Polysomnography

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home.

Multiple Allergic Rhinitis Single Nucleotide Polymorphism Variants are Associated with Sleep-Breathing Parameters in Men with Obstructive Sleep Apnea: A Large-Scale Study

Background

Sleep-disordered breathing is more prevalent in individuals with allergic rhinitis (AR) than in those without AR. In addition to increased risk for sleep-disordered breathing, AR is associated with greater severity of obstructive sleep apnea (OSA) symptoms. The aim of this research study was to evaluate the association of multiple single nucleotide polymorphism (SNP) variations in AR with sleep- and breathing-related parameters in men with OSA.

Methods

Men who had complained of snoring were consecutively enrolled in the Shanghai Sleep Health Study of Shanghai Sixth People's Hospital from 2007 to 2018. After rigorous screening, 5322 men were included in the analysis. Anthropometric, fasting biochemical, and polysomnographic parameters, along with 27 AR-associated SNPs were analyzed. The associations between AR-related genetic polymorphisms and OSA were determined via linear, binary, and multinomial logistic regression analyses.

Results

Rs12509403 had significantly positive associations with most sleep-breathing parameters. While the risk for OSA was increased by rs12509403, it was decreased by rs7717955 [odds ratio (OR) = 1.341, 95% confidence interval [CI] = 1.039-1.732, P = 0.024; OR = 0.829, 95% CI = 0.715-0.961, P = 0.013, respectively]. A graded increase in the risk of being in the highest quartile (Q4) vs the reference category (Q1) for sleep breathing indicators, especially REM-AHI and NREM-AHI, was identified by rs12509403 (OR = 1.496, 95% CI = 1.175-1.904, P = 0.001; OR = 1.471, 95% CI = 1.151-1.879, P < 0.001, respectively).

Conclusion

The association of multiple AR SNPs with OSA-related hypoxia and sleep indices provides a genetic explanation for the higher AR susceptibility of OSA patients. Understanding the AR-related genetic underpinnings of OSA may lead to more personalized treatment approaches.

A novel deep learning model based on transformer and cross modality attention for classification of sleep stages

The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspective on sleep patterns, can have a great impact on the efficiency of the classification models. In the context of neural networks and deep learning models, transformers are very effective, especially when dealing with time series data, and have shown remarkable compatibility with sequential data analysis as physiological channels. On the other hand, cross-modality attention by integrating information from multiple views of the data enables to capture relationships among different modalities, allowing models to selectively focus on relevant information from each modality. In this paper, we introduce a novel deep-learning model based on transformer encoder-decoder and cross-modal attention for sleep stage classification. The proposed model processes information from various physiological channels with different modalities using the Sleep Heart Health Study Dataset (SHHS) data and leverages transformer encoders for feature extraction and cross-modal attention for effective integration to feed into the transformer decoder. The combination of these elements increased the accuracy of the model up to 91.33% in classifying five classes of sleep stages. Empirical evaluations demonstrated the model's superior performance compared to standalone approaches and other state-of-the-art techniques, showcasing the potential of combining transformer and cross-modal attention for improved sleep stage classification.

Individual-Level Experiences of Structural Inequity and Their Association with Subjective and Objective Sleep Outcomes in the Adolescent Brain Cognitive Development Study

PURPOSE

Research has documented that adolescent sleep is impacted by various stressors, including interpersonal experiences and structural disadvantage. This study extends existing knowledge by empirically examining interconnected individual experiences of structural inequity and assessing its association with subjective and objective sleep outcomes.

METHODS

We utilized data from the Adolescent Brain and Cognitive Development Study to identify seven conceptual domains of structural inequity: perceived discrimination, low school inclusivity, neighborhood safety, unmet medical needs, legal problems, material hardship, and housing insecurity. We operationalized experiences of structural inequity as latent classes, a cumulative exposure, and each domain separately. Sleep disturbances were measured using the Sleep Disturbance Scale, and sleep duration was assessed using Fitbits. Mixed effects linear regression estimated the association between our measures of structural inequity, longitudinal sleep disturbances, and cross-sectional sleep duration.

RESULTS

Latent class analysis revealed common exposure profiles (low risk, interpersonal, and systemic) of experiences of structural inequity across our sample. In longitudinal models, structural inequity was associated with higher Sleep Disturbance Scale scores, whether measured as latent classes, a cumulative exposure, or individual domains. Individuals with interpersonal exposures, those with at least one exposure, and those with legal problems, material hardship, and housing insecurity had lower mean sleep duration.

DISCUSSION

Results are consistent with literature that frames structural inequity as a lifelong determinant of sleep disturbance and duration. Adolescence represents a crucial time for interventions aimed at improving sleep and redressing inequities throughout the life course; our work can inform the development of policies and interventions toward this end.

Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

Risk of Obstructive Sleep Apnea, Chronic Dizziness, and Sleep Duration

BACKGROUND

Although it is recommended that obstructive sleep apnea (OSA) be screened for using a validated self-report questionnaire in patients experiencing dizziness, there is still a lack of research on the relationship between high risk of OSA and chronic dizziness.

OBJECTIVES

The study aimed to examine the relationship between the high risk of OSA and chronic dizziness and investigate how this relationship is affected by sleep duration.

METHODS

This cross-sectional study used data from the 8th Korea National Health and Nutrition Examination Survey (2019-2021). Adults aged 40 years or older were included and divided into two groups using the STOP-Bang Questionnaire (SBQ): a high-risk group for OSA or not. Complex samples logistic regression analyses were performed to examine the odds ratios of chronic dizziness based on the national population estimates.

RESULTS

Our findings showed that individuals in the high-risk group for OSA were significantly more likely to experience chronic dizziness. Specifically, among subgroups based on sleep duration, the high-risk group for OSA with a short sleep duration of ≤5 hours demonstrated the highest odds of chronic dizziness, showing a significantly 2.48-fold increased likelihood compared to the non-high risk for OSA with a sleep duration of 5-9 hours.

