Proposed supplements and amendments to 'A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects', the Rechtschaffen & Kales (1968) standard

Can People Sleep Too Much? Effects of Extended Sleep Opportunity on Sleep Duration and Timing

Many people are concerned about whether they are getting "enough" sleep, and if they can "sleep too much." These concerns can be approached scientifically using experiments probing long-term (i.e., multi-night) sleep homeostatic processes, since homeostatic processes move the system toward its physiological setpoint (i.e., between "not enough" and "too much"). We analyzed sleep data from two human studies with sleep opportunities much longer than people usually stay in bed (i.e., conditions in which sleep homeostatic responses could be documented): sleep opportunities were 14-16 h per day for 3-28 days. Across the nights of the extended sleep opportunities, total sleep duration, Rapid Eye Movement (REM) sleep duration and non-REM sleep durations decreased and sleep latency increased. Multiple nights were required to reach approximately steady-state values. These results suggest a multi-day homeostatic sleep process responding to self-selected insufficient sleep duration prior to the study. Once steady state-values were reached, there were large night-to-night variations in total sleep time and other sleep metrics. Our results therefore answer these concerns about sleep amount and are important for understanding the basic physiology of sleep and for two sleep-related topics: (i) the inter-individual and intra-individual variability are relevant to understanding "normal" sleep patterns and for people with insomnia and (ii) the multiple nights of sleep required for recovery from insufficient sleep from self-selected sleep loss is important for public health and other efforts for reducing the adverse effects of sleep loss on multiple areas of physiology.

Coregistration of magnetic resonance spectroscopy and polysomnography for sleep analysis in human subjects

We developed a protocol for simultaneous magnetic resonance spectroscopy (MRS) and polysomnography (PSG) recordings while subjects are in sleep. The approach is useful to estimate plasticity-stability balances by measuring neurochemical changes in the brain during sleep. We detail the steps needed to minimize artifacts in PSG recordings and the setup and coregistration of MRS data to sleep stages. We also describe useful information for various types of electroencephalogram (EEG) experiments in magnetic resonance imaging (MRI) environments. For complete details on the use and execution of this protocol, please refer to Tamaki et al. (2020b).

The Impact of Work Stress on Job Satisfaction and Sleep Quality for Couriers in China: The Role of Psychological Capital

Objective: Work stress is one of the urgent public health problems, which has aroused wide attention. In addition, work stress also has a negative impact on the development of enterprises. This study has three purposes: (1) to understand the current status of working stress among couriers, (2) to examine the association between work stress, job satisfaction and sleep quality of Chinese couriers, and (3) to verify the mediating role of psychological capital. Methods: A cross-sectional survey was conducted among 3000 couriers in Beijing of China from January to December 2019 using cluster stratified random sampling. Descriptive statistical analysis was used to describe demographic characteristics of respondents. Pearson correlation analysis was used to analyze the correlation among work stress, job satisfaction, sleep quality, and psychological capital. The mediating analysis was calculated role of psychological capital. Results: A total of 2831 participants were included in this study. The average score of work stress was 2.49 ± 0.76. Work stress was negatively correlated with job satisfaction (r = -0.266, P < 0.01), sleep quality (r = -0.142, P < 0.01), and psychological capital (r = -0.268, P < 0.01), respectively. The direct effect of work stress on job satisfaction is -0.223, and the total effect of work stress on job satisfaction is -0.267. The a*b*c' is positive, indicating the mediating role psychological capital has in explaining the relationship between couriers' work stress and job satisfaction. Moreover, psychological capital plays a mediating role between work stress and sleep quality among couriers. Conclusion: Couriers face certain work stress and should be paid attention to and psychological capital can effectively alleviate the work stress of couriers, so measures can be taken to improve it, promoting healthy development of employees and organizations.

Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency

In this study, we analyze the effect of a recliner chair with rocking motions on sleep quality of naps using automated sleep scoring and spindle detection models. The quality of sleep corresponding to the two rocking motions was measured quantitatively and qualitatively. For the quantitative evaluation, we conducted a sleep parameter analysis based on the results of the estimated sleep stages obtained on the brainwave and spindle estimation, and a sleep survey assessment from the participants was analyzed for the qualitative evaluation. The analysis showed that sleep in the recliner chair with rocking motions positively increased the duration of the spindles and deep sleep stage, resulting in improved sleep quality.

Hot flashes, insomnia, and the reproductive stages: a cross-sectional observation of women from the EPISONO study

STUDY OBJECTIVES

To investigate the association of hot flashes and insomnia in women in premenopause and postmenopause.

METHODS

The study was performed using data from the São Paulo Epidemiological Sleep Study. Women in premenopause were classified as having regular menstrual cycles, being anovulatory, or using hormonal contraceptives. Women in menopause were classified as being in perimenopause, early postmenopause, or late postmenopause. Women reporting frequent insomnia symptoms and relevant daytime complaints were classified as having insomnia disorder. Polysomnography alterations suggestive of insomnia were also identified.

RESULTS

The frequency of hot flashes was 42% among women in postmenopause (mainly in early postmenopause) and 9% among women in premenopause (mainly anovulatory; P < .01). Approximately 18.7% had insomnia disorder, 48% had isolated insomnia symptoms, and 32.4% had polysomnography alterations. Comparing women in menopause with those in premenopause, the diagnosis of insomnia was similar (premenopause: 18.9% vs menopause: 17.5%), but women in menopause had more frequent isolated insomnia symptoms (premenopause: 43.9% vs menopause: 55.9%; P = .02) and polysomnography correlates of insomnia (premenopause: 26.5% vs menopause: 42.6%; P < .01). Hot flashes were more frequent among women with insomnia disorders (25.5%) and with isolated insomnia symptoms (23.0%) when compared with good sleepers (12.6%) in the entire sample (P = .01). Among women in late menopause, the prevalence of hot flashes was higher in both women with insomnia disorders (42.1%) and with isolated insomnia symptoms (37.5%) when compared with women who were good sleepers (14.3%; P = .05).

CONCLUSIONS

Hot flashes are associated with insomnia and polysomnography alterations suggestive of insomnia. The prevalence of hot flashes among women with insomnia disorder is especially high among women in late postmenopause.

CITATION

Hachul H, Castro LS, Bezerra AG, et al. Hot flashes, insomnia, and the reproductive stages: a cross-sectional observation of women from the EPISONO study. J Clin Sleep Med. 2021;17(11):2257-2267.

