Polysomnography

Impact of obstructive sleep apnea syndrome on olfactory and gustatory capacity

Only a few studies have investigated olfactory function in patients with obstructive sleep apnea syndrome (OSAS) using psychophysical testing, and there is a scarcity of data regarding taste evaluation in the existing literature. The primary objectives of this study were to assess both smell and taste in patients with OSAS and to explore the correlation between the severity of symptoms and sensory perception. A total of 85 OSAS patients and a control group comprising 81 subjects were enrolled. Initial assessments included anamnesis, nasal endoscopy, and the completion of questionnaires (Epworth Sleepiness Scale, Visual Analogue Scale, Questionnaire of Olfactory Disorders, and the importance of olfaction questionnaire). The diagnosis of OSAS was confirmed by polysomnography, while nasal airflow was evaluated using rhinomanometry. Olfaction was assessed using the Sniffin' Sticks test, and the Threshold-Discrimination-Identification (TDI) score was calculated. Taste evaluation was conducted in a subgroup of participants (42 patients, 38 controls) using taste strips. The mean TDI score was 31 ± 5.6 for OSAS patients and 35 ± 4.6 for controls, indicating a significant difference (P < 0.001). Similarly, the taste score was 7 ± 3.0 for OSAS patients and 12.6 ± 3.2 for controls (P < 0.001). No correlations were observed between TDI and Apnea Hypopnea Index (AHI) (r = -0.12; P = 0.28), as well as between the taste score and AHI (r = -0.31; P = 0.22). However, a weak but significant correlation between TDI score and Epworth Sleepiness Scale was detected (r = -0.05; P = 0.002). The study revealed a significant decrease in sensory perception among patients with OSAS, though open questions persist about the pathophysiology.

Sleep quality and comfort in fully automated vehicles: A comparison of two seat configurations

As autonomous driving technology advances, the possibility of using vehicles as sleeping environments becomes increasingly relevant. To investigate the feasibility of this concept, a sleep study was conducted with twelve participants who were given a 4-h opportunity window to sleep in both reclined and flat seat configurations. The evaluation involved both objective measures, including polysomnographic (PSG) data analysis, and subjective measures through questionnaires, assessing sleep quality and comfort. While the sleep quantity results were comparable between the two sleeping positions, the reclined position showed a slight advantage in sleep quantity (TST and WASO). Interestingly, a trend highlighting a possible difference was found between the seat positions regarding non-rapid eye movement stage 3 (NREM 3). NREM 3 tended to be in a higher proportion of total sleep time in the flat seat position. Sleep onset latency (SOL) also showed a trend of a shorter latency by participants in the flat position. Additionally, most participants reported a preference for the flat position over the reclined position. These findings suggest that a flat seat configuration could offer a more comfortable and restful sleep environment for passengers in autonomous vehicles.

Sex and Sleep Disruption as Contributing Factors in Alzheimer's Disease

Alzheimer's disease (AD) affects more women than men, with women throughout the menopausal transition potentially being the most under researched and at-risk group. Sleep disruptions, which are an established risk factor for AD, increase in prevalence with normal aging and are exacerbated in women during menopause. Sex differences showing more disrupted sleep patterns and increased AD pathology in women and female animal models have been established in literature, with much emphasis placed on loss of circulating gonadal hormones with age. Interestingly, increases in gonadotropins such as follicle stimulating hormone are emerging to be a major contributor to AD pathogenesis and may also play a role in sleep disruption, perhaps in combination with other lesser studied hormones. Several sleep influencing regions of the brain appear to be affected early in AD progression and some may exhibit sexual dimorphisms that may contribute to increased sleep disruptions in women with age. Additionally, some of the most common sleep disorders, as well as multiple health conditions that impair sleep quality, are more prevalent and more severe in women. These conditions are often comorbid with AD and have bi-directional relationships that contribute synergistically to cognitive decline and neuropathology. The association during aging of increased sleep disruption and sleep disorders, dramatic hormonal changes during and after menopause, and increased AD pathology may be interacting and contributing factors that lead to the increased number of women living with AD.

Detection of common EEG phenomena using individual electrodes placed outside the hair

Many studies over the past decades have provided exciting evidence that electrical signals recorded from the scalp (electroencephalogram, EEG) hold meaningful information about the brain's function or dysfunction. This information is used routinely in research laboratories to test specific hypotheses and in clinical settings to aid in diagnoses (such as during polysomnography evaluations). Unfortunately, with very few exceptions, such meaningful information about brain function has not yet led to valuable solutions that can address the needs of many people outside such research laboratories or clinics. One of the major hurdles to practical application of EEG-based neurotechnologies is the current predominant requirement to use electrodes that are placed in the hair, which greatly reduces practicality and cosmesis. While several studies reported results using one specific combination of signal/reference electrode outside the hair in one specific context (such as a brain-computer interface experiment), it has been unclear what information about brain function can be acquired using different signal/referencing locations placed outside the hair. To address this issue, in this study, we set out to determine to what extent EEG phenomena related to auditory, visual, cognitive, motor, and sleep function can be detected from different combinations of individual signal/referencing electrodes that are placed outside the hair. The results of our study from 15 subjects suggest that only a few EEG electrodes placed in locations on the forehead or around the ear can provide substantial task-related information in 6 of 7 tasks. Thus, the results of our study provide encouraging evidence and guidance that should invigorate and facilitate the translation of laboratory experiments into practical, useful, and valuable EEG-based neurotechnology solutions.

Sleep and Sleep Hygiene of Adolescents With and Without ADHD During COVID-19

OBJECTIVE

This study examined the effect of COVID-19 restrictions on the sleep and sleep hygiene of adolescents with ADHD and comorbid sleep problems and neurotypical adolescents (NT).

METHOD

Four groups (two ADHD and two NT) of in total 100 adolescents (50 ADHD and 50 NT) were included. One ADHD and NT group were tested during many COVID-19 restrictions, the other during few. MANCOVAs were implemented with ADHD diagnosis and level of COVID-19 restrictions as independent and sleep outcomes (subjective and objective total sleep time (TST) and sleep onset latency (SOL), sleep and sleep hygiene problems) as dependent variables.

RESULTS

Both groups had a shorter objective TST during the week during many COVID-19 restrictions. Furthermore, adolescents with ADHD had a shorter subjective SOL during the weekend when there were many COVID-19 restrictions, while the SOL of the NT group stayed the same.

CONCLUSION

COVID-19 restrictions are related to the sleep of adolescents with and without ADHD. However, causality and underlying mechanisms need further investigation.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States. Two methods are used to learn the causal structure of these data: the well-established Granger causality and "DYNOTEARS", a modern approach that uses continuous optimisation to learn dynamic Bayesian networks (DBNs). The results from the two methods are then compared. Both methods produce graphs that have a number of similarities, including the mutual causation between electrooculogram (EOG) and electroencephelogram (EEG) signals and between sleeping position and SpO2 (blood oxygen level). However, DYNOTEARS, unlike Granger causality, frequently finds a causal link to sleeping position from the other variables. Following the creation of these causal graphs, the relationship between the discovered causal structure and the characteristics of the participants is explored. It is found that there is an association between the waist size of a participant and whether a causal link is found between the electrocardiogram (ECG) measurement and the EOG and EEG measurements. It is concluded that a person's body shape appears to impact the relationship between their heart and brain during sleep and that Granger causality and DYNOTEARS can produce differing results on real-world data.

