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Study Objective: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Declaration of Competing interest There is no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

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.)

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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.)

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., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tanaka, Baba-Mori, Yonaga, Mochizuki, Igarashi, Ando, Kohda, Ito, Soejima and Sakurai.)

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.

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.

Study Objectives: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification., Methods: Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography 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. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels., Results: In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively., Conclusions: We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification., Citation: van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med . 2024;20(3):389-397., (© 2024 American Academy of Sleep Medicine.)

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., (© 2023 European Sleep Research Society.)

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., (© 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Wu, Pan and Wang.)

Background: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography., Objective: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment., Methods: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography., Results: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal., Conclusions: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep., (©Taeyoung Lee, Younghoon Cho, Kwang Su Cha, Jinhwan Jung, Jungim Cho, Hyunggug Kim, Daewoo Kim, Joonki Hong, Dongheon Lee, Moonsik Keum, Clete A Kushida, In-Young Yoon, Jeong-Whun Kim. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 02.11.2023.)

Objective . The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be very rare, whereupon the DES fails to measure the amplitude fluctuations. Approach . To address this problem, we develop a novel threshold DES (tDES) that measures the distribution of differential states within a threshold. To evaluate the proposed tDES, we first analyze five sets of synthetic signals generated in different frequency bands. We then analyze sleep electroencephalography (EEG) datasets taken from the public PhysioNet. Main results . Synthetic signals and detrend-filtered sleep EEGs have no neighboring equal values; however, tDES can effectively measure the amplitude fluctuations within these data. The tDES of EEG data increases significantly as the sleep stage increases, even with datasets covering very short periods, indicating decreased amplitude fluctuations in sleep EEGs. Generally speaking, the presence of more low-frequency components in a physiological series reflects smaller amplitude fluctuations and larger DES. Significance . The tDES provides a reliable computing method for quantifying amplitude fluctuations, exhibiting the characteristics of conceptual simplicity and computational robustness. Our findings broaden the application of quantitative amplitude fluctuations and contribute to the classification of sleep stages based on EEG data., (© 2023 Institute of Physics and Engineering in Medicine.)

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans., (Published by Elsevier Inc.)

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.

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.

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. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wenjian, Qian, Jun and Zhikun.)

Objective: 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., Methods: 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., Results: 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., Conclusions: 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., Competing Interests: Declaration of competing interest All authors declare no conflict of interest and no financial support., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.

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 sub-periods is 30, the α (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., Competing Interests: The authors declare that the research is conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Xu, Zhao, Liu, Yu, Wang, Lou, Shi, Liu, Gao, Yang, Zhang, Lu, Tang and Leng.)

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.

Introduction: Accurate 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., Method: In 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 Discussion: The 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ling, Luyuan, Xinxin and Bingliang.)

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., Competing Interests: Masafumi and Koki works for Shimadzu Corporation, but there is no conflict of interest., (© 2022 Kamon, Okada, Furuta and Yosida.)

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., Competing Interests: Declaration of competing interest The authors declared that there was no any potential conflict of interest in this work., (Copyright © 2021. Published by Elsevier B.V.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhuang, Dai, Zhou and Sun.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Long, Ding, Wang, Yu, Lu and Yao.)

Purpose: 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 (ĸ). 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% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=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., Competing Interests: The authors report no conflicts of interest in this work., (© 2021 Wang et al.)

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer's and Parkinson's diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.

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., (© 2021 European Sleep Research Society.)