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

Sleep Phases in Crayfish: Relationship Between Brain Electrical Activity and Autonomic Variables

In vertebrates like mammals and birds, two types of sleep have been identified: rapid eye movement and non-rapid eye movement sleep. Each one is associated with specific electroencephalogram patterns and is accompanied by variations in cardiac and respiratory frequencies. Sleep has been demonstrated only in a handful of invertebrates, and evidence for different sleep stages remains elusive. Previous results show that crayfish sleeps while lying on one side on the surface of the water, but it is not known if this animal has sleep phases. Heart rate and respiratory frequency are modified by diverse changes in the crayfish environment during wakefulness, and previously, we showed that variations in these variables are present during sleep despite that there are no autonomic anatomical structures described in this animal. Here, we conducted experiments to search for sleep phases in crayfish and the relationships between sleep and cardiorespiratory activity. We used the wavelet transform, grouping analysis with k-means clustering, and principal component analysis, to analyze brain and cardiorespiratory electrical activity. Our results show that (a) crayfish can sleep lying on one side or when it is motionless and (b) the depth of sleep (measured as the power of electroencephalographic activity) changes over time and is accompanied by oscillations in cardiorespiratory signal amplitude and power. Finally, we propose that in crayfish there are at least three phases of sleep.

Evaluation of movement and brain activity

Clinical neurophysiology studies can contribute important information about the physiology of human movement and the pathophysiology and diagnosis of different movement disorders. Some techniques can be accomplished in a routine clinical neurophysiology laboratory and others require some special equipment. This review, initiating a series of articles on this topic, focuses on the methods and techniques. The methods reviewed include EMG, EEG, MEG, evoked potentials, coherence, accelerometry, posturography (balance), gait, and sleep studies. Functional MRI (fMRI) is also reviewed as a physiological method that can be used independently or together with other methods. A few applications to patients with movement disorders are discussed as examples, but the detailed applications will be the subject of other articles.

Association of Pediatric Obstructive Sleep Apnea With Elevated Blood Pressure and Orthostatic Hypertension in Adolescence

Importance

Although pediatric guidelines have delineated updated thresholds for elevated blood pressure (eBP) in youth and adult guidelines have recognized obstructive sleep apnea (OSA) as an established risk factor for eBP, the relative association of pediatric OSA with adolescent eBP remains unexplored.

Objective

To assess the association of pediatric OSA with eBP and its orthostatic reactivity in adolescence.

Design, Setting, and Participants

At baseline of this population-based cohort study (Penn State Child Cohort) in 2000-2005, a random sample of 700 children aged 5 to 12 years from the general population was studied. A total of 421 participants (60.1%) were followed up in 2010-2013 after 7.4 years as adolescents (ages, 12-23 years). Data analyses were conducted from July 6 to October 29, 2020.

Main Outcomes and Measures

Outcomes were the apnea-hypopnea index (AHI) score, ascertained via polysomnography conducted in a laboratory; eBP measured in the seated position identified using guideline-recommended pediatric criteria; orthostatic hyperreactivity identified with BP assessed in the supine and standing positions; and visceral adipose tissue assessed via dual-energy x-ray absorptiometry.

Results

Among the 421 participants (mean [SD] age at follow-up, 16.5 [2.3] years), 227 (53.9%) were male and 92 (21.9%) were racial/ethnic minorities. A persistent AHI of 2 or more since childhood was longitudinally associated with adolescent eBP (odds ratio [OR], 2.9; 95% CI 1.1-7.5), while a remitted AHI of 2 or more was not (OR, 0.9; 95% CI 0.3-2.6). Adolescent OSA was associated with eBP in a dose-response manner; however, the association of an AHI of 2 to less than 5 among adolescents was nonsignificant (OR, 1.5; 95% CI, 0.9-2.6) and that of an AHI of 5 or more was approximately 2-fold (OR, 2.3; 95% CI, 1.1-4.9) after adjusting for visceral adipose tissue. An AHI of 5 or more (OR, 3.1; 95% CI, 1.2-8.5), but not between 2 and less than 5 (OR, 1.3; 95% CI, 0.6-3.0), was associated with orthostatic hyperreactivity among adolescents even after adjusting for visceral adipose tissue. Childhood OSA was not associated with adolescent eBP in female participants, while the risk of OSA and eBP was greater in male participants.

Conclusions and Relevance

The results of this cohort study suggest that childhood OSA is associated with adolescent hypertension only if it persists during this developmental period. Visceral adiposity explains a large extent of, but not all, the risk of hypertension associated with adolescent OSA, which is greater in male individuals.

The Regional EEG Pattern of the Sleep Onset Process in Older Adults

Healthy aging is characterized by macrostructural sleep changes and alterations of regional electroencephalographic (EEG) sleep features. However, the spatiotemporal EEG pattern of the wake-sleep transition has never been described in the elderly. The present study aimed to assess the topographical and temporal features of the EEG during the sleep onset (SO) in a group of 36 older participants (59-81 years). The topography of the 1 Hz bins' EEG power and the time course of the EEG frequency bands were assessed. Moreover, we compared the delta activity and delta/beta ratio between the older participants and a group of young adults. The results point to several peculiarities in the elderly: (a) the generalized post-SO power increase in the slowest frequencies did not include the 7 Hz bin; (b) the alpha power revealed a frequency-specific pattern of post-SO modifications; (c) the sigma activity exhibited only a slight post-SO increase, and its highest bins showed a frontotemporal power decrease. Older adults showed a generalized reduction of delta power and delta/beta ratio in both pre- and post-SO intervals compared to young adults. From a clinical standpoint, the regional EEG activity may represent a target for brain stimulation techniques to reduce SO latency and sleep fragmentation.

Effects of Obstructive Sleep Apnea Syndrome and Medical Comorbidities on Language Abilities

Objective: The primary objective of the present cross-sectional study is to evaluate the semantic language abilities of patients with Obstructive Sleep Apnea Syndrome (OSAS) compared to normative data. Secondary objectives are to examine the effects of OSAS comorbidities on language test performance. Method: 118 adult patients suffering from OSAS were assessed using standardized tests (Boston Naming Test, the Peabody Picture Vocabulary Test and the Verbal Fluency Test). Results: Compared to normative standards, the OSAS group (age and education adjusted mean) scored significantly lower on all tests (p < 0.01). The OSAS group also included a significantly higher percentage of persons scoring below the 5th percentile of the normative distribution on the four tests (p < 0.01). The Apnea/Hypopnea Index, O2 Desaturation index, SaO2 <85% (min) and SaO2 <75% (min) were significantly associated with language test scores (p < 0.05). Moreover, higher Apnea-Hypopnea Index score and night-time oxygen desaturation were associated with reduced phonemic and semantic fluency performance only among patients with a history of hypertension and hypercholesterolemia (p < 0.05). The moderating effect of diabetes and cardiovascular disease on the association between OSAS severity indices and test scores did not reach significance (p > 0.6). Conclusions: Results suggest that the severity of semantic language impairments in patients with OSAS is associated with the severity of the disease and intensified by common medical comorbidities (hypertension and hypercholesterolemia).