Association between sleep quality and nocturnal erection monitor by RigiScan in erectile dysfunction patients: a prospective study using fitbit charge 2

BACKGROUND

Few studies were conducted to explore the association between sleep quality and nocturnal erection. Here, we intended to explore the association between sleep quality and nocturnal erection monitor when conducting nocturnal erection monitor. All erectile dysfunction (ED) patients underwent sleep monitors using Fitbit Charge 2™ (Fitbit Inc.) and nocturnal penile tumescence and rigidity (NPTR) monitors using RigiScan® (GOTOP medical, Inc., USA) for two nights. Subsequently, the patients were divided into two groups: Group A included patients who experienced effective erections only on the second night, while Group B included patients who had effective erections on both nights. To explore the associations between NPTR parameters and sleep parameters, a comparative analysis was performed between Group A and Group B for both nights.

RESULTS

Finally, our study included 103 participants, with 47 patients in Group A and 56 patients in Group B. Notably, the Group A patients showed significant improvements in NPTR parameters on the second night compared to the first night. Conversely, the NPTR parameters on Group B of the second night did not demonstrate a superior outcome when compared to the second night of Group A. Interestingly, it was found that only the disparities in sleep parameters accounted for the variation in NPTR parameters between the two groups on the first night. After correlation and ROC analysis, we identified the rapid eye movement (REM) sleep time and wake after sleep onset (WASO) time monitoring by the Fitbit Charge 2 as the primary parameters for predicting abnormal NPTR results in the first night.

CONCLUSIONS

Therefore, our study strongly suggests a close association between sleep parameters and NPTR parameters. It emphasizes the importance of incorporating sleep monitoring alongside nocturnal erection monitoring to enhance the reliability of the NPTR results.

Diagnostic Accuracy of a Portable Electromyography and Electrocardiography Device to Measure Sleep Bruxism in a Sleep Apnea Population: A Comparative Study

BACKGROUND

The gold standard for diagnosing sleep bruxism (SB) and obstructive sleep apnea (OSA) is polysomnography (PSG). However, a final hypermotor muscle activity often occurs after apnea episodes, which can confuse the diagnosis of SB when using portable electromyography (EMG) devices. This study aimed to compare the number of SB episodes obtained from PSG with manual analysis by a sleep expert, and from a manual and automatic analysis of an EMG and electrocardiography (EKG) device, in a population with suspected OSA.

METHODS

Twenty-two subjects underwent a polysomnographic study with simultaneous recording with the EMG-EKG device. SB episodes and SB index measured with both tools and analyzed manually and automatically were compared. Masticatory muscle activity was scored according to published criteria. Patients were segmented by severity of OSA (mild, moderate, severe) following the American Academy of Sleep Medicine (AASM) criteria. ANOVA and the Bland-Altman plot were used to quantify the agreement between both methods. The concordance was calculated through the intraclass correlation coefficient (ICC).

RESULTS

On average, the total events of SB per night in the PSG study were (8.41 ± 0.85), lower than the one obtained with EMG-EKG manual (14.64 ± 0.76) and automatic (22.68 ± 16.02) analysis. The mean number of SB episodes decreases from the non-OSA group to the OSA group with both PSG (5.93 ± 8.64) and EMG-EKG analyses (automatic = 22.47 ± 18.07, manual = 13.93 ± 11.08). However, this decrease was minor in proportion compared to the automatic EMG-EKG analysis mode (from 23.14 to 22.47). The ICC based on the number of SB episodes in the segmented sample by severity degree of OSA along the three tools shows a moderate correlation in the non-OSA (0.61) and mild OSA (0.53) groups. However, it is poorly correlated in the moderate (0.24) and severe (0.23) OSA groups: the EMG-EKG automatic analysis measures 14.27 units more than PSG. The results of the manual EMG-EKG analysis improved this correlation but are not good enough.

CONCLUSIONS

The results obtained in the PSG manual analysis and those obtained by the EMG-EKG device with automatic and manual analysis for the diagnosis of SB are acceptable but only in patients without OSA or with mild OSA. In patients with moderate or severe OSA, SB diagnosis with portable electromyography devices can be confused due to apneas, and further study is needed to investigate this.

Acupuncture Therapy for Military Veterans Suffering from Posttraumatic Stress Disorder and Related Symptoms: A Scoping Review of Clinical Studies

Military personnel in combat face a high risk of developing posttraumatic stress disorder (PTSD). In this study, a protocol-based scoping review was conducted to identify the current status of research on the efficacy of acupuncture for treating combat-related PTSD in military personnel. A literature search was conducted across 14 databases in November 2022, and data from the included studies were collected and descriptively analyzed. A total of eight studies were included. Participants were assessed for core PTSD symptoms using the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 and the Clinician-Administered PTSD Scale, as well as related symptoms, such as sleep issues. Although the efficacy of acupuncture has been substantiated in numerous studies, certain metrics did not exhibit improvement. Auricular acupuncture was the most commonly used treatment (50%) followed by manual acupuncture (25%) and a combination of both (25%). Shenmen and Kidney points were frequently targeted at auricular acupoints. The treatment period varied between 5 days and 2 months. While adverse events were reported in two of the fifty-five patients in the intervention group and in four of the sixty-four patients in the control group in the randomized controlled trial studies, no fatal adverse events were reported.

From early birds to night owls: a longitudinal study of actigraphy-assessed sleep trajectories during the transition from pre- to early adolescence

STUDY OBJECTIVES

Pre- and early adolescence are believed to constitute periods of important age-related changes in sleep. However, much of the research on these presumed developmental changes has used cross-sectional data or subjective measures of sleep, limiting the quality of the evidence. In addition, little is known about the development of certain features of the sleep-wake cycle pertaining to regularity (e.g. weekend-weekday differences and intra-individual variability) or circadian rhythms (e.g. sleep midpoint).

METHODS

This study examined the sleep trajectories of 128 typically developing youth (69 girls) from ages 8 to 12 years on four sleep characteristics: sleep onset, sleep offset, total sleep time (TST), and sleep midpoint. For each of these characteristics, actigraphy-derived estimates of typical (i.e. mean) sleep and sleep regularity were obtained at each time point. Multilevel growth curves were modeled.

RESULTS

Overall, the sleep-wake cycle significantly changed between 8 and 12 years. Mean sleep onset, offset and midpoint exhibited an ascending curvilinear growth pattern that shifted later with age, while mean TST decreased linearly. Weekend-weekday differences (social jetlag) for sleep offset and midpoint became more pronounced each year. Weekday TST was longer than weekend TST, though this difference became smaller over time. Finally, intra-individual variability increased over time for all sleep characteristics, with variability in TST ascending curvilinearly. Important between-person and sex differences were also observed.