DISCUSSION

The SBQ can be beneficial when other causes do not explain chronic dizziness, helping to rule in the possibility of OSA. Educating individuals suspected of having OSA or who have been diagnosed with OSA about the importance of adequate sleep duration may help reduce the risk of chronic dizziness.

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

A scoping review of the effect of chronic stretch training on sleep quality in people with sleep disorders

PURPOSE

The widespread and health-detrimental sleep disorders have resulted in stretching exercises being investigated as a non-drug solution for enhanced sleep quality. However, a comprehensive understanding of the impact of stretching exercises on individuals with sleep disorders is lacking.

METHODS

This scoping review systematically maps the existing literature and identifies research gaps on the impact of stretching exercises on sleep quality in individuals with sleep disorders.

RESULTS

Sixteen eligible studies were included, where the weighted mean changes indicate a positive trend in sleep quality improvement, ranging from trivial to very large magnitudes. However, concerning the individual study results only 5 out of 16 studies reported significant improvements. Notable enhancements include a small 1.22% overall sleep quality improvement, a large 6.51% reduction in insomnia severity, a large 8.88% increase in sleep efficiency, a moderate 4.36% decrease in sleep onset latency, a large 8.27% decrease in wake after sleep onset, and a very large 14.70% improvement in total sleep time. Trivial changes are noted in sleep duration (0.58%), sleep disturbance reduction (0.07%), and daytime dysfunction reduction (0.19%). Likely mechanisms for the improvement of sleep include autonomic nervous system modulation, muscle tension relief, cortisol regulation, enhanced blood circulation, and psychological benefits such as stress reduction and mood enhancement.

CONCLUSION

There is little evidence that stretching exercises positively impact sleep quality in individuals with sleep disorders. Additionally, further research is vital for designing optimal protocols, understanding of the long-term effects, and clarification of the mechanisms.

Association of total sleep duration variability with risk of new stroke in the middle-aged and elderly Chinese population

OBJECTIVE

To investigate the association between total sleep duration variability and stroke in the middle-aged and elderly population in China.

METHODS

Data were collected from the 2011, 2013, 2015, and 2018 surveys of the China Health and Retirement Longitudinal Study (CHARLS). A total of 3485 participants, who had not experienced a stroke until 2015 and completed the follow-up in 2018, were enrolled to analyze the relationship between total sleep duration variability and new stroke. Total sleep duration was calculated by summing self-reported nocturnal sleep duration and daytime napping. The variability was determined by calculating the standard deviation (SD) of total sleep duration across the first three waves. A binary logistic regression model was utilized to analyze this association.

RESULTS

Of the 3485 participants, 183 (5.25%) sustained a stroke event. A dose-response relationship was observed, indicating an increased stroke risk of 0.2 per unit (hours) increase in total sleep duration variability [OR (95% CI): 1.20 (1.01-1.42)]. Upon stratification by sex groups, this increased risk was significant only in men [OR (95% CI): 1.44 (1.12-1.83)].

CONCLUSION

Increased total sleep duration variability was associated with an increased risk of stroke in the middle-aged and elderly, independent of factors such as age, nocturnal sleep duration, napping habits, region of residence, hypertension, diabetes mellitus, dyslipidemia, BMI, smoking, drinking habits, and marital status. However, a more notable correlation was observed in males.

Application of non-contact sensors for health monitoring in hospitals: a narrative review

The continuous monitoring of the health status of patients is essential for the effective monitoring of disease progression and the management of symptoms. Recently, health monitoring using non-contact sensors has gained interest. Therefore, this study aimed to investigate the use of non-contact sensors for health monitoring in hospital settings and evaluate their potential clinical applications. A comprehensive literature search was conducted using PubMed to identify relevant studies published up to February 26, 2024. The search terms included "hospital," "monitoring," "sensor," and "non-contact." Studies that used non-contact sensors to monitor health status in hospital settings were included in this review. Of the 38 search results, five studies met the inclusion criteria. The non-contact sensors described in the studies were radar, infrared, and microwave sensors. These non-contact sensors were used to obtain vital signs, such as respiratory rate, heart rate, and body temperature, and were then compared with the results from conventional measurement methods (polysomnography, nursing records, and electrocardiography). In all the included studies, non-contact sensors demonstrated a performance similar to that of conventional health-related parameter measurement methods. Non-contact sensors are expected to be a promising solution for health monitoring in hospital settings.

Revealing inconsistencies between Epworth scores and apnea-hypopnea index when evaluating obstructive sleep apnea severity: a clinical retrospective chart review

Introduction

A common practice in clinical settings is the use of the Epworth Sleepiness Scale (ESS) and apnea-hypopnea index (AHI) to demonstrate the severity of obstructive sleep apnea (OSA). However, several instances were noted where there were discrepancies in the reported severity between Epworth scores and AHI in our patient sample, prompting an investigation into whether OSA severity as demonstrated by AHI or predicted by ESS quantification of sleepiness is primarily responsible for inconsistencies.

Methods

Discrepancies were examined between Epworth scores and AHI by categorizing patients into two categories of inconsistency: individuals with either ESS < 10 and AHI ≥ 15 events/h or ESS ≥ 10 and AHI < 15 events/h. The potential influence of sex on these categories was addressed by assessing whether a significant difference was present between mean Epworth scores and AHI values for men and women in the sample. We investigated BMI both by itself as its own respective variable and with respect to the sex of the individuals, along with a consideration into the role of anxiety. Furthermore, we tested anxiety with respect to sex.

Results

In the first category of inconsistency the average ESS of 5.27 ± 0.33 suggests a normal level of daytime sleepiness. However, this contrasts with the average AHI of 32.26 ± 1.82 events/h which is indicative of severe OSA. In the second category the average ESS of 14.29 ± 0.47 suggests severe daytime sleepiness, contradicting the average AHI of 9.16 ± 0.44 events/h which only indicates mild OSA. Sex, BMI (both as a variable by itself and with respect to sex), and anxiety (both as a variable by itself and with respect to sex) contributed to observed inconsistencies.