'SleepCycles' package for R - A free software tool for the detection of sleep cycles from sleep staging

The detection of NREM-REM sleep cycles in human sleep data (i.e., polysomnographically assessed sleep stages) enables fine-grained analyses of ultradian variations in sleep microstructure (e.g., sleep spindles, and arousals), or other amplitude- and frequency-specific electroencephalographic features during sleep. While many laboratories have software that is used internally, reproducibility requires the availability of open-source software. Therefore, we here introduce the ‘SleepCycles’ package for R, an open-source software package that identifies sleep cycles and their respective (non-) rapid eye movement ([N]REM) periods from sleep staging data. Additionally, each (N)REM period is subdivided into parts of equal duration (percentiles), which may be useful for further fine-grained analyses. The detection criteria used in the package are, with some adaptations, largely based on criteria originally proposed by Feinberg and Floyd (1979). The latest version of the package can be downloaded from the Comprehensive R Archives Network (CRAN).•

The package ‘SleepCycles’ for R allows to identify sleep cycles and their respective NREM and REM periods from sleep staging results.

•

Besides the cycle detection, NREM and REM periods are also split into parts of equal duration (percentiles) thereby allowing for a better temporal resolution across the night and comparisons of sleep cycles with different durations amongst different night recordings.

Automatic Sleep Staging Algorithm Based on Time Attention Mechanism

The most important part of sleep quality assessment is the automatic classification of sleep stages. Sleep staging is helpful in the diagnosis of sleep-related diseases. This study proposes an automatic sleep staging algorithm based on the time attention mechanism. Time-frequency and non-linear features are extracted from the physiological signals of six channels and then normalized. The time attention mechanism combined with the two-way bi-directional gated recurrent unit (GRU) was used to reduce computing resources and time costs, and the conditional random field (CRF) was used to obtain information between tags. After five-fold cross-validation on the Sleep-EDF dataset, the values of accuracy, WF1, and Kappa were 0.9218, 0.9177, and 0.8751, respectively. After five-fold cross-validation on the our own dataset, the values of accuracy, WF1, and Kappa were 0.9006, 0.8991, and 0.8664, respectively, which is better than the result of the latest algorithm. In the study of sleep staging, the recognition rate of the N1 stage was low, and the imbalance has always been a problem. Therefore, this study introduces a type of balancing strategy. By adopting the proposed strategy, SEN-N1 and ACC of 0.7 and 0.86, respectively, can be achieved. The experimental results show that compared to the latest method, the proposed model can achieve significantly better performance and significantly improve the recognition rate of the N1 period. The performance comparison of different channels shows that even when the EEG channel was not used, considerable accuracy can be obtained.

Inter-database validation of a deep learning approach for automatic sleep scoring

STUDY OBJECTIVES

Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance.

METHODS

A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios.

RESULTS

Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases.

CONCLUSIONS

Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.

Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome

OBJECTIVE/BACKGROUND

Recently, several tools for screening obstructive sleep apnea-hypopnea syndrome (OSAHS) have been devised with varied shortcomings. To overcome these drawbacks, we aimed to propose a self-estimation method using an explainable prediction model with easy-to-obtain variables and evaluate its performance for predicting OSAHS.

PATIENTS/METHODS

This retrospective, cross-sectional study selected significant easy-to-obtain variables from patients, suspected of having OSAHS by regression analysis, and fed these variables into the proposed explainable fuzzy neural network (EFNN), a back propagation neural network (BPNN) and a stepwise regression model to compare the screening performance for OSAHS.

RESULTS

Of the 300 participants, three easily available features, such as waist circumference, mean blood pressure (BP) at the end of polysomnography and the difference in systolic BP between the end and start of polysomnography, were obtained from regression analysis with a five-fold cross-validation scheme. Feeding these three variables into the prediction models showed that the average prediction differences for apnea-hypopnea index (AHI) when using the EFNN, BPNN, and regression model were respectively 1.5 ± 18.2, 3.5 ± 19.1 and 0.1 ± 19.3, indicating none of the tested methods had good efficacy to predict the AHI values. The performance as determined by the sensitivity + specificity-1 value for screening moderate-to-severe OSAHS of the EFNN, BPNN and regression model were respectively 0.440, 0.414 and 0.380.

CONCLUSIONS

When fed with easy-to-obtain physiological features, the understandable EFNN should be the preferred method to predict moderate-to-severe OSAHS.

Sex and Pubertal Differences in the Maturational Trajectories of Sleep Spindles in the Transition from Childhood to Adolescence: A Population-Based Study

Sleep spindles, bursts of electroencephalogram (EEG) activity in the σ-frequency (11–16 Hz) range, may be biomarkers of cortical development. Studies capturing the transition to adolescence are needed to delineate age-related, sex-related, and pubertal-related changes in sleep spindles at the population-level. We analyzed the sleep EEG of 572 subjects 6–21 years (48% female) and 332 subjects 5–12 years (46% female) followed-up at 12–22 years. From 6 to 21 years, spindle density (p quadratic = 0.019) and fast (12–16 Hz) spindle percent (p quadratic = 0.016) showed inverted U-shaped trajectories, with plateaus after 15 and 19 years, respectively. Spindle frequency increased (p linear < 0.001), while spindle power decreased (p linear < 0.001) from 6 to 21 years. The trajectories of spindle density, frequency, and fast spindle percent diverged between females and males, in whom density plateaued by 14 years, fast spindle percent by 16 years, and frequency by 18 years, while fast spindle percent and spindle frequency continued to increase until 21 years in females. Males experienced a longitudinal increase in spindle density 31% greater than females by 12–14 years (p = 0.006). Females experienced an increase in spindle frequency and fast spindle percent 2% and 41% greater, respectively, than males by 18–22 years (both p = 0.004), while males experienced a 14% greater decline in spindle power by 18–22 years (p = 0.018). Less mature adolescents (86% male) experienced a longitudinal increase in spindle density 36% greater than mature adolescents by 12–14 years (p = 0.002). Overall, males experience greater maturational changes in spindle density in the transition to adolescence, driven by later pubertal development, and sex differences become prominent in early adulthood when females have greater spindle power, frequency, and fast spindle percent.

Autonomic dysregulation and sleep homeostasis in insomnia

STUDY OBJECTIVES

Insomnia is common in older adults, and is associated with poor health, including cognitive impairment and cardio-metabolic disease. Although the mechanisms linking insomnia with these comorbidities remain unclear, age-related changes in sleep and autonomic nervous system (ANS) regulation might represent a shared mechanistic pathway. In this study, we assessed the relationship between ANS activity with indices of objective and subjective sleep quality in older adults with insomnia.

METHODS

Forty-three adults with chronic insomnia and 16 age-matched healthy sleeper controls were studied. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), objective sleep quality by electroencephalogram spectral components derived from polysomnography, and ANS activity by measuring 24-h plasma cortisol and norepinephrine (NE).

RESULTS

Sleep cycle analysis displayed lower slow oscillatory (SO: 0.5-1.25 Hz) activity in the first cycle in insomnia compared to controls. In insomnia, 24-h cortisol levels were higher and 24-h NE levels were lower than controls. In controls, but not in insomnia, there was a significant interaction between NE level during wake and SO activity levels across the sleep cycles, such that in controls but not in insomnia, NE level during wake was positively associated with the amount of SO activity in the first cycle. In insomnia, lower 24-h NE level and SO activity in the first sleep cycle were associated with poorer subjective sleep quality.