CONCLUSION

This study reveals the marked changes that occur in the sleep of typically developing pre- and early adolescents. We discuss the potential implications of these trajectories.

The use of the sleep apnea-specific hypoxic burden to predict obstructive sleep apnea hypopnea syndrome: Evidence from a large cross-sectional study

OBJECTIVES

Obstructive sleep apnea hypopnea syndrome (OSA) is an independent risk factor for neurocognitive and behavioral problems and cardiovascular and metabolic morbidities, ultimately increasing mortality. However, OSA diagnosis is time-consuming, labor-intensive, and expensive. We evaluated the predictive utility of the sleep apnea-specific hypoxic burden (SASHB) in terms of OSA and the severity thereof in Han Chinese individuals.

METHODS

From January 2019 to July 2022, subjects with suspected OSA were recruited in the sleep center of the Shanghai Sixth People's Hospital during sleep evaluation via standard polysomnography. Basic anthropometric measurements and polysomnographic indicators were collected; SASHB was calculated based on the SpO2 trends of apnea or hypopnea events. Models predictive of OSA were established via logistic regression in the experimental group and verified in an independent group by drawing receiver operating characteristic (ROC) curves.

RESULTS

A total of 2303 subjects with suspected OSA (1200 in the experimental group and 1103 in the validation group) were included. SASHB was positively correlated with the apnea-hyponea index (AHI) in all subjects (r = 0.823, P < 0.001). SASHB distinguished OSA from non-OSA subjects in both the experimental group {area under the curve (AUC) 0.948 [0.934∼0.962]} and the validation group (AUC 0.931 [0.913∼0.949]). SASHB predicted OSA severity well, better than did the neck, waist, or hip circumference; the lowest or mean oxygen saturation; and the Epworth sleepiness scale score.

CONCLUSION

SASHB predicted OSA both accurately and efficiently in a Chinese Han population. Further studies are warranted to verify our findings in community samples.

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

Fabry disease and sleep disorders: a systematic review

Background

Fabry disease (FD) is an X-chromosome-linked disorder characterized by a reduced or complete absence of the enzyme α-galactosidase, resulting in the accumulation of lysosomal globotriaosylceramide. Despite the presence of these deposits in multiple organs, the problem of sleep disorders within this population has very rarely been documented.

Objective

This study aimed to investigate the types and prevalence of sleep disorders among patients with FD.

Methods

Screening of the following medical databases using key terms was performed on 10 February 2023: PubMed, Scopus, and Embase. A total of 136 records were identified. The quality assessment of the studies was conducted by using tools from the National Institutes of Health (NIH) and critical appraisal tools from the Joanna Briggs Institute (JBI).

Results

The study included nine studies on sleep disorders in patients with FD. The overall quality of the majority of these studies was assessed as either poor or fair. Among 330 patients, there was a slightly higher representation of female patients (56%). Sleep problems manifested 4-5 years after the onset of FD and sometimes even after 10-11 years. Genotypes of disease associated with sleep problems were rarely described. Within the FD population, the most commonly reported conditions were excessive daytime sleepiness (EDS) as well as obstructive and central sleep apnea (OSA, CSA). However, EDS occurred more frequently in FD patients, while the prevalence of OSA and CSA was within the ranges observed in the general population. The studies included indicated a lack of association between organ impairment by primary disease and EDS and OSA. The effectiveness of enzyme replacement therapy (ERT) in treating sleep disorders was not demonstrated.

Conclusion

The findings of this report revealed the presence of many sleep-related disorders within the FD population. However, very few studies on this subject are available, and their limited results make it difficult to truly assess the real extent of the prevalence of sleep disturbances among these individuals. There is a need to conduct further studies on this topic, involving a larger group of patients. It is important to note that there are no guidelines available for the treatment of sleep disorders in patients with FD.

Is camera recording crucial for the correct diagnosis of sleep bruxism in polysomnography?

Due to potential issues, several sleep laboratories conduct sleep bruxism diagnosis without the use of a camera, instead relying mostly on electromyographic monitoring. The purpose of the study was to determine if a camera is necessary for the polysomnographic evaluation of sleep bruxism and how sleep bruxism parameters change when a camera is not utilised. The study material was actual polysomnographic recordings made in the years 2017-2020. A total of 199 single-night video-polysomnographic recordings of patients who were suspected of having sleep bruxism were chosen at random. The first analysis involved the evaluation of the video recording, while in the second video recording was not used. The findings revealed that all the examined sleep bruxism indices had statistically significantly higher values for the recordings made without the use of a camera. According to the findings, noncamera recordings had decreased sensitivity and specificity when the criteria of bruxism and mild-to-moderate bruxism were taken into account, and decreased sensitivity in the case of the criterion of severe bruxism. Sleep efficiency, the apnea/hypopnea index, and arousals had independent effects on the bruxism episode index value in the analysis of noncamera recordings. In conclusion, camera-based polysomnography still appears to be the most reliable way to assess the severity of sleep bruxism, particularly in patients who also report other sleep disturbances. The presence of symptoms of sleep-disordered breathing, insomnia, or other sleep disorders should prompt us to use camera-based diagnostics.

A Comparison of Sleep Duration Accuracy Between Questionnaire and Accelerometer in Middle Childhood

Purpose Healthy sleep is vital to children's well-being, and assessing sleep efficiently and accurately can help understand children's lifestyles. Due to the difficulty in objectively measuring sleep duration using wearable sensors in large-scale surveys of children, self-administered questionnaires are often used in Japan; however, their accuracy is uncertain. We evaluated and compared the accuracy of questionnaire-based sleep times to those of wearable sensors. Methods This observational study was conducted between November 2019 and January 2020. A self-administered questionnaire on lifestyle habits and ActiGraph GT3X+ (ActiGraph, Inc., Pensacola, USA) accelerometer data were collected from 40 fourth-grade elementary school students in Kagawa Prefecture, Japan. We analyzed measurements for 256 days out of 280 days (40 persons × 7 days) after excluding days when the rate of wearing the accelerometer was < 90%. Results The median sleep duration per accelerometry was 453 minutes, and the median time in bed was 519 minutes. Questionnaire-based time in bed was 11 minutes longer, with relatively high inter-individual variability. The difference in bedtime was 26 minutes earlier, and wake-up time was 12 minutes earlier for the questionnaire. The average sleep efficiency was 87.4%, and one-third of the children had sleep efficiency < 85%. Conclusion The difference in sleep duration by questionnaire compared to accelerometry was approximately 10 minutes, suggesting the questionnaire may determine sleep duration with accuracy.