Background: Sleep quality typically decreases after menopause, but the underlying mechanisms are poorly understood. Concentrations of melatonin are lower and its secretion profiles different before and after menopause. However, whether and how melatonin and sleep architecture are associated in women of different reproductive states have not been examined to date., Methods: Overnight serum melatonin samples were taken from 17 perimenopausal and 18 postmenopausal healthy women. Sleep quality was measured with all-night polysomnography recordings., Results: Melatonin concentrations tended to be the lowest during NREM sleep, and were associated with higher odds of transitions from wake to NREM sleep. The curves of predicted overnight melatonin values from linear mixed models varied according to sleep phases (NREM, REM, Wake) in perimenopausal, but not in postmenopausal women. In perimenopause higher melatonin area under curve (AUC) correlated with higher slow-wave activity (p = 0.043), and higher minimum concentrations with shorter slow-wave sleep (SWS) latency (p = 0.029). In postmenopause higher mean and maximum melatonin concentrations and AUC correlated with lower SWS percentage (p = 0.044, p = 0.029, p = 0.032), and higher mean (p = 0.032), maximum (p = 0.032) and minimum (p = 0.037) concentrations with more awakenings from REM sleep. In the age- and BMI- adjusted regression models, the association between higher maximum (p = 0.046) melatonin concentration and lower SWS percentage remained., Conclusions: The relationship between melatonin and sleep architecture differed in perimenopausal and postmenopausal women. After menopause, high melatonin concentrations were associated with worse sleep. Whether these different patterns are related to aging of the reproductive system, and to decrease in menopausal sleep quality, remains to be elucidated., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Attention-deficit hyperactivity disorder (ADHD) is associated with disrupted sleep and circadian rhythm. Medication for ADHD may have side effects aggravating sleep-disturbances, however beneficial effects on ADHD may contribute to improve sleep., Aims: This pilot study aims to examine outcomes of first time stimulant treatment on objective and subjective sleep characteristics, and psychiatric symptoms, in adult ADHD patients with pretreatment sleep problems, but without any primary sleep disorder., Methods: In total, 9 previously unmedicated adult ADHD subjects who reported pretreatment sleep problems, completed polysomnography (PSG) and questionnaires on subjective sleep disturbances and psychiatric symptoms. Data was collected before and after 6 weeks on first time medication with immediate-release methylphenidate (MPH-IR), mean daily dose 43 mg., Results: Subjects on-medication showed an increased percentage of Stage 2 sleep compared to their non-treated baseline (46.6% versus 55.2%, p  = .011). Otherwise, there were no significant changes in PSG variables. There were no firm changes in daytime sleepiness or symptoms of sleep disturbances., Conclusions: These findings should be interpreted cautiously given the open-label design and small sample size, and should be examined in larger studies with more rigorous study designs.

Introduction: Sleep is essential for performing cognitive function in humans. We have hypothesized that sleep fragmentation compared to sleep efficiency may have a negative impact on the working memory., Material and Methods: Twenty-eight healthy adults (18 males and 10 females; mean age 27.8±15.5 years) were enrolled in this study. We measured the total sleep time (TST), sleep efficiency, %stage wakefulness (W), %stage rapid eye movement (REM), %stage N1, %stage N2, %stage N3, wake after sleep onset (WASO), and arousal index using polysomnography. Working memory, executive function, and sustained attention of three domains of cognitive function were evaluated with the number of back task (N-back task), Wisconsin card sorting test (WCST), and continuous performance test-identical pairs (CPT-IP), respectively., Results: The percentage of correct answers on the 2-back task was significantly correlated with %stage REM, %stage N1, and %stage N2 (%stage REM: r =0.505, p =0.006; %stage N1: r =-0.637, p <0.001; %stage N2: r =0.670, p <0.001), and multiple regression analysis including the stepwise forward selection method revealed that %stage N2 was the most significant factor (%stage N2: β =0.670, p <0.001). The percentage of correct answers on the 2-back task was also significantly correlated with TST, sleep efficiency, WASO, and arousal index (TST: r =0.492, p =0.008; sleep efficiency: r =0.622, p <0.001; WASO: r =-0.721, p <0.001; arousal index: r =-0.656, p <0.001), and WASO was the significant factor ( β =-2.086, p =0.007). The WCST category achievement and CPT-IP d -prime score were correlated with none of the sleep variables., Conclusion: Increased WASO and a decrease in %stage N2 were associated with worse working memory.

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet's cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen's Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen's Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects-the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics . To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chung, Hu, Lo and Wu.)

Background and Objectives: In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages., Methods: EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals., Results: Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems)., Conclusions: The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

We examined the relationships between sleep and inflammatory biomarkers during late pregnancy. Seventy-four women underwent an overnight sleep assessment by polysomnography. Blood samples were collected before bedtime and again within 1 h upon awakening to measure C-reactive protein (CRP), interleukin (IL)-6, and IL-6 soluble receptor. Sleep parameters included variables characterizing sleep architecture and sleep continuity. The participants were 32.2 (SD = 4.1) years old, and the average gestational age was 32.8 (3.5) weeks. Controlling for covariates, evening CRP was negatively associated with N3 sleep (β = -0.30, P = 0.010). N3 sleep was also negatively associated with morning CRP (β = -0.26, P = 0.036), with a higher percentage of N3 sleep associated with a lower level of morning CRP. Contrarily, there was a tendency for a positive association between stage N2 sleep and morning CRP (β = 0.23, P = 0.065). Stage N1 sleep was associated with morning IL-6 (β = 0.28, P = 0.021), with a higher percentage of N1 sleep associated with a higher morning IL-6. No significant associations were found between morning inflammatory biomarkers and sleep continuity parameters. In conclusion, increased light sleep was associated with increased inflammatory biomarkers, whereas more deep sleep was associated with decreased inflammatory biomarkers. These findings further support the interactions between sleep and the immune system during late pregnancy., (© 2020 New York Academy of Sciences.)