CONCLUSION

Dysregulation of autonomic activity may be an underlying mechanism that links objective and subjective measures of sleep quality in older adults with insomnia, and potentially contribute to adverse health outcomes.

The abrupt shift to slower frequencies after arousal from sleep in healthy young adults

STUDY OBJECTIVES

Post-arousal hypersynchrony (PAH) is an atypical arousal pattern in children's electroencephalography. PAH is an abrupt shift to slower frequencies in arousal-related responses, appearing as slow-wave clusters. In contrast, the prevalence of PAH in healthy young adults is still unknown. Here, we examined the prevalence and characteristics of PAH in healthy young participants.

METHODS

Thirty healthy young participants underwent one night of polysomnography (thirteen females, 22.8 ± 2.0 years [mean ± standard deviation]). We examined the prevalence of PAH as a function of sleep stage, sleep cycle, and time course (the first or the second half). The correlation between PAH and sleep variables was examined. The %N3 was compared for each sleep cycle and time course.

RESULTS

Twenty-eight out of 30 participants exhibited PAH (4.6 ± 4.8 times per night). PAH increased significantly during the first sleep cycle and the first half-sleep period. It was observed only in non-rapid eye movement (NREM) and not in REM sleep. The number of PAHs correlated with the number of arousals and arousal indices. The %N3 increased in the first half-sleep and the first sleep cycle.

CONCLUSIONS

PAH was relatively common in healthy young participants. Since PAH occurred in a state with a high prevalence of %N3, the first sleep cycle, or the first half-sleep, we suggest that PAH may be affected by the sleep homeostasis process. Since PAH occurred only in NREM sleep and correlated with arousal increment, it may have the function of suppressing NREM sleep's cortical arousal.

REM sleep without atonia and nocturnal body position in prediagnostic Parkinson's disease

BACKGROUND

Sleep disturbances are features of Parkinson's disease (PD), that can already occur before PD diagnosis. The most investigated prodromal PD sleep disorder is REM sleep behavior disorder (RBD). The relation between other polysomnographic (PSG) alterations and the prediagnostic stages of PD, however, is less clear.

METHODS

We performed a retrospective case-control study to characterize polysomnographic alterations in PD and prediagnostic PD. We included 63 PD subjects (33 subjects that underwent a video-PSG before PD diagnosis [13 with and 20 without RBD] and 30 subjects that underwent a PSG after PD diagnosis) and 30 controls. PSGs were analyzed for sleep stages, different RSWA variables, body position, arousals, periodic limb movements, and REM density.

RESULTS

Higher subscores of all RSWA variables were observed in subjects with PD and prediagnostic PD (with and without RBD). Total RSWA, tonic RSWA and chin RSWA severity were significant predictors for all PD and prediagnostic PD groups. Our study also shows a higher percentage of nocturnal supine body position in all PD and prediagnostic PD groups. Supine body position percentage is the highest in the PD group and has a positive correlation with time since diagnosis.

CONCLUSIONS

These findings suggest that increased total, tonic and chin RSWA as well as nocturnal supine body position are already present in prediagnostic PD, independently of RBD status. Prospective longitudinal studies are necessary to confirm the additional value of these PSG abnormalities as prodromal PD biomarkers.

Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients

Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

Effect of Sleep-Disordered Breathing During Rapid Eye Movement Sleep and Non-Rapid Eye Movement sleep on Acute Ischemic Stroke

OBJECTIVES

Sleep-disordered breathing adversely impacts stroke outcomes. We investigated whether sleep-disordered breathing during rapid eye movement sleep and non-rapid eye movement sleep differentially influenced stroke outcomes.

MATERIALS AND METHODS

Acute ischemic stroke patients who finished polysomnography within 14 days of stroke onset from April 2010 to August 2018 were reviewed. Patients were divided into four groups according to apnea-hypopnea index during rapid eye movement sleep and non-rapid eye movement sleep. The modified Rankin Scale was used to evaluate short-term outcome. During January and April 2019, another follow-up was performed for long-term outcomes, including stroke-specific quality-of-life scale, modified Rankin Scale, stroke recurrence and death.

RESULTS

Of 140 patients reviewed, 109 were finally recruited. Although patients with sleep-disordered breathing during non-rapid eye movement sleep only and with sleep-disordered breathing during both rapid eye movement sleep and non-rapid eye movement sleep had higher apnea-hypopnea indices and more disrupted sleep structures, short-term and long-term outcomes did not significantly different between four groups. In Logistic regression analysis, apnea-hypopnea index (p = 0.013, OR 1.023, 95%CI 1.005-1.042) was found independently associated with short-term outcome. Rapid eye movement sleep latency (p = 0.045, OR 0.994, 95%CI 0.987-1.000) was found independently associated with quality of life. Apnea-hypopnea indices during rapid eye movement sleep or non-rapid eye movement sleep were not significantly associated with short-term or long-term outcomes.

CONCLUSIONS

Apnea-hypopnea index is an independent risk factor of short-term outcome of acute ischemic stroke while sleep-disordered breathing during rapid eye movement sleep and non-rapid eye movement sleep do not affect stroke outcomes differently.

REM Sleep EEG Activity and Clinical Correlates in Adults With Autism

We tested the hypothesis of an atypical scalp distribution of electroencephalography (EEG) activity during Rapid Eye Movement (REM) sleep in young autistic adults. EEG spectral activity and ratios along the anteroposterior axis and across hemispheres were compared in 16 neurotypical (NT) young adults and 17 individuals with autism spectrum disorder (ASD). EEG spectral power was lower in the ASD group over the bilateral central and right parietal (beta activity) as well as bilateral occipital (beta, theta, and total activity) recording sites. The NT group displayed a significant posterior polarity of intra-hemispheric EEG activity while EEG activity was more evenly or anteriorly distributed in ASD participants. No significant inter-hemispheric EEG lateralization was found. Correlations between EEG distribution and ASD symptoms using the Autism Diagnostic Interview-Revised (ADI-R) showed that a higher posterior ratio was associated with a better ADI-R score on communication skills, whereas a higher anterior ratio was related to more restricted interests and repetitive behaviors. EEG activity thus appears to be atypically distributed over the scalp surface in young adults with autism during REM sleep within cerebral hemispheres, and this correlates with some ASD symptoms. These suggests the existence in autism of a common substrate between some of the symptoms of ASD and an atypical organization and/or functioning of the thalamo-cortical loop during REM sleep.

Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States

An expansion of the corticothalamic transfer function into eigenmodes and resonant poles is used to derive a simple formula for evoked response potentials (ERPs) in various states of arousal. The transfer function corresponds to the cortical response to an external stimulus, which encodes all the information and properties of the linear system. This approach links experimental observations of resonances and characteristic timescales in brain activity with physically based neural field theory (NFT). The present work greatly simplifies the formula of the analytical ERP, and separates its spatial part (eigenmodes) from the temporal part (poles). Within this framework, calculations involve contour integrations that yield an explicit expression for ERPs. The dominant global mode is considered explicitly in more detail to study how the ERP varies with time in this mode and to illustrate the method. For each arousal state in sleep and wake, the resonances of the system are determined and it is found that five poles are sufficient to study the main dynamics of the system in waking eyes-open and eyes-closed states. Similarly, it is shown that six poles suffice to reproduce ERPs in rapid-eye movement sleep, sleep state 1, and sleep state 2 states, whereas just four poles suffice to reproduce the dynamics in slow wave sleep. Thus, six poles are sufficient to preserve the main global ERP dynamics of the system for all states of arousal. These six poles correspond to the dominant resonances of the system at slow-wave, alpha, and beta frequencies. These results provide the basis for simplified analytic treatment of brain dynamics and link observations more closely to theory.

Trait Interindividual Differences in the Magnitude of Subjective Sleepiness from Sleep Inertia

In shift work settings and on-call operations, workers may be at risk of sleep inertia when called to action immediately after awakening from sleep. However, individuals may differ substantially in their susceptibility to sleep inertia. We investigated this using data from a laboratory study in which 20 healthy young adults were each exposed to 36 h of total sleep deprivation, preceded by a baseline sleep period and followed by a recovery sleep period, on three separate occasions. In the week prior to each laboratory session and on the corresponding baseline night in the laboratory, participants either extended their sleep period to 12 h/day or restricted it to 6 h/day. During periods of wakefulness in the laboratory, starting right after scheduled awakening, participants completed neurobehavioral tests every 2 h. Testing included the Karolinska Sleepiness Scale to measure subjective sleepiness, for which the data were analyzed with nonlinear mixed-effects regression to quantify sleep inertia. This revealed considerable interindividual differences in the magnitude of sleep inertia, which were highly stable within individuals after both baseline and recovery sleep periods, regardless of study condition. Our results demonstrate that interindividual differences in subjective sleepiness due to sleep inertia are substantial and constitute a trait.

EEG Patterns Prior to Motor Activations of Parasomnias: A Systematic Review

INTRODUCTION

Non-rapid eye movement (NREM) parasomnias are defined as abnormal nocturnal behaviors that typically arise from the NREM sleep stage 3 during the first sleep cycle. The polysomnographic studies showed an increase in sleep fragmentation and an atypical slow wave activity (SWA) in participants with NREM parasomnias compared to healthy controls. To date, the pathophysiology of NREM parasomnias is still poorly understood. The recent investigation of the EEG patterns immediately before parasomnia events could shed light on the motor activations' processes. This systematic review aims to summarize empirical evidence about these studies and provide an overview of the methodological issues.

METHODS

A systematic literature search was carried out in PubMed, Web of Science, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The documents obtained were evaluated using the Newcastle-Ottawa Scale (NOS).

RESULTS

Nine studies were included in the qualitative synthesis. The major evidence revealed an increased slow frequency EEG activity immediately before the motor activations in frontal and central areas and increased beta activity in the anterior cingulate cortices.

DISCUSSION

The investigation of EEG patterns before parasomniac episodes could provide new insight into the study of NREM parasomnia pathophysiology. The high- and low-frequency EEG increase before the episodes could represent a predictive electrophysiological pattern of the motor activations' onset. Overall, identifying specific sleep markers before parasomnias might also help differentiate between NREM parasomnias and other motor sleep disorders. Different methodological protocols should be integrated for overcoming the lack of consistent empirical findings. Thus, future studies should focus on the topographical examination of canonical EEG frequency bands to better understand spatial and time dynamics before the episodes and identify the networks underlying the onset of activations.

Association between polysomnographic parameters of sleep bruxism and attrition-type tooth wear

BACKGROUND

Clinically, sleep bruxism is considered to be associated with the presence of tooth wear, but strong evidence is still lacking.

OBJECTIVE

To examine whether an association exists between polysomnographic parameters, recorded from patients with possible sleep bruxism and tooth wear.

METHODS

Sixty-three possible sleep bruxers (19 males and 44 females, mean ± SD age = 38.5 ± 11.4 years) were recruited among patients attending the Clinic for orofacial pain and dysfunction of the Academic Centre for Dentistry Amsterdam (ACTA). The incisal/occlusal tooth wear was recorded for each tooth clinically, using a 5-point ordinal scale. Subsequently, all patients underwent an one-night ambulatory polysomnographic recording, during which the number of bruxism episodes per hour of sleep (Epi/h), the number of bruxism bursts per hour of sleep (Bur/h), and the bruxism time index (BTI) were recorded and analysed. Logistic regression analysis was performed using the presence of tooth wear as the dependent variable, the polysomnographic recordings as independent variables, and corrected for age and gender. The Bur/h and BTI were removed from the analyses due to collinearity with the Epi/h. Additionally, the polysomnographic recordings were also tested for possible association with self-reported grinding of the teeth during sleep.

RESULTS

No significant correlation was found between tooth wear and Epi/h (P = 0.381). In addition, the presence of tooth wear was not associated with self-reported parafunctions.

CONCLUSION

Clinically measured tooth wear and self-reported parafunction seem not be related to the polysomnographic parameters of possible sleep bruxism.

Actigraphy in sleep research with infants and young children: Current practices and future benefits of standardized reporting

Actigraphy is a cost-efficient method to estimate sleep-wake patterns over long periods in natural settings. However, the lack of methodological standards in actigraphy research complicates the generalization of outcomes. A rapidly growing methodological diversity is visible in the field, which increasingly necessitates the detailed reporting of methodology. We address this problem and evaluate the current state of the art and recent methodological developments in actigraphy reporting with a special focus on infants and young children. Through a systematic literature search on PubMed (keywords: sleep, actigraphy, child *, preschool, children, infant), we identified 126 recent articles (published since 2012), which were classified and evaluated for reporting of actigraphy. Results show that all studies report on the number of days/nights the actigraph was worn. Reporting was good with respect to device model, placement and sleep diary, whereas reporting was worse for epoch length, algorithm, artefact identification, data loss and definition of variables. In the studies with infants only (n = 58), the majority of articles (62.1%) reported a recording of actigraphy that was continuous across 24 hr. Of these, 23 articles (63.9%) analysed the continuous 24-hr data and merely a fifth used actigraphy to quantify daytime sleep. In comparison with an evaluation in 2012, we observed small improvements in reporting of actigraphy methodology. We propose stricter adherence to standards in reporting methodology in order to streamline actigraphy research with infants and young children, to improve comparability and to facilitate big data ventures in the sleep community.