Impact of sleep chronotype on in-laboratory polysomnography parameters

Morningness-eveningness preference, also known as chronotype, is the tendency for a person to sleep during certain hours of the day and is broadly categorised into morning and evening types. In-laboratory polysomnography (iPSG) is the gold-standard to assess sleep, however, an individual's chronotype is not accounted for in current protocols, which may confound collected sleep data. The objective of our study was to assess if chronotype had an association with sleep physiology. Patients who completed the diagnostic iPSG and the Morningness-Eveningness Questionnaire (MEQ), which categorises patients into morning type, neither or evening type, were assessed. Multivariable linear regression models were used to assess if chronotype was associated with sleep quality, duration, and physiology during iPSG. The study sample included 2612 patients (mean age of 53.6 years, 48% male) recruited during 2010-2015. Morning type, compared with neither type, was significantly associated with an increase in total sleep time and rapid eye movement (REM) sleep, and a decrease in sleep onset latency and the arousal index. Evening type, compared with neither type, was significantly associated with a decrease in total sleep time, sleep efficiency, and REM sleep, and an increase in sleep onset latency and wake after sleep onset. Additionally, iPSG lights out time was significantly different between the different chronotypes. Overall, a morningness chronotype was associated with favourable sleep quality and duration while an eveningness chronotype was associated with reduced sleep quality. Our study quantifies the association of chronotype with iPSG metrics and suggests that laboratory protocols should consider chronotype in their evaluations.

SelANet: decision-assisting selective sleep apnea detection based on confidence score

BACKGROUND

One of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician.

METHOD

Our developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage.

RESULT

To confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction.

CONCLUSION

This algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.

Effects of resistance training on sleep of patients with fibromyalgia: A systematic review

This study investigated the effects of resistance training (RT) on the sleep quality of patients with fibromyalgia (FM), through a systematic review. The search was carried out in the PubMed, Embase, Scopus, and Web of Science databases. Initially, 148 studies were found, of which six were included for qualitative analysis. Four studies showed significant improvement in sleep after RT. Compared to other exercise modalities, RT proved to be superior to flexibility training and equivalent to aerobic exercise. Interventions lasted from 4 to 21 weeks, the weekly frequency ranged from two to three times, and the intensity ranged from 40% to 80% 1RM, with exercises for the major muscle groups. RT is an intervention that can be used to treat FM, however, more studies are needed to verify its effectiveness in improving sleep.

INSOMNet: Automated insomnia detection using scalogram and deep neural networks with ECG signals

Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.

Association of nocturnal sleep duration and nocturnal sleep changes with instrumental activities of daily living disability among middle-aged and elderly Chinese

OBJECTIVE

To investigate the association of baseline nocturnal sleep duration and sleep changes with functional disability in middle-aged and elderly Chinese.

METHODS

Data for this study were collected from the China Health and Retirement Longitudinal Study (CHARLS) from baseline (2011) to the Wave 3 follow-up (2018). 8361 participants free of IADL disability in 2011 and aged ≥ 45 years old were recruited and prospectively followed till 2018 to analyze the association between baseline nocturnal sleep duration and IADL disability. Of these 8361 participants, a total of 6948 participants had no IADL disability at the first three follow-up visits and completed the 2018 follow-up to analyze the association between nocturnal sleep changes and IADL disability. Nocturnal sleep duration (hours) was self-reported at baseline. The coefficient of variation (CV) of nocturnal sleep duration at baseline and three follow-up visits was used to calculate sleep changes and classified into mild, moderate, and severe degrees by the quantiles. Cox proportional hazards regression model was used to analyze the association of baseline nocturnal sleep duration with IADL disability, and the binary logistic regression model was used to analyze the association of nocturnal sleep changes with IADL disability.

RESULTS

Among the 8361 participants of 50237.5 person-years follow-up with a median follow-up of 7 years, 2158 (25.81%) participants developed IADL disabilities. Higher risks of IADL disability were observed among participants with sleep duration <7 h [HR(95%): 1.23(1.09-1.38)], 8∼<9 h [HR(95%): 1.05(1.00-1.32)] and ≥9 h [HR(95%): 1.21(1.01-1.45)] compared to those with 7∼<8 h. Among the 6948 participants, a total of 745 (10.72%) participants finally developed IADL disabilities. Compared with mild nocturnal sleep changes, moderate [OR(95%): 1.48(1.19-1.84)] and severe [OR(95%): 2.43(1.98-3.00)] sleep changes increased the probability of IADL disability. The restricted cubic spline model showed that a higher degree of nocturnal sleep changes was associated with a greater probability of IADL disability.

CONCLUSION

Both insufficient and excessive nocturnal sleep duration were associated with higher risk of IADL disability in middle-aged and elderly adults, independent of the participants' gender, age, and napping habits. Higher nocturnal sleep changes were associated with a higher probability of disability in IADL. These findings highlight the importance of appropriate and stable nocturnal sleep, and the need to pay attention to population differences in the impact of nocturnal sleep duration on health.

The Role of Emotion Regulation, Affect, and Sleep in Individuals With Sleep Bruxism and Those Without: Protocol for a Remote Longitudinal Observational Study

BACKGROUND

Sleep bruxism (SB) is an oral behavior characterized by high levels of repetitive jaw muscle activity during sleep, leading to teeth grinding and clenching, and may develop into a disorder. Despite its prevalence and negative outcomes on oral health and quality of life, there is currently no cure for SB. The etiology of SB remains poorly understood, but recent research suggests a potential role of negative emotions and maladaptive emotion regulation (ER).

OBJECTIVE

This study's primary aim investigates whether ER is impaired in individuals with SB, while controlling for affective and sleep disturbances. The secondary aim tests for the presence of cross-sectional and longitudinal mediation pathways in the bidirectional relationships among SB, ER, affect, and sleep.

METHODS

The study used a nonrandomized repeated-measures observational design and was conducted remotely. Participants aged 18-49 years underwent a 14-day ambulatory assessment. Data collection was carried out using electronic platforms. We assessed trait and state SB and ER alongside affect and sleep variables. We measured SB using self-reported trait questionnaires, ecological momentary assessment (EMA) for real-time reports of SB behavior, and portable electromyography for multinight assessment of rhythmic masticatory muscle activity. We assessed ER through self-reported trait questionnaires, EMA for real-time reports of ER strategies, and heart rate variability derived from an electrocardiography wireless physiological sensor as an objective physiological measure. Participants' trait affect and real-time emotional experiences were obtained using self-reported trait questionnaires and EMA. Sleep patterns and quality were evaluated using self-reported trait questionnaires and sleep diaries, as well as actigraphy as a physiological measure. For the primary objective, analyses will test for maladaptive ER in terms of strategy use frequency and effectiveness as a function of SB using targeted contrasts in the general linear model. Control analyses will be conducted to examine the persistence of the SB-ER relationship after adjusting for affective and sleep measures, as well as demographic variables. For the secondary objective, cross-sectional and longitudinal mediation analyses will test various competing models of directional effects among self-reported and physiological measures of SB, ER, affect, and sleep.

RESULTS

This research received funding in April 2017. Data collection took place from August 2020 to March 2022. In all, 237 participants were eligible and completed the study. Data analysis has not yet started.

CONCLUSIONS

We hope that the effort to thoroughly measure SB and ER using gold standard methods and cutting-edge technology will advance the knowledge of SB. The findings of this study may contribute to a better understanding of the relationship among SB, ER, affect, and sleep disturbances. By identifying the role of ER in SB, the results may pave the way for the development of targeted interventions for SB management to alleviate the pain and distress of those affected.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41719.