Conclusion

The findings of our study substantiate our hypothesis that Epworth scores should be de-emphasized in the assessment of OSA and a greater importance should be placed on measures like AHI. While Epworth scores offer insights into patients' daytime sleepiness levels and the perceived severity of their OSA, the inconsistencies highlighted in our results when compared to AHI-based OSA severity underscore their potential inaccuracy. Caution is advised when utilizing Epworth scores for evaluating OSA severity in clinical settings.

Concussion and the Sleeping Brain

BACKGROUND

Emerging research has suggested sleep to be a modifier of the trajectory of concussion recovery in adolescent and adult populations. Despite the growing recognition of the relationship between sleep and concussion, the mechanisms and physiological processes governing this association have yet to be established.

MAIN BODY

Following a concussion, a pathophysiologic cascade of events occurs, characterized by numerous factors including microglia activation, ionic imbalance, and release of excitatory neurotransmitters. Importantly, each of these factors plays a role in the regulation of the sleep-wake cycle. Therefore, dysregulation of sleep following injury may be a function of the diffuse disruption of cerebral functioning in the wake of both axonal damage and secondary physiological events. As the onset of sleep-related symptoms is highly variable following a concussion, clinicians should be aware of when and how these symptoms present. Post-injury changes in sleep have been reported in the acute, sub-acute, and chronic phases of recovery and can prolong symptom resolution, affect neurocognitive performance, and influence mood state. Though these changes support sleep as a modifier of recovery, limited guidance exists for clinicians or their patients in the management of sleep after concussion. This may be attributed to the fact that research has correlated sleep with concussion recovery but has failed to explain why the correlation exists. Sleep is a complex, multifactorial process and the changes seen in sleep that are seen following concussion are the result of interactions amongst numerous processes that regulate the sleep-wake cycle.

SHORT CONCLUSION

The assessment and management of sleep by identifying and considering the biological, sociological, and psychological interactions of this multifactorial process will allow for clinicians to address the dynamic nature of changes in sleep following concussion.

Sleep and sleep-related breathing disorders in patients with spinal muscular atrophy: a changing perspective from novel treatments?

Spinal Muscular Atrophy (SMA) is an inherited neuromuscular disorder characterized by progressive muscle weakness and atrophy, resulting from the degeneration of motor neurons in the spinal cord. A critical aspect of SMA is its impact on respiratory function. As the disease progresses, respiratory muscles, in particular intercostal muscles, become increasingly affected, leading to breathing difficulties and respiratory failure. Without intervention, many children with SMA type 1 die from respiratory failure before their second year of life. While assisted ventilation has improved survival, it often results in ventilator dependence. The development of new SMN-augmenting therapies has renewed optimism, but their long-term impact on respiratory function is uncertain, and non-invasive respiratory support remains an important part of SMA management. Despite the importance of respiratory support in SMA, knowledge regarding sleep disorders in this population is limited. This review aims to synthesize existing literature on sleep and sleep-related breathing disorders in patients with SMA, with a focus on SMA type 1. We summarize evidence of sleep-disordered breathing and respiratory failure in SMA, as well as outcomes and survival benefits associated with non-invasive or invasive ventilation with or without pharmacological therapies. We also discuss current knowledge regarding the effects of novel disease-modifying therapies for SMA on respiratory function and sleep. In conclusion, optimal care for children with SMA requires a multidisciplinary approach that includes neurology and respiratory specialists. This review highlights the importance of monitoring sleep and respiratory function in SMA, as well as the potential benefits and challenges associated with assisted ventilation combined with new therapies.

A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals

Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.

Intermittent Versus Sustained Hypoxemia from Sleep-disordered Breathing: Outcomes in Patients with Chronic Lung Disease and High Altitude

In a variety of physiologic and pathologic states, people may experience both chronic sustained hypoxemia and intermittent hypoxemia ("combined" or "overlap" hypoxemia). In general, hypoxemia in such instances predicts a variety of maladaptive outcomes, including excess cardiovascular disease or mortality. However, hypoxemia may be one of the myriad phenotypic effects in such states, making it difficult to ascertain whether adverse outcomes are primarily driven by hypoxemia, and if so, whether these effects are due to intermittent versus sustained hypoxemia.

Reduced coupling between global signal and cerebrospinal fluid inflow in patients with depressive disorder: A resting state functional MRI study

BACKGROUND

Depressed patients often suffer from sleep disturbance, which has been recognized to be responsible for glymphatic dysfunction. The purpose of this study was to investigate the coupling strength of global blood‑oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) inflow dynamics, which is a biomarker for glymphatic function, in depressed patients and to explore its potential relationship with sleep disturbance by using resting-state functional MRI.

METHODS

A total of 138 depressed patients (112 females, age: 34.70 ± 13.11 years) and 84 healthy controls (29 females, age: 36.6 ± 11.75 years) participated in this study. The gBOLD-CSF coupling strength was calculated to evaluate glymphatic function. Sleep disturbance was evaluated using the insomnia items (item 4 for insomnia-early, item 5 for insomnia-middle, and item 6 for insomnia-late) of The 17-item Hamilton Depression Rating Scale for depressed patients, which was correlated with the gBOLD-CSF coupling strength.

RESULTS

The depressed patients exhibited weaker gBOLD-CSF coupling relative to healthy controls (p = 0.022), possibly due to impairment of the glymphatic system. Moreover, the gBOLD-CSF coupling strength correlated with insomnia-middle (r = 0.097, p = 0.008) in depressed patients. Limitations This study is a cross-sectional study.

CONCLUSION

Our findings shed light on the pathophysiology of depression, indicating that cerebral waste clearance system deficits are correlated with poor sleep quality in depressed patients.