It remains unclear whether neurobiological dysfunction observed in major depressive disorder (MDD) and insomnia is an expression of common or independent bases. The present investigation sought to explore differences in heart rate variability (HRV)-a widely utilized biomarker of neurobiological functioning-among individuals with MDD, insomnia, and healthy controls, while awake and during distinct sleep stages (REM, N2), with the goal of improving our understanding of shared neurobiological factors in depression and insomnia. Participants were 73 adults who underwent home polysomnography. All N2 and REM sleep epochs with a duration greater than or equal to 5 min were identified for HRV analysis. Additionally, a single waking epoch was defined for each participant. From waking to N2 sleep, and waking to REM sleep, changes in HRV indices indicated participants experienced reductions in sympathetic arousal and increases in parasympathetic arousal. Contrary to hypotheses, however, no between group differences were observed in HRV. Though the present findings do not support the hypotheses of a shard neurobiological pathway between MDD and insomnia, more work is warranted to advance our understanding of the neurobiological bases of these common, debilitating, and frequently co-occurring psychiatric conditions, to improve early detection and identify novel intervention targets for these disorders., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

Background and Objective: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, the state-of-the-art of such methods are quite complex. Using a simple CNN architecture to classify sleep stages is important for portable sleep devices. In addition, employing CNNs to learn rich and diverse representations remains a challenge. Therefore, we propose a novel CNN model for sleep stage classification., Methods: Generally, EEG signals are better described in the frequency domain; thus, we convert EEG data to a time-frequency representation via Hilbert-Huang transform. To learn rich and effective feature representations, we propose an orthogonal convolutional neural network (OCNN). First, we construct an orthogonal initialization of weights. Second, to avoid destroying the orthogonality of the weights in the training process, orthogonality regularizations are proposed to maintain the orthogonality of weights. Simultaneously, a squeeze-and-excitation (SE) block is employed to perform feature recalibration across different channels., Results: The proposed method achieved a total classification accuracy of 88.4% and 87.6% on two public datasets, respectively. The classification performances of different convolutional neural networks models were compared to that of the proposed method. The experiment results demonstrated that the proposed method is effective for sleep stage classification., Conclusions: Experiment results indicate that the proposed OCNN can learn rich and diverse feature representations from time-frequency images of EEG data, which is important for deep learning. In addition, the proposed orthogonality regularization is simple and can be easily adapted to other architectures., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Although positive airway pressure (PAP) therapy is effective for treating obstructive sleep apnea (OSA), some patients with severe OSA are intolerable to this treatment, which may lead to an increase in the mortality and morbidity of cardiovascular diseases. We investigated the relationship between heart rate variability (HRV) and sleep parameters during natural sleep and treatment of patients with OSA., Materials and Methods: This was the cross-sectional observation study. Patients were 17 males with severe OSA who were unable to accept continuous PAP. Standard polysomnography was performed for two consecutive nights, i.e., during natural sleep and following night with bilevel PAP (BiPAP) treatment. Time-dependent responses of the amplitudes of low frequency (LF), very low frequency (VLF), and high frequency components of HRV were assessed with the technique of complex demodulation., Results: Apnea-hypopnea index, oxygen desaturation time, and percentage of stage 1 sleep were significantly reduced, whereas the percentages of rapid eye movement and stages 3 + 4 sleep were increased, by BiPAP treatment. Therapy also reduced the amplitudes of VLF and LF components of HRV. Difference in amplitudes of VLF during natural sleep and treatment with BiPAP was significantly correlated with difference in percentages of stage 1 and stages 3 + 4 sleep., Conclusion: Therapy-induced amelioration of OSA and sleep quality was accompanied by decrease in the amplitudes of VLF components of HRV. The VLF component may thus reflect physiological changes in both autonomic activity and sleep structure and serve as an objective marker for therapeutic efficacy in patients with severe OSA., Competing Interests: There are no conflicts of interest., (Copyright: © 2019 Journal of Research in Medical Sciences.)