Development and validation of a sleep questionnaire, SNoRE 3.0, to evaluate sleep in companion dogs

Disturbances in the sleep-wake cycle are a debilitating, yet rather common condition not only in humans, but also in family dogs. While there is an emerging need for easy-to-use tools to document sleep alterations (in order to ultimately treat and/or prevent them), the veterinary tools which yield objective data (e.g. polysomnography, activity monitors) are both labor intensive and expensive. In this study, we developed a modified version of a previously used sleep questionnaire (SNoRE) and determined criterion validity in companion dogs against polysomnography and physical activity monitors (PAMs). Since a negative correlation between sleep time and cognitive performance in senior dogs has been demonstrated, we evaluated the correlation between the SNoRE scores and the Canine Dementia Scale (CADES, which includes a factor concerning sleep). There was a significant correlation between SNoRE 3.0 questionnaire scores and polysomnography data (latency to NREM sleep, ρ = 0.507, p < 0.001) as well as PAMs' data (activity between 1:00 and 3:00 AM, p < 0.05). There was a moderate positive correlation between the SNoRE 3.0 scores and the CADES scores (ρ = 0.625, p < 0.001). Additionally, the questionnaire structure was validated by a confirmatory factor analysis, and it also showed an adequate test-retest reliability. In conclusion the present paper describes a valid and reliable questionnaire tool, that can be used as a cost-effective way to monitor dog sleep in clinical settings.

Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic

A good night's sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict "sleep" and "awake" periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models-K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.

Characteristics of macroscopic sleep structure in patients with mild cognitive impairment: a systematic review

Objectives

Conducting a systematic analysis of objective measurement tools to assess the characteristics of macroscopic sleep architecture in patients with mild cognitive impairment (MCI), amnestic MCI (aMCI), and non-amnestic MCI (naMCI) in order to provide sleep disorder guidance for MCI patients.

Methods

PubMed, EMbase, Web of Science, Cochrane Library, CNKI, SinoMed, Wanfang Data, and VIP Data were examined to find literature relating to sleep in patients with MCI, aMCI, and naMCI, with a search time frame of build to April 2023. Following independent literature screening, data extraction, and quality evaluation by two researchers, statistical analysis was performed using RevMan 5.4 software.

Results

Twenty-five papers with 1,165 study subjects were included. Patients with MCI and aMCI were found to have altered total sleep time (TST), reduced sleep efficiency (SE), more wake-time after sleep onset (WASO), longer sleep latency (SL), a higher proportion of N1 stage and a lower proportion of N2 and N3 stage. naMCI was only found to have statistically significant differences in WASO.

Conclusions

The results of this study provide evidence for macroscopic sleep architecture abnormalities among MCI patients with sleep disorders. Maintaining a normal sleep time, improving SE, and reducing sleep fragmentation may have an association with a slowed development of cognitive impairment. Further exploration is required of the effects each component of macroscopic sleep structure after the intervention has on altered sleep disturbance and cognition in MCI, aMCI, and naMCI.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023401937, identifier: CRD42023401937.

Identifying an Appropriate Area to Facilitate the Cardiorespiratory Measurement during Sleep Monitoring

Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.

Developing customized NIRS-EEG for infant sleep research: methodological considerations

Significance

Studies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed.

Aim

We describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG.

Approach

N = 49 5- to 9-month-old infants participated. In phase 1, N = 26 (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N = 23 infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage.

Results

The infant nap rate during phase 1 was ∼ 40 % (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant.

Conclusions

We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants' comfort, longer sleep duration, and opportunities for caregiver-child interaction).

Fascia Ecosystem: A Step Forward in Sleep Engineering and Research

Millions suffer from sleep disorders, and sleep clinics and research institutions seek improved sleep study methods. This paper proposes the Fascia Ecosystem for Sleep Engineering to improve traditional sleep studies. The Fascia Sleep Mask is more comfortable and accessible than overnight stays at a sleep center, and the Fascia Portal and Fascia Hub allow for home-based sleep studies with real-time intervention and data analysis capabilities.A study of 10 sleep experts found that the Fascia Portal is easy to access, navigate, and use, with 44.4% finding it very easy to access, 33.3% very easy to navigate, and 60% very easy to get used to. Most experts found the Fascia Portal reliable and easy to use.Moreover, the study analyzed physiological signals during various states of sleep and wakefulness in two subjects. The results demonstrated that the Fascia dataset captured higher amplitude spindles in N2 sleep (72.20 V and 109.87 V in frontal and parietal regions, respectively) and higher peak-to-peak amplitude slow waves in N3 sleep (93.51 V) compared to benchmark datasets. Fascia produced stronger and more consistent EOG signals during REM sleep, indicating its potential to improve sleep disorder diagnosis and treatment by providing a deeper understanding of sleep patterns.

Feasibility of Exploiting Physiological and Motion Features for Camera-based Sleep Staging: A Clinical Study

Camera-based sleep monitoring is an emergent research topic in sleep medicine. The feasibility of using both the physiological features and motion features measured by a video camera for sleep staging was not thoroughly investigated. In this paper, we built a camera-based non-contact sleep monitoring setup in the Institute of Respiratory Diseases, Shenzhen People's Hospital, and created a clinical sleep dataset (nocturnal video data of 11 adults) including the expert-corrected PSG references synchronized with the video. The camera-based measurements have shown high correlations with the PSG. It obtains an overall Mean Absolute Error (MAE) of 1.5 bpm for heart-rate (HR), 0.7 bpm for breathing-rate (BR), 13.9 ms for heart-rate variability (HRV), and an accuracy of 93.5% for leg motion detection. The statistical analysis indicates that the averaged HR and variations of BR are distinct features for annotating four sleep stages (awake, REM, light sleep, and deep sleep). HRV parameter (SDNN) can clearly differentiate rapid-eye-movement (REM) and non-REM, while the leg movement is a distinctive feature for separating awake and sleep. The clinical trial demonstrated the feasibility of using physiological and motion features measured by a camera for joint sleep staging, and provides insights for sleep-related feature selection.

A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles

This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.

Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification

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/.

The potential of tRF-21-U0EZY9X1B plasmatic level as a biomarker of children with obstructive sleep apnea-hypopnea syndrome

PURPOSE

We investigated changes in plasma transfer RNA related fragments (tRF) in children with obstructive sleep apnea-hypopnea syndrome (OSAHS) and the potential value as a disease marker.

METHODS

Firstly, we randomly selected five plasma samples from the case group and the control group for high-throughput RNA sequencing. Secondly, we screened one tRF with different expression between the two groups, amplified it by quantitative reverse transcription-PCR (qRT-PCR) and sequenced the amplified product. After confirming that the qRT-PCR results were consistent with the sequencing results and the sequence of the amplified product contained the original sequence of the tRF, we performed qRT-PCR on all samples. Then we analyzed the diagnostic value of the tRF and its correlation with some clinical data.