Home Sleep Testing versus Traditional Polysomnography: Pros and Cons

Obstructive sleep apnea (OSA) is associated with long-term cardiovascular and respiratory comorbidities and increased burden on the health-care system. Early and accurate diagnosis is essential to reduce physical and financial implications of the disease. Polysomnography uses neurophysiologic channels as well as basic respiratory and sleep parameters to best estimate the presence and/or severity of OSA. Although home sleep testing may have the potential for more variable results, it is a viable alternative to increase access to diagnosis of OSA and facilitate initiation of positive airway pressure.

Interhemispheric differences of electroencephalography signal characteristics in different sleep stages

OBJECTIVE

The current electroencephalography (EEG) measurement setup is complex, laborious to set up, and uncomfortable for patients. We hypothesize that differences in EEG signal characteristics for sleep staging between the left and right hemispheres are negligible; therefore, there is potential to simplify the current measurement setup. We aimed to investigate the technical hemispheric differences in EEG signal characteristics along with electrooculography (EOG) signals during different sleep stages.

METHODS

Type II portable polysomnography (PSG) recordings of 50 patients were studied. Amplitudes and power spectral densities (PSDs) of the EEG and EOG signals were compared between the left (C3-M2, F3-M2, O1-M2, and E1-M2) and the right (C4-M1, F4-M1, O2-M1, and E2-M2) hemispheres. Regression analysis was performed to investigate the potential influence of sleep stages on the hemispheric differences in PSDs. Wilcoxon signed-rank tests were also employed to calculate the effect size of hemispheres across different frequency bands and sleep stages.

RESULTS

The results showed statistically significant differences in signal characteristics between hemispheres, but the absolute differences were minor. The median hemispheric differences in amplitudes were smaller than 3 μv with large interquartile ranges during all sleep stages. The absolute and relative PSD characteristics were highly similar between hemispheres in different sleep stages. Additionally, there were negligible differences in the effect size between hemispheres across all sleep stages.

CONCLUSIONS

Technical signal differences between hemispheres were minor across all sleep stages, indicating that both hemispheres contain similar information needed for sleep staging. A reduced measurement setup could be suitable for sleep staging without the loss of relevant information.

Functional Exercise Capacity and Perceived Exertion in Patients with Empty Nose Syndrome

Empty nose syndrome (ENS) is a complex condition characterized by symptoms such as dyspnea, nasal discomfort, and emotional challenges. This study aimed to evaluate functional exercise capacity and perceived exertion in patients with ENS. Patients with ENS who presented with a range of severe symptoms were prospectively enrolled. Pulmonary function was evaluated using spirometry, and functional exercise capacity was measured via the 6 min walk test (6-MWT). Perceived exertion was quantified using the Borg scale, and cardiopulmonary function was evaluated by monitoring peripheral oxygen saturation (SpO2). These parameters were assessed before and after nasal reconstruction surgery. A total of 44 patients with ENS were enrolled and classified into mild-to-moderate (n = 20) and severe (n = 24) symptom groups. Spirometry results showed no significant differences before and after surgery in the entire cohort. Perceived exertion showed significant postoperative improvement (p = 0.006). The severe ENS symptom group experienced significant improvement in SpO2 (p = 0.013) and perceived exertion (p = 0.002) at the end of the 6-MWT after surgery. Surgical intervention significantly enhanced functional exercise capacity (p = 0.038) in patients with mild-to-moderate ENS symptoms. Surgical reconstruction positively affected perceived exertion and SpO2 at the end of the 6-MWT in patients with ENS. The severity of ENS symptoms, as assessed by SNOT-25 scores, influenced these outcomes. These findings underscore the potential benefits of surgical intervention for enhancing exercise tolerance and respiratory efficiency.

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

OBJECTIVE

Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.

METHODS AND PROCEDURES

The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches.

RESULTS

Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process.

CONCLUSION

Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques.

CLINICAL IMPACT

An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

Effectiveness of multi-modal home-based videoconference interventions on sleep in older adults: study protocol for a randomized controlled trial

Aging is characterized by substantial changes in sleep architecture that negatively impact fitness, quality of life, mood, and cognitive functioning. Older adults often fail to reach the recommended level of physical activity to prevent the age-related decline in sleep function, partly because of geographical barriers. Implementing home-based interventions could surmount these obstacles, thereby encouraging older adults to stay active, with videoconference administration emerging as a promising solution. Increasing the availability of biological rhythms synchronizers, such as physical activity, light exposure, or vestibular stimulation, represents a viable non-pharmacological strategy for entraining circadian rhythms and potentially fortifying the sleep-wake cycle, thereby enhancing sleep in aging. This study aims to (1) assess the impact of remote physical exercise training and its combination with bright light exposure, and (2) investigate the specific contribution of galvanic vestibular stimulation, to sleep quality among healthy older adults with sleep complaints. One hundred healthy older adults aged 60-70 years with sleep complaints will be randomly allocated to one of four groups: a physical exercise training group (n = 25), a physical exercise training combined with bright light exposure group (n = 25), a galvanic vestibular stimulation group (n = 25) or a control group (i.e., health education) (n = 25). While physical exercise training and health education will be supervised via videoconference at home, bright light exposure (for the physical exercise training combined with bright light exposure group) and vestibular stimulation will be self-administered at home. Pre-and post-tests will be conducted to evaluate various parameters, including sleep (polysomnography, subjective questionnaires), circadian rhythms (actigraphy, temperature), fitness (physical: VO2 peak, muscular function; and motor: balance, and functional mobility), cognition (executive function, long-term memory), quality of life and mood (anxiety and depression). The findings will be anticipated to inform the development of recommendations and non-pharmaceutical preventive strategies for enhancing sleep quality in older adults, potentially leading to improvements in fitness, cognition, quality of life, and mood throughout aging.