Coupling between slow waves and sharp-wave ripples engages distributed neural activity during sleep in humans

Hippocampal-dependent memory consolidation during sleep is hypothesized to depend on the synchronization of distributed neuronal ensembles, organized by the hippocampal sharp-wave ripples (SWRs, 80 to 150 Hz), subcortical/cortical slow-wave activity (SWA, 0.5 to 4 Hz), and sleep spindles (SP, 7 to 15 Hz). However, the precise role of these interactions in synchronizing subcortical/cortical neuronal activity is unclear. Here, we leverage intracranial electrophysiological recordings from the human hippocampus, amygdala, and temporal and frontal cortices to examine activity modulation and cross-regional coordination during SWRs. Hippocampal SWRs are associated with widespread modulation of high-frequency activity (HFA, 70 to 200 Hz), a measure of local neuronal activation. This peri-SWR HFA modulation is predicted by the coupling between hippocampal SWRs and local subcortical/cortical SWA or SP. Finally, local cortical SWA phase offsets and SWR amplitudes predicted functional connectivity between the frontal and temporal cortex during individual SWRs. These findings suggest a selection mechanism wherein hippocampal SWR and cortical slow-wave synchronization governs the transient engagement of distributed neuronal populations supporting hippocampal-dependent memory consolidation.

Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework

Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measures), self-reported sleep (three), health (eight), health behaviours (four), and sociodemographic factors (five). A 10-fold repeated internal validation (WSCS and SHHS combined) and external validation (training in SHHS; testing in WSCS) were used to compute unbiased variable importance metrics and associated p values. We observed that health, sociodemographic factors, and PSG-assessed sleep domains predicted mortality using both external validation and repeated internal validation. The PSG-assessed sleep efficiency and the percentage of sleep time with oxygen saturation <90% were among the most predictive individual measures. Multivariable Cox regression also revealed the PSG-assessed sleep domain to be predictive, with very low sleep efficiency and high hypoxaemia conferring the highest risk. These findings, coupled with the emergence of new low-burden technologies for objectively assessing sleep and overnight oxygen saturation, suggest that consideration of physiological sleep measures may improve risk screening.

Maturational trajectories of non-rapid eye movement slow wave activity and odds ratio product in a population-based sample of youth

BACKGROUND

Brain maturation is reflected in the sleep electroencephalogram (EEG) by a decline in non-rapid eye movement (NREM) slow wave activity (SWA) throughout adolescence and a related decrease in sleep depth. However, this trajectory and its sex and pubertal differences lack replication in population-based samples. We tested age-related changes in SWA (0.4-4 Hz) power and odds ratio product (ORP), a standardized measure of sleep depth.

METHODS

We analyzed the sleep EEG of 572 subjects aged 6-21 y (48% female, 26% racial/ethnic minority) and 332 subjects 5-12 y followed-up at 12-22 y. Multivariable-adjusted analyses tested age-related cross-sectional and longitudinal trajectories of SWA and ORP.

RESULTS

SWA remained stable from age 6 to 10, decreased between ages 11 and 17, and plateaued from age 18 to 21 (p-cubic<0.001); females showed a longitudinal decline 23% greater than males by 13 y, while males experienced a steeper slope after 14 y and their longitudinal decline was 21% greater by 19 y. More mature adolescents (75% female) experienced a greater longitudinal decline in SWA than less mature adolescents by 14 y. ORP showed an age-related increasing trajectory (p-linear<0.001) with no sex or pubertal differences.

CONCLUSIONS

We provide population-level evidence for the maturational decline and sex and pubertal differences in SWA in the transition from childhood to adolescence, while introducing ORP as a novel metric in youth. Along with previous studies, the distinct trajectories observed suggest that age-related changes in SWA reflect brain maturation and local/synaptic processes during this developmental period, while those of ORP may reflect global/state control of NREM sleep depth.

Couple Relationships Are Associated With Increased REM Sleep-A Proof-of-Concept Analysis of a Large Dataset Using Ambulatory Polysomnography

Background/Objectives: Rapid Eye Movement (REM) sleep is associated with memory consolidation and several health effects including stress response, mental health, and longevity. Recently, it has been shown that regularly co-sleeping couples have increased and stabilized REM sleep when co-sleeping as compared to sleeping individually. However, it remained unclear whether this is due to a specific effect of altering the usual sleeping environment by partner deprivation or due to a generalizable REM-sleep promoting effect of couple relationships. The present study aims to clarify this ambiguity. Methods: Married or never married individuals were taken from the Sleep Heart Health Study (n = 5,804) and matched regarding sociodemographic and health parameters. Matching was done using propensity score matching (1:1, nearest neighbor) and resulted in two groups of n = 69 each (married vs. never married). After confirmation of successful matching, samples were compared regarding REM sleep and other polysomnographic parameters (paired Students t-tests or Wilcoxon signed-rank tests). Results: Married individuals showed significantly higher levels of total and relative REM sleep as compared to never married individuals (all p's ≤ 0.003). Neither other sleep stages nor REM-sleep fragmentation differed between groups (all p's ≥ 0.29). Results regarding number of sleep cycles were ambiguous. Conclusion: This is the first between-subjects study to show that couple relationships are associated with increased REM sleep. This finding represents a necessary (but not sufficient) condition for the previously hypothesized self-enhancing feedback loop of REM sleep and sociality as well as for REM-sleep promotion as a mechanism through which couple relationships prevent mental illness.

Synergistic effect of sleep depth and seizures correlates with postictal heart rate

Our objective was to determine the effect of sleep on heart rate following a recorded seizure. We prospectively acquired heart rate data in hospitalized epilepsy monitoring unit patients. We analyzed heart rate trends for multiple seizures (n = 101) in patients (n = 42) with electroencephalographically confirmed events. The patient's sleep state was scored for the 5 min preceding each seizure and correlated with the postictal nadir heart rate (PINHR). The depth of sleep during the 5 min before a seizure correlated (correlation coefficient [CC] = -.229, p < .05) with PINHR. This result was more significant and strengthened (CC = -.272, 95% confidence interval = -.392 to -.152, p < .001) when adjusted for covariates of age, generalized tonic-clonic seizures, and baseline heart rate. Sleep depth is an independent predictor of the change in heart rate following a seizure. Diminished heart rate following a seizure in the setting of sleep is likely secondary to non-rapid eye movement sleep's synergistic effect on parasympathetic tone.

The (mis)perception of sleep: factors influencing the discrepancy between self-reported and objective sleep parameters

STUDY OBJECTIVES

Self-reported perception of sleep often differs from objective sleep study measures, but factors predicting the discrepancy between self-reported and objective sleep parameters are controversial, and a comparison of laboratory vs ambulatory polysomnography (PSG) is lacking.

METHODS

We retrospectively analyzed PSGs conducted between 2012 and 2016. Linear regression was applied to predict the discrepancy between self-reported and objective sleep parameters (total sleep time, sleep efficiency, sleep latency, using age, sex, arousal index, type of sleep disorder, and PSG type [laboratory vs ambulatory] as regressors).

