls may be related to the different diseases they suffered from. We found that the patients mainly suffered from borderline personality disorder, and Rett syndrome may have a higher delta power than healthy individuals. Meanwhile, patients who are affected by Asperger syndrome, respiratory failure, chronic fatigue, and post-traumatic stress disorder have lower delta power. Second, if the insomnia patients received the therapy, the difference may be caused by the treatment method. Cognitive or music therapy shows that a better therapeutic effect is associated with decreased delta power, whereas in drug treatment, there is an opposite change in delta power. Last, for healthy people, the difference in delta change may be related to sleep stages. The higher sleep quality is associated with increased delta power during the NREM period, whereas a deceased delta change accompanies higher sleep quality during the REM period. Our work summarizes the effect of changes in delta power on sleep quality and may positively impact the monitoring and intervention of sleep quality.

Purpose: The goal of this study on Spinal Cord Injury (SCI) patients with cervical or thoracic lesion was to assess whether disturbances of ANS control, according to location, might differently affect vagal and sympatho-vagal markers during sleep and orthostatic challenge. We analyzed with linear and nonlinear techniques beat-by-beat RR and arterial pressure (and respiration) variability signals, extracted from a polysomnographic study and a rest–tilt test. We considered spontaneous or induced sympathetic excitation, as obtained shifting from non-REM to REM sleep or from rest to passive tilt. We obtained evidence of ANS cardiac (dys)regulation, of greater importance for gradually proximal location (i.e., cervical) SCI, compatible with a progressive loss of modulatory role of sympathetic afferents to the spinal cord. Furthermore, in accordance with the dual, vagal and sympathetic bidirectional innervation, the results suggest that vagally mediated negative feedback baroreflexes were substantially maintained in all cases. Conversely, the LF and HF balance (expressed specifically by normalized units) appeared to be negatively affected by SCI, particularly in the case of cervical lesion (group p = 0.006, interaction p = 0.011). Multivariate analysis of cardiovascular variability may be a convenient technique to assess autonomic responsiveness and alteration of functionality in patients with SCI addressing selectively vagal or sympathetic alterations and injury location. This contention requires confirmatory studies with a larger population.

Summary: Obstructive sleep apnea (OSA) is a highly prevalent condition, resulting in recurrent hypoxic events, sleep arousal, and daytime sleepiness. Patients with OSA are at an increased risk of cardiovascular morbidity and mortality. The mechanisms underlying the development of cardiovascular disease in OSA are multifactorial and cause a cascade of events. The primary contributing factor is sympathetic overactivity. Heart rate variability (HRV) can be used to evaluate shifts in the autonomic nervous system, during sleep and in response to treatment in patients with OSA. Newer technologies are aimed at improving HRV analysis to accelerate processing time, improve the diagnosis of OSA, and detection of cardiovascular risk. The present review will present contemporary understandings and uses for HRV, specifically in the realms of physiology, technology, and clinical management. [ABSTRACT FROM AUTHOR]

Monitoring single-channel EEG is a promising home-based approach for insomnia identification. Currently, many automatic sleep stage scoring approaches based on single-channel EEG have been developed, whereas few studies research on automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations. In this paper, we propose a one-dimensional convolutional neural network (1D-CNN) model for automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages EEG epochs. Single-channel EEG on 9 insomnia patients and 9 healthy subjects was used in this study. We constructed 4 subdatasets from EEG epochs based on the sleep stage annotations: All sleep stage dataset (ALL-DS), REM sleep stage dataset (REM-DS), light sleep stage dataset (LSS-DS), and SWS sleep stage dataset (SWS-DS). Subsequently, 4 subdatasets were fed into our 1D-CNN. We conducted experiments under intra-patient and inter-patient paradigms, respectively. Our experiments demonstrated that our 1D-CNN leveraging 3 subdatasets composed of REM, LSS and SWS epochs, respectively, achieved higher average accuracies in comparison with baseline methods under both intra-patient and inter-patient paradigms. The experimental results also indicated that amongst all the sleep stages, 1D-CNN leveraging REM and SWS epochs exhibited the best insomnia identification average accuracies in intra-patient paradigm, which are 98.98% and 99.16% respectively, whereas no statistically significant difference was found in inter-patient paradigm. For automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, 1D-CNN model introduced in this paper could achieve superior performance than traditional methods.

In this paper, an automatic sleep scoring system based on electroencephalogram (EEG) and electrooculogram (EOG) signals was proposed for sleep stage classification and depression detection. Our automatic sleep stage classification method contained preprocessing based on independent component analysis, feature extraction including spectral features, spectral edge frequency features, absolute spectral power, statistical features, Hjorth features, maximum-minimum distance and energy features, and a modified ReliefF feature selection. Finally, a support vector machine was employed to classify four states (awake, light sleep [LS], slow-wave sleep [SWS] and rapid eye movement [REM]). The overall accuracy of the Sleep-EDF database reached 90.10 ± 2.68% with a kappa coefficient of 0.87 ± 0.04. Furthermore, a depression recognition method was developed to distinguish the patients with depression from healthy subjects. Specifically, according to the differences in sleep patterns between the two groups, REM latency, sleep latency, LS proportion, SWS proportion, sleep maintenance and arousal times were employed in this study. Sleep data from 12 healthy individuals and 19 patients with depression were applied to the system. The accuracy of the recognition results reached 95.24%, thus verifying the feasibility of our approach. [ABSTRACT FROM AUTHOR]