Efficacy of a fish hydrolysate supplement on sleep quality: A randomized, double-blind, placebo-controlled, crossover clinical trial

BACKGROUND & AIMS

Sleep disturbances are widespread in modern societies and linked to a variety of diseases, creating an urgent need for the development of products that help combat sleep difficulties. One suitable nutritional supplement may be a fish hydrolysate composed of low molecular weight peptides.

METHODS

This two-arm, double-blind, randomized, placebo-controlled crossover study investigated the effect of a 4-week fish hydrolysate intervention on sleep in a healthy German population reporting poor sleep quality, assessed with the Pittsburgh Sleep Quality Index (PSQI). Further sleep parameters were measured using an online diary and a wrist wearable device. Additionally, questionnaires related to stress, anxiety, depression, and well-being were evaluated and salivary cortisol and product satisfaction were assessed.

RESULTS

The 4-week fish hydrolysate supplementation significantly improved subjective sleep quality measured with the PSQI-score (p = .002). Moreover, individuals reported improvements in sleep efficacy and a reduction in sleep disturbances and daytime sleepiness during fish hydrolysate intake (p = .013, p = .046, p = .004 respectively), but not during placebo phase (all p > .05). No significant intra-individual differences were found between fish hydrolysate and placebo supplementation (p > .05).

CONCLUSIONS

Although no significant intra-individual differences were found between fish hydrolysate and placebo supplementation, the significant improvement in subjective sleep quality from baseline to treatment phase suggests that fish hydrolysate is a safe nutritional supplement to support individuals with self-reported sleep problems.

CLINICAL TRIAL REGISTRATION

The study is registered at ClinicalTrials.gov with the Identifier NCT04983355.

The Association Between the 5-Hydroxytryptamine Receptor 2A Gene Variants rs6311 and rs6313 and Obstructive Sleep Apnea in the Iranian Kurdish Population

Introduction: Sleep is one of the most significant parts of everyone's life. Most people sleep for about one-third of their lives. Sleep disorders negatively impact the quality of life. Obstructive sleep apnea (OSA) is a severe sleep disorder that significantly impacts the patient's life and their family members. This study aimed to investigate the relationship between rs6313 and rs6311 polymorphisms in the serotonin receptor type 2A gene and OSA in the Kurdish population. Materials and Methods: The study's population comprises 100 OSA sufferers and 100 healthy people. Polysomnography diagnostic tests were done on both the patient and control groups. The polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) was used to investigate the relationship between OSA and LEPR gene polymorphisms. Results: Statistical analysis showed a significant relationship between genotype frequencies of patient and control groups of rs6311 with OSA in dominant [odds ratio (OR) = 5.203, p < 0.001) and codominant models (OR = 9.7, p < 0.001). Also, there was a significant relationship between genotype frequencies of patient and control groups of rs6313 with OSA in dominant (OR = 10.565, p < 0.001) and codominant models (OR = 5.938, p < 0.001). Conclusions: Findings from the study demonstrated that the two polymorphisms rs6311 and rs6313 could be effective at causing OSA; however, there was no correlation between the severity of the disease and either of the two polymorphisms.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Integrating Phenotypic Information of Obstructive Sleep Apnea and Deep Representation of Sleep-Event Sequences for Cardiovascular Risk Prediction

Background

Advances in mobile, wearable and machine learning (ML) technologies for gathering and analyzing long-term health data have opened up new possibilities for predicting and preventing cardiovascular diseases (CVDs). Meanwhile, the association between obstructive sleep apnea (OSA) and CV risk has been well-recognized. This study seeks to explore effective strategies of incorporating OSA phenotypic information and overnight physiological information for precise CV risk prediction in the general population.

Methods

1,874 participants without a history of CVDs from the MESA dataset were included for the 5-year CV risk prediction. Four OSA phenotypes were first identified by the K-mean clustering based on static polysomnographic (PSG) features. Then several phenotype-agnostic and phenotype-specific ML models, along with deep learning (DL) models that integrate deep representations of overnight sleep-event feature sequences, were built for CV risk prediction. Finally, feature importance analysis was conducted by calculating SHapley Additive exPlanations (SHAP) values for all features across the four phenotypes to provide model interpretability.

Results

All ML models showed improved performance after incorporating the OSA phenotypic information. The DL model trained with the proposed phenotype-contrastive training strategy performed the best, achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.877. Moreover, PSG and FOOD FREQUENCY features were recognized as significant CV risk factors across all phenotypes, with each phenotype emphasizing unique features.

Conclusion

Models that are aware of OSA phenotypes are preferred, and lifestyle factors should be a greater focus for precise CV prevention and risk management in the general population.

Structural EEG signal analysis for sleep apnea classification

OBJECTIVES

Diagnosing the sleep apnea can be critical in preventing the person having sleep disorder from unhealthy results. The aim of this study is to obtain a sleep apnea scoring approach by comparing parametric and non-parametric power spectral density (PSD) estimation methods from EEG signals recorded from different brain regions (C4-M1 and O2-M1) for transient signal analysis of sleep apnea patients.

METHODS

Power Spectral Density (PSD) methods (Burg, Yule-Walker, periodogram, Welch and multi-taper) are examined for the detection of apnea transition states including pre-apnea, intra-apnea and post-apnea together with statistical methods.

RESULTS

In the experimental studies, EEG recordings available in the database were analyzed with PSD methods. Results showed that there are statistically significant differences between parametric and non-parametric methods applied for PSD analysis of apnea transition states in delta, theta, alpha and beta bands. Moreover, it was also revealed that PSD of EEG signals obtained from C4-M1 and O2-M1 channels were also found statistically different as proved by classification using the K-nearest neighbour (KNN) method.

CONCLUSIONS

It was concluded that not only applying different PSD methods, but also EEG signals from different brain regions provided different statistical results in terms of apnea transition states as obtained from KNN classification.

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children

STUDY OBJECTIVES

Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification.