The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroencephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classification algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k-Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Graphical abstract.

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal., Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals., Results: Our review shows that all of these signals contain information for sleep stage scoring., Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and five parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these five parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed sufficient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device., Competing Interests: We declares that we have no conflict of interest.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Informed consent was obtained from all individual participants included in the study.

Objectives: In pediatric obstructive sleep apnea (OSA), the relationship between rapid eye movement sleep and upper airway collapse, and between sleep position and airway dimensions are well known. However, the interrelations between these factors and the obstructive apnea hypopnea index (O-AHI) have not been thoroughly investigated., Methods: A retrospective study including 100 children who underwent adenotonsillectomy between March 2010 and July 2017. Total O-AHI was divided into four subcategories by sleep stage and position., Results: Preoperatively 14 of 47 mild cases of OSA (1 ≤ total O-AHI) and 17 of 18 moderate (5 ≤ total O-AHI) had time showing severe apnea (10 ≤ subcategorized O-AHI). Twenty-two of 24 severe cases (10 ≤ total O-AHI) exhibited very severe apnea (30 ≤ subcategorized O-AHI). All 11 very severe cases (30 ≤ total O-AHI) experienced more than 50 apnea events per hour in at least one of the O-AHI subcategories. After surgery, 23 of 70 cases classified as completely resolved (total O-AHI < 1) still had mild apnea in the O-AHI subcategories, and six of 13 cases who continued to experience apnea events had moderate-to-severe apnea. Seventeen cases worsened in the O-AHI subcategories, and total O-AHI deteriorated in two cases of the 17. The amount of REM sleep and use of the supine position increased significantly postoperatively in the quartile groups with the lowest baseline values (p < 0.0001)., Conclusions: When an unexpected AHI value is encountered, the O-AHI subcategories may be informative regarding the indications for surgery and evaluating the efficacy thereof., (Copyright © 2019. Published by Elsevier B.V.)

The study of sleep has continued to garner increased attention. However, most studies assume stationarity of sleep electroencephalogram (EEG) signals, whereas they are typically nonlinear and nonstationary. Little work has focused on the time irreversibility of sleep EEG signals. Hence, the aim of this work is to reveal the temporally irreversible structures of rapid-eye-movement (REM) and non-REM sleep using a visibility algorithm, which is robust to nonstationarity and finite-size effect. Results show that the temporal structure of non-REM sleep is more irreversible than that of REM sleep. The degree of irreversibility is highest in slow-wave sleep. Moreover, statistical analysis suggests that aging is the major factor that affects the irreversibility of sleep signals, while gender and body mass index contribute insignificantly. The dominant role of slow oscillations on the irreversible structures of the sleep signals is also indicated., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest.

Purpose: It was shown in a previous cohort study that men with internal carotid artery (ICA) plaque, defined as focal wall thickness of ≥ 1.5 mm, had a threefold higher risk of stroke than those without plaque. We examined the relationship between arousal indices and sleep stages in patients with obstructive sleep apnea syndrome (OSAS) and carotid atherosclerosis., Methods: Carotid atherosclerosis severity was evaluated using the maximal carotid wall intima-media thickness of the ICA (ICA-maxIMT) and plaque in 83 patients with OSAS., Results: The ICA-maxIMT values were positively correlated with the apnea hypopnea index (AHI) (ρ = 0.294, P = 0.007), arousal index (ρ = 0.289, P = 0.008), oxygen desaturation index (ρ = 0.298, P = 0.006), percentage of visually scored total sleep time spent in nocturnal oxygen saturation < 90% (SpO 2 < 90%) (ρ = 0.246, P = 0.025), and the percentage of visually scored total sleep time spent in non-REM sleep stage 1 (ρ = 0.326, P = 0.003) and were negatively correlated with the percentage of visually scored total sleep time spent in non-REM sleep stages 2 and 3. Arousal index, diabetes mellitus, and age were found to be independent predictors of ICA plaque presence (OR 1.052, P = 0.003; OR 8.705, P = 0.026; OR 1.064, P = 0.023, respectively)., Conclusions: Several PSG variables that are indicative of sleep fragmentation, sleep disordered breathing, and poor sleep quality correlated with the occurrence of atherosclerosis, but total arousal index was the only independent predictive factor.