RESULTS

A total of 50 OSAHS children and 38 control children were included in this study. There were significant differences in height, serum creatinine (SCR) and total cholesterol (TC) between the two groups. The plasma expression levels of tRF-21-U0EZY9X1B (tRF-21) were significantly different between the two groups. Receiver operating characteristic curve (ROC) showed that it had valuable diagnostic index, with area under the curve (AUC) of 0.773, 86.71% and 63.16% sensitivity and specificity.

CONCLUSIONS

The expression levels of tRF-21 in the plasma of OSAHS children decreased significantly which were closely related to hemoglobin, mean corpuscular hemoglobin, triglyceride and creatine kinase-MB, may become novel biomarkers for the diagnosis of pediatric OSAHS.

High- and low-intensity expiratory muscle strength training in patients with severe obstructive sleep apnea syndrome using non-invasive mechanical ventilation: A double-blinded, randomized controlled trial

BACKGROUND

The effects of expiratory muscle strength training (EMST) with different intensity on stages of sleep were not examined in patients with obstructive sleep apnea syndrome (OSAS).

OBJECTIVES

The aim of this study was to compare the effects of high- and low-intensity EMST (H-EMST and L-EMST) on disease severity, sleep stages, disease-related symptoms, daytime sleepiness, fatigue severity, and sleep quality in adult patients with severe OSAS using non-invasive mechanical ventilation (NIMV).

METHODS

Thirty-one clinically stable patients with severe OSAS were included in this prospective, randomized controlled, double-blinded study. These were randomly divided into two groups. The H-EMST group underwent training at 60% of maximum expiratory pressure (MEP) and the L-EMST group at 30% of MEP, seven days a week for eight weeks. Apnea hypopnea index (AHI), sleep stages, and respiratory sleep parameters were recorded using polysomnographic sleep analysis.

RESULTS

AHI decreased by 34.57% and 20.20% in the H-EMST and L-EMST groups, respectively. A statistically significant improvement in disease severity classifications distributions was observed in the H-EMST group after training (p = 0.016). A greater number of symptoms improved in the H-EMST. The effects of training on respiratory muscle strength, daytime sleepiness, fatigue perception, and sleep quality levels were comparable between the two groups (p>0.05).

CONCLUSIONS

H-EMST training is more effective in reducing disease severity and disease-related symptoms than L-EMST. Further studies are warranted for evaluating the long-term effects of EMST with larger sample sizes in OSAS.

Potential of Polyphenols for Improving Sleep: A Preliminary Results from Review of Human Clinical Trials and Mechanistic Insights

Global epidemiologic evidence supports an interrelationship between sleep disorders and fruits and vegetable ingestion. Polyphenols, a broad group of plant substances, are associated with several biologic processes, including oxidative stress and signaling pathways that regulate the expression of genes promoting an anti-inflammatory environment. Understanding whether and how polyphenol intake is related to sleep may provide avenues to improve sleep and contribute to delaying or preventing the development of chronic disease. This review aims to assess the public health implications of the association between polyphenol intake and sleep and to inform future research. The effects of polyphenol intake, including chlorogenic acid, resveratrol, rosmarinic acid, and catechins, on sleep quality and quantity are discussed to identify polyphenol molecules that may improve sleep. Although some animal studies have investigated the mechanisms underlying the effects of polyphenols on sleep, the paucity of trials, especially randomized controlled trials, does not allow for conducting a meta-analysis to reach clear conclusions about the relationships among these studies to support the sleep-improving effects of polyphenols.

MS-Net: Sleep apnea detection in PPG using multi-scale block and shadow module one-dimensional convolutional neural network

Sleep Apnea (SA) is a respiratory disorder that affects sleep. However, the SA detection method based on polysomnography is complex and not suitable for home use. The detection approach using Photoplethysmography is low cost and convenient, which can be used to widely detect SA. This study proposed a method combining a multi-scale one-dimensional convolutional neural network and a shadow one-dimensional convolutional neural network based on dual-channel input. The time-series feature information of different segments were extracted from multi-scale temporal structure. Moreover, shadow module was adopted to make full use of the redundant information generated after multi-scale convolution operation, which improved the accuracy and ensured the portability of the model. At the same time, we introduced balanced bootstrapping and class weight, which effectively alleviated the problem of unbalanced classes. Our method achieved the result of 82.0% average accuracy, 74.4% average sensitivity and 85.1% average specificity for per-segment SA detection, and reached 93.6% average accuracy for per-recording SA detection after 5-fold cross validation. Experimental results show that this method has good robustness. It can be regarded as an effective aid in SA detection in household use.

Sleep changes during a spontaneous manic episode: PSG assessment in a clinical context

Sleep plays a key role in the pathogenesis and clinical presentation of mood disorders. However, only a few studies have investigated sleep architecture during the manic episodes of Bipolar Disorder (BD) and changes in sleep parameters that follow clinical variations. Twenty-one patients (8 males, 13 females) affected by BD, manic phase, underwent polysomnographic recordings (PSG) at the beginning of the admission in our ward (T₀) and after three weeks of hospital treatment (T₁). All participants were clinically evaluated using Young Mania Rating Scale (YMRS), Pittsburgh Sleep Quality Index (PSQI) and Morningness-Eveningness Questionnaire (MEQ). During the admission, we observed an increase in both quantity (Total Sleep Time - TST) and quality (Sleep Efficiency - SE) of sleep. In addition, clinical improvement, evaluated with YMRS and PSQI scales, was accompanied by a significant increase in the percentage of REM sleep. According to our findings, the improvement of manic symptoms is accompanied by an increase in "REM pressure" (increase in REM% and REM density, reduction of REM latency). Overall, changes in sleep architecture appear to be markers sensitive to clinical variations during manic phases of Bipolar Disorder.

Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Efficacy of repetitive transcranial magnetic stimulation and agomelatine on sleep quality and biomarkers of adult patients with mild to moderate depressive disorder

BACKGROUND

Mild to moderate depressive disorder (DD), which accounts for much larger patient population, has been largely neglected in previous studies exploring the sleep quality of DD patients; in addition, most of these patients had comorbid insomnia. Thus, this study aimed to explore the effect of repetitive transcranial magnetic stimulation (rTMS) and agomelatine on sleep quality of adult patients with mild to moderate DD.

METHODS

100 participants were randomly divided into high-frequency rTMS group and sham rTMS group (n = 50 each). All patients were administered agomelatine simultaneously. Hamilton Depression Scale-17 Items (HAMD-17), Pittsburgh Sleep Index (PSQI), and polysomnography were used to evaluate the efficacy. Serum norepinephrine (NE), 5-hydroxytryptamine, brain-derived neurotrophic factor (BDNF), and melatonin were also determined.

RESULTS

The HAMD-17 and PSQI scores in high-frequency rTMS group were lower than those in sham rTMS group at the 4th and 8th weekend after treatment (P < 0.05). Post-treatment total sleep time, sleep efficiency, and N3 percentage in high-frequency rTMS group were better than those in sham rTMS group (P < 0.05); while post-treatment sleep latency, awakening time, micro-awakening times, and N1 percentage were significantly less than those in sham rTMS group (P < 0.01). Post-treatment serum levels of NE and BDNF in high-frequency rTMS group were higher than those in sham rTMS group (P < 0.05).

