Showing papers on "Polysomnography published in 2021"
TL;DR: In this paper, the performance of consumer sleep-tracking devices, alongside actigraphy, was compared with the gold-standard sleep assessment technique, polysomnography (PSG).
Abstract: Study objectives Consumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG). Methods In total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG. Results Most devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18-0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep. Conclusions Consumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.
116 citations
TL;DR: In this paper, a systematic literature search, from 2008 to 2020, was performed using the electronic databases PubMed and Scopus, with predefined search terms, and 49 articles were analyzed from the 5734 articles found.
Abstract: Sleep quality is an important clinical construct since it is increasingly common for people to complain about poor sleep quality and its impact on daytime functioning. Moreover, poor sleep quality can be an important symptom of many sleep and medical disorders. However, objective measures of sleep quality, such as polysomnography, are not readily available to most clinicians in their daily routine, and are expensive, time-consuming, and impractical for epidemiological and research studies., Several self-report questionnaires have, however, been developed. The present review aims to address their psychometric properties, construct validity, and factorial structure while presenting, comparing, and discussing the measurement properties of these sleep quality questionnaires. A systematic literature search, from 2008 to 2020, was performed using the electronic databases PubMed and Scopus, with predefined search terms. In total, 49 articles were analyzed from the 5734 articles found. The psychometric properties and factor structure of the following are reported: Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), Mini-Sleep Questionnaire (MSQ), Jenkins Sleep Scale (JSS), Leeds Sleep Evaluation Questionnaire (LSEQ), SLEEP-50 Questionnaire, and Epworth Sleepiness Scale (ESS). As the most frequently used subjective measurement of sleep quality, the PSQI reported good internal reliability and validity; however, different factorial structures were found in a variety of samples, casting doubt on the usefulness of total score in detecting poor and good sleepers. The sleep disorder scales (AIS, ISI, MSQ, JSS, LSEQ and SLEEP-50) reported good psychometric properties; nevertheless, AIS and ISI reported a variety of factorial models whereas LSEQ and SLEEP-50 appeared to be less useful for epidemiological and research settings due to the length of the questionnaires and their scoring. The MSQ and JSS seemed to be inexpensive and easy to administer, complete, and score, but further validation studies are needed. Finally, the ESS had good internal consistency and construct validity, while the main challenges were in its factorial structure, known-group difference and estimation of reliable cut-offs. Overall, the self-report questionnaires assessing sleep quality from different perspectives have good psychometric properties, with high internal consistency and test-retest reliability, as well as convergent/divergent validity with sleep, psychological, and socio-demographic variables. However, a clear definition of the factor model underlying the tools is recommended and reliable cut-off values should be indicated in order for clinicians to discriminate poor and good sleepers.
93 citations
15 Apr 2021
TL;DR: U-sleep as mentioned in this paper is a fully convolutional neural network, which was trained and evaluated on polysomnography (PSG) recordings from 15,660 participants of 16 clinical studies.
Abstract: Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging (
sleep.ai.ku.dk
). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.
66 citations
07 Jan 2021
TL;DR: In this article, a deep learning model for automatic sleep stage classification is proposed, which can handle arbitrary polysomnography (PSG) montages and reach 97% of the F1 of a model explicitly trained on this dataset.
Abstract: Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods demonstrated better performance than human sleep experts on specific datasets, they remain largely unused in sleep clinics. The main reason is that each sleep clinic uses a specific PSG montage that most automatic approaches cannot handle out-of-the-box. Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics. To address these issues, we introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages. We trained and evaluated this model in a leave-one-out-dataset fashion on a large corpus of 8 heterogeneous sleep staging datasets to make it robust to demographic changes. When evaluated on an unseen dataset, RobustSleepNet reaches 97% of the F1 of a model explicitly trained on this dataset. Hence, RobustSleepNet unlocks the possibility to perform high-quality out-of-the-box automatic sleep staging with any clinical setup. We further show that finetuning RobustSleepNet, using a part of the unseen dataset, increases the F1 by 2% when compared to a model trained specifically for this dataset. Therefore, finetuning might be used to reach a state-of-the-art level of performance on a specific population.