RESULTS

A total of 303 PSGs were analyzed (49% women, median age 48 years), comprising patients with insomnia (32%), sleep-related breathing disorders (27%), sleep-related movement disorders (15%), hypersomnia/narcolepsy (14%), and parasomnias (12%). Sleep disorder was the best predictor of discrepancy between self-reported and objective total sleep time, and patients with insomnia showed higher discrepancy values compared to all other patient groups (P < .001), independent of age and PSG type (P > .05). Contributory effects for higher discrepancy values were found for lower arousal index. Patients with insomnia underestimated both total sleep time (median discrepancy: 46 minutes, P < .001) and sleep efficiency (median discrepancy: 11%, P < .001). No significant predictor for discrepancy of sleep latency was found.

CONCLUSIONS

Misperception of sleep duration and efficiency is common in sleep lab patients, but most prominent in insomnia, independent of age, sex, or laboratory vs ambulatory recording setting. This underlines the role of PSG in patients with a clinical diagnosis of insomnia and its use in cognitive behavioral therapy.

CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.

Effect of CPAP Therapy on 24-Hour Intraocular Pressure-Related Pattern From Contact Lens Sensors in Obstructive Sleep Apnea Syndrome

Purpose

To evaluate the effect of continuous positive airway pressure (CPAP) therapy on 24-hour intraocular pressure (IOP)-related pattern from contact lens sensors (CLS) in obstructive sleep apnea syndrome (OSAS).

Methods

Prospective, observational, case series study. Twenty-two eyes of 22 newly diagnosed patients with severe OSAS were included. A first 24-hour CLS measurement was performed before CPAP therapy was started, and a second 24-hour CLS monitoring was performed after beginning CPAP. We analyzed the amplitude and the maximum and minimum IOP-related values (m Veq). We also analyzed IOP-related measurements at five-minute intervals throughout the first hour of nocturnal acrophase, starting from when the patient fell asleep.

Results

The baseline measurements showed significant fluctuations in the IOP, with the highest IOP readings being recorded at night (nocturnal acrophase) in 17 of 22 patients (77.27%). Nocturnal acrophase began when the patients laid down to sleep. During CPAP therapy, the patients showed a more marked increase in IOP in the initial phase of nocturnal acrophase, with significant differences at 20, 25, 30, and 55 minutes (P < 0.05).

Conclusions

Most of patients with severe OSAS exhibited a nocturnal acrophase and the highest IOP readings at night. CPAP was associated with additional increase in IOP-related pattern for at least the first hour of CPAP use.

Translational Relevance

Our results suggest that CPAP was associated with additional increase in IOP during the initial phase of nocturnal acrophase. This effect could be important in the management of patients with OSAS and glaucomatous progression.

Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors

OBJECTIVE

Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.

METHODS

Polysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels.

RESULTS

The EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10.

CONCLUSIONS

This study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques.

SIGNIFICANCE

This study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.

Effects of exercise training on autonomic modulation and mood symptoms in patients with obstructive sleep apnea

We evaluated the effects of exercise training (ET) on the profile of mood states (POMS), heart rate variability, spontaneous baroreflex sensitivity (BRS), and sleep disturbance severity in patients with obstructive sleep apnea (OSA). Forty-four patients were randomized into 2 groups, 18 patients completed the untrained period and 16 patients completed the exercise training (ET). Beat-to-beat heart rate and blood pressure were simultaneously collected for 5 min at rest. Heart rate variability (RR interval) was assessed in time domain and frequency domain (FFT spectral analysis). BRS was analyzed with the sequence method, and POMS was analyzed across the 6 categories (tension, depression, hostility, vigor, fatigue, and confusion). ET consisted of 3 weekly sessions of aerobic exercise, local strengthening, and stretching exercises (72 sessions, achieved in 40±3.9 weeks). Baseline parameters were similar between groups. The comparisons between groups showed that the changes in apnea-hypopnea index, arousal index, and O2 desaturation in the exercise group were significantly greater than in the untrained group (P<0.05). The heart rate variability and BRS were significantly higher in the exercise group compared with the untrained group (P<0.05). ET increased peak oxygen uptake (P<0.05) and reduced POMS fatigue (P<0.05). A positive correlation (r=0.60, P<0.02) occurred between changes in the fatigue item and OSA severity. ET improved heart rate variability, BRS, fatigue, and sleep parameters in patients with OSA. These effects were associated with improved sleep parameters, fatigue, and cardiac autonomic modulation, with ET being a possible protective factor against the deleterious effects of hypoxia on these components in patients with OSA.

Natural history of insomnia symptoms in the transition from childhood to adolescence: population rates, health disparities, and risk factors

STUDY OBJECTIVES

To determine the sociodemographic, behavioral, and clinical risk factors associated with the persistence, remission, and incidence of insomnia symptoms in the transition from childhood to adolescence.

METHODS

The Penn State Child Cohort is a random, population-based sample of 700 children (5-12 years at baseline), of whom 421 were followed-up as adolescents (12-23 years at follow-up). Subjects underwent polysomnography, clinical history, physical exam, and parent- and self-reported scales at baseline and follow-up. Insomnia symptoms were defined as a parent- or self-report of difficulty falling and/or staying asleep.

RESULTS

The 421 subjects with baseline (Mage = 8.8 years) and follow-up (Mage = 17 years) data were 53.9% male and 21.9% racial/ethnic minorities. The persistence of childhood insomnia symptoms (CIS) was 56% (95% CI = 46.5-65.4), with only 30.3% (95% CI = 21.5-39.0) fully remitting. The incidence of adolescent insomnia symptoms was 31.1% (95% CI = 25.9-36.3). Female sex, racial/ethnic minority, and low socioeconomic status as well as psychiatric/behavioral or neurological disorders, obesity, smoking, and evening chronotype were associated with a higher persistence or incidence of insomnia symptoms.

CONCLUSIONS

CIS are highly persistent, with full remission occurring in only a third of children in the transition to adolescence. Sex-, racial/ethnic-, and socioeconomic-related disparities in insomnia occur as early as childhood, while different mental/physical health and lifestyle/circadian risk factors play a key role in the chronicity of CIS versus their incidence in adolescence. CIS should not be expected to developmentally remit and should become a focus of integrated pediatric/behavioral health strategies.

EEG-Based Sleep Staging Analysis with Functional Connectivity

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

Diagnostic and Therapeutic Approach to Sleep Disorders, High Blood Pressure and Cardiovascular Diseases: A Consensus Document by the Italian Society of Hypertension (SIIA)

Hypertension is a major contributor to fatal/nonfatal cardiovascular diseases, and timely identification and appropriate management of factors affecting hypertension and its control are mandatory public health issues. By inducing neurohormonal alterations and metabolic impairment, sleep disorders have an impact on a variety of cardiovascular risk factors, including hypertension, and ultimately increase the risk of cardiovascular events. There is evidence that qualitative and quantitative sleep disorders are associated with resistant hypertension and with impaired circadian blood pressure variations. However, sleep disturbances are often unrecognized, or heterogeneity exists in their management by non-specialists in the field. This document by the Italian Society of Hypertension summarizes the updated evidence linking sleep disorders to hypertension and cardiovascular diseases, the major underlying mechanisms, and the possible management strategies. A simplified, evidence-based diagnostic and therapeutic algorithm for comorbid hypertension and common sleep disorders, namely obstructive sleep apnoea and insomnia, is proposed.