LIMITATIONS

Small sample size and short follow-up duration.

CONCLUSION

The combination of high-frequency rTMS and agomelatine is effective in the treatment of mild to moderate DD, which can improve the sleep quality and increase the levels of some neurotransmitters and neurotrophic factors.

No Difference in Sleep Desaturations Severity between Patients with Wake-Up and Non-Wake-Up Stroke: A PRESS Study Results

BACKGROUND

Wake-up stroke (WUS) is a certain type of ischemic stroke in which a patient wakes up with a new neurological deficit due to cerebral ischemia. Sleep-disordered breathing is an independent risk factor for stroke, but the role of nocturnal oxygen desaturation in the pathophysiology of WUS is still insufficiently explored. According to several studies, patients with WUS have a significantly more severe sleep apnea syndrome and lower mean blood oxygen saturation. This study aimed to assess the severity of nocturnal desaturations in acute WUS and non-WUS patients using nocturnal pulse oximetry.

MATERIAL AND METHODS

The cohort of 225 consecutive patients with neuroimaging-verified acute cerebral ischemia was prospectively enrolled. For further analyses, 213 subjects with known WUS/non-WUS status were selected (111 males and 102 females, average age 70.4 ±12.9, median baseline NIHSS = 5, median baseline mRS = 3). Patients were divided into the WUS group (n = 45) and the non-WUS group (n = 168). Overnight pulse oximetry was performed within 7 days of the stroke onset and data of both of the studied groups were compared.

RESULTS

We found oxygen desaturation index (ODI) in the WUS group was 14.5 vs. 16.6 (p = 0.728) in the non-WUS group, basal O2 saturation was 92.2% vs. 92.5% (p = 0.475), average low O2 saturation was 90.3% vs. 89.6% (p = 0.375), minimal O2 saturation was 79.5% vs. 80.6% (p = 0.563), and time with O2 saturation <90% (T90) was 4.4% vs. 4.7% (p = 0.729).

CONCLUSIONS

In the studied sample, monitored respiratory parameters including ODI, basal O2 saturation, average low O2 saturation, minimal O2 saturation, and T90 did not significantly differ between groups of WUS and non-WUS patients.

A Narrative Review on REM Sleep Deprivation: A Promising Non-Pharmaceutical Alternative for Treating Endogenous Depression

Endogenous depression represents a severe mental health condition projected to become one of the worldwide leading causes of years lived with disability. The currently available clinical and non-clinical interventions designed to alleviate endogenous depression-associated symptoms encounter a series of inconveniences, from the lack of intervention effectiveness and medication adherence to unpleasant side effects. In addition, depressive individuals tend to be more frequent users of primary care units, which markedly affects the overall treatment costs. In parallel with the growing incidence of endogenous depression, researchers in sleep science have discovered multiple links between rapid eye movement (REM) sleep patterns and endogenous depression. Recent findings suggest that prolonged periods of REM sleep are associated with different psychiatric disorders, including endogenous depression. In addition, a growing body of experimental work confidently describes REM sleep deprivation (REM-D) as the underlying mechanism of most pharmaceutical antidepressants, proving its utility as either an independent or adjuvant approach to alleviating the symptoms of endogenous depression. In this regard, REM-D is currently being explored for its potential value as a sleep intervention-based method for improving the clinical management of endogenous depression. Therefore, this narrative review represents a comprehensive inventory of the currently available evidence supporting the potential use of REM-D as a reliable, non-pharmaceutical approach for treating endogenous depression, or as an adjuvant practice that could improve the effectiveness of currently used medication.

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Consciousness is a multifaceted concept, involving both wakefulness, i.e., a condition of being alert that is regulated by the brainstem, and awareness, a subjective experience of any thoughts or perception or emotion. Recently, the European Academy of Neurology has published international guidelines for a better diagnosis of coma and other disorders of consciousness (DOC) through the investigation of sleep patterns, such as slow-wave and REM, and the study of the EEG using machine learning methods and artificial intelligence. The management of sleep disorders in DOC patients is an increasingly hot topic and deserves careful diagnosis, to allow for the most accurate prognosis and the best medical treatment possible. The aim of this review was to investigate the anatomo-physiological basis of the sleep/wake cycle, as well as the main sleep patterns and sleep disorders in patients with DOC. We found that the sleep characteristics in DOC patients are still controversial. DOC patients often present a theta/delta pattern, while epileptiform activity, as well as other sleep elements, have been reported as correlating with outcomes in patients with coma and DOC. The absence of spindles, as well as REM and K-complexes of NREM sleep, have been used as poor predictors for early awakening in DOC patients, especially in UWS patients. Therefore, sleep could be considered a marker of DOC recovery, and effective treatments for sleep disorders may either indirectly or directly favor recovery of consciousness.

A bibliometric analysis of the application of imaging in sleep in neurodegenerative disease

Objective

The purpose of this study was to examine the current state of the application of imaging in sleep research in degenerative disease, as well as hotspots and trends.

Materials and methods

A search was conducted on the Web of Science Core Collection (WoSCC) between 1 September 2012, and 31 August 2022 for literature related to sleep imaging. This study analyzed 7,679 articles published in this field over the past 10 years, using CiteSpace to analyze tendencies, countries, institutions, authors, and hotspots.

Results

There were 7,679 articles on the application of imaging to sleep research published by 566 institutions located in 135 countries in 1,428 journals; the number of articles was increasing on a yearly basis. According to keyword analysis, the research direction of the application of imaging in sleep research focused on the effects of degenerative diseases on sleep, such as Parkinson's disease, Alzheimer's disease, and small vessel disease. A literature evaluation found that Parkinson's disease, insomnia, sleep quality, and rapid eye movement sleep behavior disorder were the top research trends in this field.

Conclusion

A growing body of research has focused on sleep disorders caused by degenerative diseases. In the application of imaging to sleep research, magnetic resonance functional brain imaging represents a reliable research method. In the future, more aging-related diseases may be the subject of sleep-related research, and imaging could provide convenient and reliable evidence in this respect.

Conscious entry into sleep: Yoga Nidra and accessing subtler states of consciousness

Human sleep is a dynamic and complex process comprising sleep stages with REM and NREM sleep characteristics that come in cycles. During sleep, there is a loss of responsiveness or a perceptual loss of conscious awareness with increasing thresholds for wakefulness as sleep progresses. There are brief bursts of wakefulness or Wake After Sleep Onset (WASO) throughout a nocturnal sleep. Conscious experience during nocturnal sleep is known to occur during lucid dreaming when one is aware during dreams when the dream is occurring. Most cultures have known lucid dreaming since antiquity. However, conscious experience during dreamless sleep is relatively lesser known. Nevertheless, selected Indo-Tibetan meditation literature has documented it since antiquity. Minimal Phenomenal Experience (MPE) research describes lucid dreamless sleep as its target phenomenology. "Conscious entry into sleep" posits tonic alertness is maintained post sleep onset through the sleep stages for sustained durations of time until an eventual loss of conscious awareness. Entering sleep consciously and being aware during dreamless sleep, including Slow Wave Activity, is plausibly to be in the state of "Yoga Nidra" or Yogic sleep. An attentive sleepful state provides access to subtler states of consciousness and significantly deepens the levels of silence. It is phenomenologically distinct from hypnagogic hallucinations and lucid dreaming. Unfortunately, sleep studies validating this phenomenology are yet to be done. Therefore, an experimental methodology akin to those used in lucid dreaming experiments is described.