METHODS

Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels.

RESULTS

In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively.

CONCLUSIONS

We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification.

CITATION

van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med. 2024;20(3):389-397.

Evaluation of daytime sleepiness and insomnia symptoms in OSA patients with a characterization of symptom-defined phenotypes and their involvement in depression comorbidity-a cross-sectional clinical study

Introduction

Recent research highlights the significance of insomnia and sleepiness, shifting from obstructive sleep apnea (OSA) severity and sleep structure, in defining OSA phenotypes.

Objectives

This study aimed to characterize insomnia and sleepiness associated with OSA phenotypes and assess their involvement in depression symptoms (DS) in OSA.

Materials and methods

This cross-sectional, clinical study included 181 participants who underwent polysomnography (PSG) and filled out questionnaires, including the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Beck Depression Index (BDI). They were categorized into phenotypes: insomnia-sleepiness (I + S; ESS ≥ 11; ISI ≥ 15; n = 20), sleepiness (S; ESS ≥ 11; ISI < 15; n = 22), insomnia (I; ESS < 11; ISI ≥ 15), and asymptomatic (A; ESS < 11; ISI<15; n=55).

Results

A linear regression model for the BDI score (R2 = 0.357, p < 0.001) included ISI score and subjective-to-objective sleep latency ratio. The ISI score was a predictive factor for mild and moderate DS [OR = 1.23 (95% CI: 1.09-1.38), p < 0.001 and OR = 1.39 (95% CI: 1.13-1.72), p = 0.002]. The I and I + S phenotypes are characterized by higher BDI scores (p < 0.001 and p = 0.02), longer subjective sleep latency (p = 0.008 and p = 0.04), and shorter subjective total sleep time (TST; p = 0.049 and p = 0.006) compared to A. Furthermore, the I and I + S groups had shorter subjective TST than S (p = 0.03 and p = 0.047). The I and I + S had higher BDI scores than A (p < 0.001 and p = 0.02, respectively) and S (p < 0.001 and p = 0.02, respectively). The I phenotype was associated with the risk of mild and moderate DS (OR = 5.61 (95% CI: 1.91-16.53), p < 0.001 and OR = 9.55 (95% CI: 1.81-50.48), p = 0.008 respectively). Moreover, the I + S phenotype presented an even greater risk for mild DS (OR = 10.29 (95% CI: 2.95-35.85), p < 0.001).

Conclusion

Using clinical features for OSA phenotyping holds promise for finding OSA individuals with increased risk for DS occurrence.

Systematic Review of Sleep Duration and Development of Myopia

There is a knowledge gap in the relationship between sleep duration and myopia. Since sleep duration is a modifiable risk factor, its association with the development and progression of myopia has implications for public health. This review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The bibliographic databases of PubMed and Scopus were searched for published studies on the association between sleep duration and myopia. These databases were searched in December 2023 with no date or study design limits. The relevant literature was extracted and met the priori determined population (children, adolescents, and adults suffering from myopia with or without corrective glasses), intervention/exposure (sleep), and the outcome (various indicators of sleep especially sleep duration/bedtime/wake time and sleep quality). Data were gathered by gender, age, and refraction technique and standardized to the definition of myopia as refractive error ≥0.50 diopter. The relevant literature was extracted from these electronic databases using the keywords "sleep," "sleep duration," "bedtime," and "myopia." English language articles related to the topic were included. Articles that have discussed the role of risk factors for myopia but did not mention any relation to sleep were excluded. Sixteen studies were included after reviewing the relevant literature, and only six studies have shown a significant relationship between shorter duration of sleep and the development of myopia. This review suggests that apart from other environmental factors, sleep duration may have a role in developing myopia. Thus, increasing awareness about optimum sleep duration has a potential utility to reduce the development and progression of myopia.

Waking Up to the Importance of Sleep in Type 2 Diabetes Management: A Narrative Review

For the first time, the latest American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines have incorporated a growing body of evidence linking health outcomes associated with type 2 diabetes to the movement behavior composition over the whole 24-h day. Of particular note, the importance of sleep as a key lifestyle component in the management of type 2 diabetes is promulgated and presented using three key constructs: quantity, quality, and timing (i.e., chronotype). In this narrative review we highlight some of the key evidence justifying the inclusion of sleep in the latest consensus guidelines by examining the associations of quantity, quality, and timing of sleep with measures of glycemia, cardiovascular disease risk, and mortality. We also consider potential mechanisms implicated in the association between sleep and type 2 diabetes and provide practical advice for health care professionals about initiating conversations pertaining to sleep in clinical care. In particular, we emphasize the importance of measuring sleep in a free-living environment and provide a summary of the different methodologies and targets. In summary, although the latest ADA/EASD consensus report highlights sleep as a central component in the management of type 2 diabetes, placing it, for the first time, on a level playing field with other lifestyle behaviors (e.g., physical activity and diet), the evidence base for improving sleep (beyond sleep disorders) in those living with type 2 diabetes is limited. This review should act as a timely reminder to incorporate sleep into clinical consultations, ongoing diabetes education, and future interventions.

Clinical usefulness of dual orexin receptor antagonism beyond insomnia: Neurological and psychiatric comorbidities

Orexin is a neurotransmitter produced by a small group of hypothalamic neurons. Besides its well-known role in the regulation of the sleep-wake cycle, the orexin system was shown to be relevant in several physiological functions including cognition, mood and emotion modulation, and energy homeostasis. Indeed, the implication of orexin neurotransmission in neurological and psychiatric diseases has been hypothesized via a direct effect exerted by the projections of orexin neurons to several brain areas, and via an indirect effect through orexin-mediated modulation of sleep and wake. Along with the growing evidence concerning the use of dual orexin receptor antagonists (DORAs) in the treatment of insomnia, studies assessing their efficacy in insomnia comorbid with psychiatric and neurological diseases have been set in order to investigate the potential impact of DORAs on both sleep-related symptoms and disease-specific manifestations. This narrative review aimed at summarizing the current evidence on the use of DORAs in neurological and psychiatric conditions comorbid with insomnia, also discussing the possible implication of modulating the orexin system for improving the burden of symptoms and the pathological mechanisms of these disorders. Target searches were performed on PubMed/MEDLINE and Scopus databases and ongoing studies registered on Clinicaltrials.gov were reviewed. Despite some contradictory findings, preclinical studies seemingly support the possible beneficial role of orexin antagonism in the management of the most common neurological and psychiatric diseases with sleep-related comorbidities. However, clinical research is still limited and further studies are needed for corroborating these promising preliminary results.