The Epidemiology of Obstructive Sleep Apnea in Poland-Polysomnography and Positive Airway Pressure Therapy

The aim of this study is to provide a brief summary of the epidemiological data on obstructive sleep apnea syndrome (OSAS) diagnosis and therapy in different regions of Poland from 2010 to 2019. We performed a retrospective study in the sleep center of the Department of Sleep Medicine and Metabolic Disorders, Medical University of Lodz, Poland. We requested data from the National Health Service concerning the number of new diagnoses of OSAS, the polysomnographies (PSGs) that were performed, and reimbursements of positive airway pressure (PAP) therapy in each region of Poland in the period 2010-2019. The constant increase in the number of polysomnographies performed and PAP reimbursements suggests the need to create a national network between regional sleep centers to provide proper care for patients with OSAS, and PAP therapy.

A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen's kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

Consolidation and generalisation across sleep depend on individual EEG factors and sleep spindle density

Sleep is involved in both the consolidation of discrete episodes, as well as the generalisation of acquired memories into schemata. Here, we have isolated early versus late periods of sleep in order to replicate previous behavioural findings and to demonstrate: i) that distinct sleep and sleep electroencephalography (EEG) factors influence the generalisation of learned information, and; ii) that the consolidation and generalisation of memory across sleep depends on individual alpha frequency (IAF) and strength of initial encoding. Subjects underwent a night-half protocol with polysomnography (PSG), and completed a Chinese character-English paired associates learning task. Recognition accuracy of learned word-pairs, the extent to which the subject was able to generalise this knowledge, and the extent of explicit transfer of knowledge were measured. Results demonstrate that quality of initial learning determined the relationship between sleep neurophysiology and outcome, with IAF modulating this effect. We also note an effect of IAF in modulating the effect of sleep spindles in determining generalisation of learned materials. Finally, we note a complex relationship between initial learning, IAF and sleep spindle density in determining when information will reach explicit awareness across sleep. Together, these data implicate encoding factors in subsequent offline processing, demonstrate a potential role for individual differences in the EEG and subsequently add to our understanding of the the conditions in which sleep may benefit both memory and learning.

Blood pressure dipping and sleep quality in the Wisconsin Sleep Cohort

AIMS

Nondipping blood pressure (BP) is associated with higher risk for hypertension and advanced target organ damage. Insomnia is the most common sleep complaint in the general population. We sought to investigate the association between sleep quality and insomnia and BP nondipping cross-sectionally and longitudinally in a large, community-based sample.

METHODS

A subset of the Wisconsin Sleep Cohort (n = 502 for cross-sectional analysis and n = 260 for longitudinal analysis) were enrolled in the analysis. Polysomnography measures were used to evaluate sleep quality. Insomnia symptoms were obtained by questionnaire. BP was measured by 24-h ambulatory BP monitoring. Logistic regression models estimated cross-sectional associations of sleep quality and insomnia with BP nondipping. Poisson regression models estimated longitudinal associations between sleep quality and incident nondipping over a mean 7.4 years of follow-up. Systolic and diastolic nondipping were examined separately.

RESULTS

In cross-sectional analyses, difficulty falling asleep, longer waking after sleep onset, shorter and longer total sleep time, lower sleep efficiency and lower rapid eye movement stage sleep were associated with higher risk of SBP and DBP nondipping. In longitudinal analyses, the adjusted relative risks (95% confidence interval) of incident systolic nondipping were 2.1 (1.3-3.5) for 1-h longer waking after sleep onset, 2.1 (1.1-5.1) for 7-8 h total sleep time, and 3.7 (1.3-10.7) for at least 8-h total sleep time (compared with total sleep time 6-7 h), and 1.9 (1.1-3.4) for sleep efficiency less than 0.8, respectively.

CONCLUSION

Clinical features of insomnia and poor sleep quality are associated with nondipping BP. Our findings suggested nondipping might be one possible mechanism by which poor sleep quality was associated with worse cardiovascular outcomes.

High-Resolution Spectral Sleep Analysis Reveals a Novel Association Between Slow Oscillations and Memory Retention in Elderly Adults

Objective: In recognition of the mixed associations between traditionally scored slow wave sleep and memory, we sought to explore the relationships between slow wave sleep, electroencephalographic (EEG) power spectra during sleep and overnight verbal memory retention in older adults. Design, Setting, Participants, and Measurements: Participants were 101 adults without dementia (52% female, mean age 70.3 years). Delayed verbal memory was first tested in the evening prior to overnight polysomnography (PSG). The following morning, subjects were asked to recall as many items as possible from the same List (overnight memory retention; OMR). Partial correlation analyses examined the associations of delayed verbal memory and OMR with slow wave sleep (SWS) and two physiologic EEG slow wave activity (SWA) power spectral bands (0.5-1 Hz slow oscillations vs. 1-4 Hz delta activity). Results: In subjects displaying SWS, SWS was associated with enhanced delayed verbal memory, but not with OMR. Interestingly, among participants that did not show SWS, OMR was significantly associated with a higher slow oscillation relative power, during NREM sleep in the first ultradian cycle, with medium effect size. Conclusions: These findings suggest a complex relationship between SWS and memory and illustrate that even in the absence of scorable SWS, older adults demonstrate substantial slow wave activity. Further, these slow oscillations (0.5-1 Hz), in the first ultradian cycle, are positively associated with OMR, but only in those without SWS. Our findings raise the possibility that precise features of slow wave activity play key roles in maintaining memory function in healthy aging. Further, our results underscore that conventional methods of sleep evaluation may not be sufficiently sensitive to detect associations between SWA and memory in older adults.

Sleep-Related Declarative Memory Consolidation in Children and Adolescents with Developmental Dyslexia

Sleep has a crucial role in memory processes, and maturational changes in sleep electrophysiology are involved in cognitive development. Albeit both sleep and memory alterations have been observed in Developmental Dyslexia (DD), their relation in this population has been scarcely investigated, particularly concerning topographical aspects. The study aimed to compare sleep topography and associated sleep-related declarative memory consolidation in participants with DD and normal readers (NR). Eleven participants with DD and 18 NR (9-14 years old) underwent a whole-night polysomnography. They were administered a word pair task before and after sleep to assess for declarative memory consolidation. Memory performance and sleep features (macro and microstructural) were compared between the groups, and the intercorrelations between consolidation rate and sleep measures were assessed. DD showed a deeper worsening in memory after sleep compared to NR and reduced slow spindles in occipito-parietal and left fronto-central areas. Our results suggest specific alterations in local sleep EEG (i.e., sleep spindles) and in sleep-dependent memory consolidation processes in DD. We highlight the importance of a topographical approach, which might shed light on potential alteration in regional cortical oscillation dynamics in DD. The latter might represent a target for therapeutic interventions aimed at enhancing cognitive functioning in DD.