Effect of mini-dose dexmedetomidine supplemented intravenous analgesia on sleep structure in older patients after major noncardiac surgery: A randomized trial

STUDY OBJECTIVES

In previous studies, low-dose dexmedetomidine supplemented opioid analgesia improved sleep architecture but increased sedation level. Herein we tested the hypothesis that mini-dose dexmedetomidine supplemented analgesia improves sleep structure without increasing sedation.

METHODS

In this randomized trial, 118 older patients (≥65 years) following major noncardiac surgery were randomized to receive patient-controlled intravenous analgesia supplemented with either placebo or dexmedetomidine (median 0.02 μg kg^-1 h^-1) for up to 3 days. Polysomnogram was monitored from 9:00 p.m. on the day of surgery until 6:00 a.m. on the first day after surgery. Our primary outcome was the percentage of non-rapid eye movement stage 2 (N2) sleep. Secondary outcomes included other sleep structure parameters during the night of surgery and the sedation score during the first five postoperative days.

RESULTS

All 118 patients completed the study; of these, 85 were included in sleep structure analysis. Dexmedetomidine supplemented analgesia increased the percentage of N2 sleep (median difference, 10%; 95% CI, 1%-20%; P = 0.03). It also prolonged total sleep time (median difference, 78 min; 95% CI, 21 to 143; P = 0.01), increased sleep efficiency (median difference, 14%; 95% CI, 4%-26%; P = 0.01), decreased percentage of N1 sleep (median difference, -10%; 95% CI, -20% to -1%; P = 0.04), and lowered sleep fragmentation index (median difference, -1.6 times⋅h^-1; 95% CI, -3.7 to 0.1; P = 0.04). Sedation score within 5 days did not differ between the two groups.

CONCLUSIONS

Supplementing intravenous analgesia with mini-dose dexmedetomidine improved sleep structure without increasing sedation in older patients recovering from major surgery.

CLINICAL TRIALS

www.

CLINICALTRIALS

gov (NCT03117790), registered 2 April 2017.

Dynamic assessment of oropharynx with ultrasonography as a screening tool for obstructive sleep apnea

Ultrasonography is an easily available and portable tool to assess the dynamic changes in the upper airway and surrounding soft tissue. This study aimed to evaluate the utility of oropharynx ultrasonography as a screening tool for obstructive sleep apnea (OSA). The study sequentially enrolled overweight individuals (body mass index >25 kg/m² ) and subjected them to OSA screening tools (Berlin questionnaire, Epworth Sleepiness Scale and STOP-Bang scores), ultrasonography of the oropharynx followed by overnight polysomnography. A total of 30 healthy individuals were also recruited as controls. Detailed dynamic and static ultrasonography measurements of the oropharynx and surrounding tissue were done. The diagnostic ability of various ultrasonography parameters to detect OSA was determined using receiver operating characteristic curve analysis. A total of 63 subjects were enrolled, with 33 in the OSA group and 30 in non-OSA overweight group. All baseline characteristics were similar in the two groups. Except for the dynamic measurements of oropharynx (Retropalatal% change-inspiration, retropalatal% change-Muller manoeuvre, retroglossal% change-inspiration, and retroglossal% change-Muller manoeuvre) all other parameters were similar in the OSA and non-OSA overweight subjects. The area under the receiver operating characteristic curve was highest for retropalatal% change-inspiration: 0.989, followed by retropalatal% change-Muller manoeuvre: 0.988. Both were also significant predictors of OSA with odds ratios of 0.338 (p = 0.003; 95% confidence interval [CI] 0.164-0.696) and 0.346 (p = 0.018; 95% CI 0.143-0.837), respectively. Ultrasonography provides a near complete picture of the dynamic changes and collapsibility of the oropharynx and can be an effective tool in screening for OSA.

Central Sleep Apnea Detection by Means of Finger Photoplethysmography

Obstructive Sleep Apnea (OSA) and Central Sleep Apnea (CSA) are two types of Sleep Apnea (SA) with different etiologies and treatment options. Home sleep apnea testing based on photoplethysmography-derived peripheral arterial tonometry (PAT HSAT) has become the most widely deployed outpatient SA diagnostic method. Being able to differentiate between CSA and OSA based solely on photoplethysmography-data would further increase PAT HSAT's clinical utility. The present work proposes a method to detect CSA using finger photoplethysmography (PPG) data and evaluates the proposed method against simultaneous in-lab polysomnography (PSG).

METHODS

For 266 patients with a suspicion of SA, concurrent in-lab PSG and PPG data were acquired. The respiratory information embedded in the PPG data was extracted and used to train an ensemble of trees classifiers that predicts the central or obstructive nature of each respiratory event. The classifier performance was evaluated using patient-wise leave-one-out cross-validation where an expert analysis of the PSG served as ground truth. A second, independent analysis of the PSG was also evaluated against the ground truth to allow benchmarking of the PPG-based method.

RESULTS

The method achieved a sensitivity of 81%, a specificity of 99%, a positive predictive value of 90%, and a negative predictive value of 98% at the central apnea-hypopnea index cutoff of 10 events per hour of sleep.

CONCLUSION AND SIGNIFICANCE

The present study aimed to evaluate a method to detect CSA in SA patients using only PPG data which could be used to flag CSA which in turn may aid in more optimal therapy decision making.

A literature review of sleep problems and neurodevelopment disorders

INTRODUCTION

Sleep is an incredibly complex process that goes beyond relaxing and body resting. Disturbance in sleep leads to several short-term and long-term consequences. Neurodevelopmental diseases such as "autism spectrum disorder" (ASDs), Attention-deficit hyperactivity disorder (ADHD), and intellectual disability commonly experience sleep disorders that affect their clinical presentation, daily function, and quality of life.

DISCUSSION

The incidence of sleep problems in ASD patients ranges from 32 to 71.5%, especially insomnia, while an estimated 25-50% of people with ADHD report having sleep issues in clinical settings. The incidence of sleep issues is widespread in persons with intellectual disabilities, reaching up to 86%. This article is a literature review covering the neurodevelopmental disorder interaction with sleep disorder and different management.

CONCLUSION

Disorders of sleep are key concerns in children with neurodevelopmental disorders. In this group of patients, sleep disorders are common and tend to be chronic. Recognizing and diagnosis of sleep disorders will enhance their function, response to treatment, and quality of life.