SleepMI: An AI-based screening algorithm for myocardial infarction using nocturnal electrocardiography

Myocardial infarction (MI) is a common cardiovascular disease, the early diagnosis of which is essential for effective treatment and reduced mortality. Therefore, novel methods are required for automatic screening or early diagnosis of MI, and many studies have proposed diverse conventional methods for its detection. In this study, we aimed to develop a sleep-myocardial infarction (sleepMI) algorithm for automatic screening of MI based on nocturnal electrocardiography (ECG) findings from diagnostic polysomnography (PSG) data using artificial intelligence (AI) models. The proposed sleepMI algorithm was designed using representation and ensemble learning methods and optimized via dropout and batch normalization. In the sleepMI algorithm, a deep convolutional neural network and light gradient boost machine (LightGBM) models were mixed to obtain robust and stable performance for screening MI from nocturnal ECG findings. The nocturnal ECG signal was extracted from 2,691 participants (2,331 healthy individuals and 360 patients with MI) from the PSG data of the second follow-up stage of the Sleep Heart Health Study. The nocturnal ECG signal was extracted 3 h after sleep onset and segmented at 30-s intervals for each participant. All ECG datasets were divided into training, validation, and test sets consisting of 574,729, 143,683, and 718,412 segments, respectively. The proposed sleepMI model exhibited very high performance with precision, recall, and F1-score of 99.38%, 99.38%, and 99.38%, respectively. The total mean accuracy for automatic screening of MI using a nocturnal single-lead ECG was 99.387%. MI events can be detected using conventional 12-lead ECG signals and polysomnographic ECG recordings using our model.

Non-Pharmacological Intervention for Personalizing Sleep Quality through Gentle Rocking Motion

INTRODUCTION

Achieving restorative sleep is crucial for overall well-being, yet sleep difficulties affect a substantial portion of the adult population. Sleep disturbances are associated with diminished quality of life, physical complaints, cognitive impairment, and emotional regulation challenges.

OBJECTIVE

This study explores the influence of an innovative experimental bed designed to generate rocking motions on sleep parameters.

METHODS

A prospective observational study enrolled 60 adult participants, assessing their sleep on a regular stationary bed and the Inoveris bed, providing gentle rocking movements. Polysomnography was conducted, recording electroencephalography, electrooculogram, electromyogram, respiratory effort, and other parameters.

RESULTS

The rocking bed significantly increased total sleep time (TST) and reduced N1 sleep stage duration (p < 0.001). Participants also experienced a quicker transition to the N2 sleep stage (p = 0.01), indicative of a faster shift from wakefulness to deeper sleep. Additionally, rocking led to a higher percentage of N1 sleep stages (p = 0.01) and a significant increase in N3 sleep stage duration (p = 0.004). While some results lacked statistical significance, notable trends in the rocking bed group have clinical relevance, consistently improving sleep parameters, including increased TST. The rocking bed also showed a trend towards higher sleep efficiency (SE) and sleep duration percentage, hinting at a potential overall enhancement in sleep quality.

CONCLUSION

This study contributes valuable insights into the potential benefits of rocking motions on sleep architecture. Despite variations in outcomes across studies, our results underscore the potential of rocking beds as a non-pharmacological intervention for enhancing sleep quality. Notable improvements in total sleep time (TST), N1 sleep stage reduction, and accelerated transitions to deeper sleep stages highlight the clinical relevance of rocking interventions. Further research, collaboration, and addressing the identified limitations will advance our understanding of the therapeutic applications of rocking motions in sleep science.

Sleep Patterns of Premedical Undergraduate Students: Pilot Study and Protocol Evaluation

BACKGROUND

Poor sleep hygiene persists in college students today, despite its heavy implications on adolescent development and academic performance. Although sleep patterns in undergraduates have been broadly investigated, no study has exclusively assessed the sleep patterns of premedical undergraduate students. A gap also exists in the knowledge of how students perceive their sleep patterns compared to their actual sleep patterns.

OBJECTIVE

This study aims to address 2 research questions: What are the sleep patterns of premedical undergraduate students? Would the proposed study protocol be feasible to examine the perception of sleep quality and promote sleep behavioral changes in premedical undergraduate students?

METHODS

An anonymous survey was conducted with premedical students in the Medical Science Baccalaureate program at an R1: doctoral university in the Midwest United States to investigate their sleep habits and understand their demographics. The survey consisted of both Pittsburg Sleep Quality Index (PSQI) questionnaire items (1-9) and participant demographic questions. To examine the proposed protocol feasibility, we recruited 5 students from the survey pool for addressing the perception of sleep quality and changes. These participants followed a 2-week protocol wearing Fitbit Inspire 2 watches and underwent preassessments, midassessments, and postassessments. Participants completed daily reflections and semistructured interviews along with PSQI questionnaires during assessments.