52 citations
TL;DR: This report reviews the advances in wearable sensors, miniaturized electronics, and system packaging for home sleep monitoring and provides a comprehensive view of newly developed technologies and broad insights on wearable sensors and portable electronics toward advanced sleep monitoring as well as at-home sleep assessment.
51 citations
TL;DR: Among surveyed sleep centers, the vast majority reported near-cessation of in-laboratory sleep studies, while a smaller proportion reported reductions in HSATs, and a large increase in the use of telemedicine was reported, with the majority of respondents expecting theUse of telehealth to endure in the future.
Abstract: Study Objectives:The COVID-19 pandemic required sleep centers to consider and implement infection control strategies to mitigate viral transmission to patients and staff. Our aim was to assess meas...
47 citations
TL;DR: In this paper, an analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset was provided.
Abstract: Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.
41 citations
TL;DR: This paper proposes a low-cost and non-intrusive sleep monitoring system using commodity Wi-Fi devices, namely WiFi-Sleep, and introduces a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources.
Abstract: Sleep monitoring is essential to people’s health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi-based sensing, this article proposes a low-cost and nonintrusive sleep monitoring system using commodity Wi-Fi devices, namely, WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.
40 citations
TL;DR: The most common treatment for obstructive sleep apnea syndrome (OSAS) is positive airway pressure (PAP), but compliance continues to be a challenge for many patients.
Abstract: Obstructive sleep apnea syndrome (OSAS) is a common and underdiagnosed medical condition characterized by recurrent sleep-dependent pauses and reductions in airflow. While a narrow, collapsible oropharynx plays a central role in the pathophysiology of OSAS, there are other equally important nonanatomic factors including sleep-stage dependent muscle tone, arousal threshold, and loop gain that drive obstructive apneas and hypopneas. Through mechanisms of intermittent hypoxemia, arousal-related sleep fragmentation, and intrathoracic pressure changes, OSAS impacts multiple organ systems. Risk factors for OSAS include obesity, male sex, age, specific craniofacial features, and ethnicity. The prevalence of OSAS is rising due to increasing obesity rates and improved sensitivity in the tools used for diagnosis. Validated questionnaires have an important but limited role in the identification of patients that would benefit from formal testing for OSA. While an in-laboratory polysomnography remains the gold standard for diagnosis, the widespread availability and accuracy of home sleep apnea testing modalities increase access and ease of OSAS diagnosis for many patients. In adults, the most common treatment involves the application of positive airway pressure (PAP), but compliance continues to be a challenge. Alternative treatments including mandibular advancement device, hypoglossal nerve stimulator, positional therapies, and surgical options coupled with weight loss and exercise offer possibilities of an individualized personal approach to OSAS. Treatment of symptomatic patients with OSAS has been found to be beneficial with regard to sleep-related quality of life, sleepiness, and motor vehicle accidents. The benefit of treating asymptomatic OSA patients, particularly with regard to cardiovascular outcomes, is controversial and more data are needed.
38 citations
TL;DR: Results from this systematic review indicate that rTMS and tDCS are safe and have potential to improve insomnia symptoms and sleep disturbances across different types of neurological and neuropsychiatric diseases.
38 citations
TL;DR: Overall, sleep quantity and quality in athletes is reduced and potentially insufficient, in comparison to the general consensus of the American Academy of Sleep Medicine for non-athlete healthy adults.
TL;DR: With rising awareness of OSA and the increasing prevalence of obesity, OSA is increasingly recognized as a major contributor to cardiovascular morbidity including systemic and pulmonary arterial hypertension, heart failure, acute coronary syndromes, atrial fibrillation, and other arrhythmias.
TL;DR: In this article, the authors used an ensemble of bagged tree (EBT) classifier with 10-fold cross validation for automated sleep stage classification using single or dual channel EEG epochs.
Abstract: Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.
TL;DR: In this paper, the authors found that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency, and slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function.