RESULTS

According to 103 survey responses, premedical students slept an average of 7.1 hours per night. Only a quarter (26/103) of the participants experienced good sleep quality (PSQI<5), although there was no significant difference (P=.11) in the proportions of good (PSQI<5) versus poor sleepers (PSQI≥5) across cohorts. When students perceived no problem at all in their sleep quality, 50% (14/28) of them actually had poor sleep quality. Among the larger proportion of students who perceived sleep quality as only a slight problem, 26% (11/43) of them presented poor sleep quality. High stress levels were associated with poor sleep quality. This study reveals Fitbit as a beneficial tool in raising sleep awareness. Participants highlighted Fitbit elements that aid in comprehension such as being able to visualize their sleep stage breakdown and receive an overview of their sleep pattern by simply looking at their Fitbit sleep scores. In terms of protocol evaluation, participants believed that assessments were conducted within the expected duration, and they did not have a strong opinion about the frequency of survey administration. However, Fitbit was found to provide notable variation daily, leading to missing data. Moreover, the Fitbit app's feature description was vague and could lead to confusion.

CONCLUSIONS

Poor sleep quality experienced by unaware premedical students points to a need for raising sleep awareness and developing effective interventions. Future work should refine our study protocol based on lessons learned and health behavior theories and use Fitbit as an informatics solution to promote healthy sleep behaviors.

Comparison of sleep characteristics between Parkinson's disease with and without freezing of gait: A systematic review

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Among the motor complaints, freezing of gait (FOG) is a common and disabling phenomenon that episodically hinders patients' ability to produce efficient steps. Concurrently, sleep disorders are prevalent in PD and significantly impact the quality of life of affected individuals. Numerous studies have suggested a bidirectional relationship between FOG and sleep disorders. Therefore, our objective was to systematically review the literature and compare sleep outcomes in PD patients with FOG (PD + FOG) and those without FOG (PD-FOG). By conducting a comprehensive search of the PubMed and Web of Science databases, we identified 20 eligible studies for inclusion in our analysis. Our review revealed that compared to PD-FOG, PD + FOG patients exhibited more severe symptoms of rapid eye movement sleep behavior disorder in nine studies, increased daytime sleepiness in eight studies, decreased sleep quality in four studies, and more frequent and severe sleep disturbances in four studies. These findings indicate that PD + FOG patients generally experience worse sleep quality, higher levels of daytime sleepiness, and more disruptive sleep disturbances compared to those without FOG (PD-FOG). The association between sleep disturbances and FOG highlights the importance of evaluating and monitoring these symptoms in PD patients and open the possibility for future studies to assess the impact of managing sleep disturbances on the severity and occurrence of FOG, and vice versa.

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classification of some sleep stages and worsening others.

Effect of exercise on sleep quality in Parkinson's disease: a mini review

The growing incidence of Parkinson's Disease (PD) is a major burden on the healthcare system. PD is caused by the degeneration of dopaminergic neurons and is known for its effects on motor function and sleep. Sleep is vital for maintaining proper homeostasis and clearing the brain of metabolic waste. Adequate time spent in each sleep stage can help maintain homeostatic function; however, patients with PD appear to exhibit sleep impairments. Although medications enhance the function of remaining dopaminergic neurons and reduce motor symptoms, their potential to improve sleep is still under question. Recently, research has shifted towards exercise protocols to help improve sleep in patients with PD. This review aims to provide an overview of how sleep is impaired in patients with PD, such as experiencing a reduction in time spent in slow-wave sleep, and how exercise can help restore normal sleep function. A PubMed search summarized the relevant research on the effects of aerobic and resistance exercise on sleep in patients with PD. Both high and low-intensity aerobic and resistance exercises, along with exercises related to balance and coordination, have been shown to improve some aspects of sleep. Neurochemically, sleeping leads to an increase in toxin clearance, including α-synuclein. Furthermore, exercise appears to enhance the concentration of brain-derived neurotrophic factors, which has preliminary evidence to suggest correlations to time spent in slow-wave sleep. More research is needed to further elucidate the physiological mechanism pertaining to sleep and exercise in patients with PD.

Association between Psychological Distress and Possible, Probable, and Definite Sleep Bruxism-A Comparison of Approved Diagnostic Procedures

(1) Background: The relationship between sleep bruxism (SB) and psychological distress has been investigated in numerous studies and is heterogeneous. Different diagnostic procedures have been applied to determine SB. The aim of this study was to directly compare the association between psychological distress and SB diagnosed by different accepted methods. (2) Methods: Data of N = 45 subjects were analyzed, including group comparisons and correlation analyses. Following diagnostic methods for the determination of SB were used in one sample: self-report, clinical assessment, polysomnography with audio-video recording and a novel diagnostic sheet with analyzing software. Psychological distress was measured using the global severity index (GSI) of the Symptom Checklist-90-Standard (SCL-90-S). (3) Results: The GSI did not differ significantly between subjects with and without SB, regardless of the underlying diagnostic classification (p > 0.05). In-depth correlation analyses of self-report and clinical data revealed a weak-to-medium correlation with the GSI (r = 0.12-0.44). Due to non-normally distributed data, a test of statistical significance was not possible. Variables of instrumental methods such as the SB index (amount of SB activity per hour) of polysomnography (PSG) showed almost no correlation with psychological distress (r = -0.06-0.05). (4) Conclusions: Despite these limitations, the results provide an indication that the choice of diagnostic procedure may elucidate the variance in the correlation between SB and psychological distress.

Controversies in Sleep Apnea

This chapter discusses controversies in diagnosis and management of obstructive sleep apnea (OSA), with particular focus on surgical management to improve quality of life. Though OSA is a complex disorder that affects millions of people worldwide, its management remains controversial among clinicians. Gaps in understanding its pathophysiology, long-term health consequences, diagnostic methods, and treatment strategies exist. While continuous positive airway pressure (CPAP) therapy is considered the gold standard for moderate to severe obstructive sleep apnea (OSA), its adherence rate is often low, and its efficacy in improving outcomes beyond symptom reduction and quality of life improvement is uncertain. As such, surgical intervention may be an alternative for specific patient populations. Additionally, the type of surgical intervention may depend on individual patient needs, anatomic features, as well as preferences.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

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.

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.

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.