Abstract: Sleep monitoring may provide markers for future Alzheimer's disease; however, the relationship between sleep and cognitive function in preclinical and early symptomatic Alzheimer's disease is not well understood. Multiple studies have associated short and long sleep times with future cognitive impairment. Since sleep and the risk of Alzheimer's disease change with age, a greater understanding of how the relationship between sleep and cognition changes over time is needed. In this study, we hypothesized that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency and non-REM slow wave activity. To test this hypothesis, we monitored sleep-wake activity over 4-6 nights in 100 participants who underwent standardized cognitive testing longitudinally, APOE genotyping, and measurement of Alzheimer's disease biomarkers, total tau and amyloid-β42 in the CSF. To assess cognitive function, individuals completed a neuropsychological testing battery at each clinical visit that included the Free and Cued Selective Reminding test, the Logical Memory Delayed Recall assessment, the Digit Symbol Substitution test and the Mini-Mental State Examination. Performance on each of these four tests was Z-scored within the cohort and averaged to calculate a preclinical Alzheimer cognitive composite score. We estimated the effect of cross-sectional sleep parameters on longitudinal cognitive performance using generalized additive mixed effects models. Generalized additive models allow for non-parametric and non-linear model fitting and are simply generalized linear mixed effects models; however, the linear predictors are not constant values but rather a sum of spline fits. We found that longitudinal changes in cognitive function measured by the cognitive composite decreased at low and high values of total sleep time (P < 0.001), time in non-REM (P < 0.001) and REM sleep (P < 0.001), sleep efficiency (P < 0.01) and <1 Hz and 1-4.5 Hz non-REM slow wave activity (P < 0.001) even after adjusting for age, CSF total tau/amyloid-β42 ratio, APOE e4 carrier status, years of education and sex. Cognitive function was stable over time within a middle range of total sleep time, time in non-REM and REM sleep and <1 Hz slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function. Although longitudinal and interventional studies are needed, diagnosing and treating sleep disturbances to optimize sleep time and slow wave activity may have a stabilizing effect on cognition in preclinical or early symptomatic Alzheimer's disease.
01 Mar 2021
TL;DR: In this article, the authors evaluated the utility of the STOP-Bang questionnaire in the sleep clinic setting to screen for and stratify the risk of obstructive sleep apnea among populations from different geographical regions.
Abstract: Importance Obstructive sleep apnea (OSA) is a highly prevalent global health concern and is associated with many adverse outcomes for patients. Objective To evaluate the utility of the STOP-Bang (snoring, tiredness, observed apnea, blood pressure, body mass index, age, neck size, gender) questionnaire in the sleep clinic setting to screen for and stratify the risk of OSA among populations from different geographical regions. Data sources and study selection MEDLINE, MEDLINE In-process, Embase, EmCare Nursing, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, PsycINFO, Journals@Ovid, Web of Science, Scopus, and CINAHL electronic databases were systematically searched from January 2008 to March 2020. This was done to identify studies that used the STOP-Bang questionnaire and polysomnography testing in adults referred to sleep clinics. Data extraction and synthesis Clinical and demographic data were extracted from each article independently by 2 reviewers. The combined test characteristics were calculated using 2 × 2 contingency tables. Random-effects meta-analyses and metaregression with sensitivity analyses were performed. The Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guideline was followed. Main outcomes and measures The combined test characteristics and area under summary receiver operating characteristic curves (AUCs) were used to compare STOP-Bang questionnaire accuracy with polysomnography testing. Results A total of 47 studies with 26 547 participants (mean [SD] age, 50 [5] years; mean [SD] body mass index, 32 [3]; 16 780 [65%] men) met the criteria for the systematic review. Studies were organized in different geographic regional groups: North America, South America, Europe, Middle East, East Asia, and South or Southeast Asia. The prevalence rates for all OSA, moderate to severe OSA, and severe OSA were 80% (95% CI, 80%-81%), 58% (95% CI, 58%-59%), and 39% (95% CI, 38%-39%), respectively. A STOP-Bang score of at least 3 had excellent sensitivity (>90%) and high discriminative power to exclude moderate to severe and severe OSA, with negative predictive values of 77% (95% CI, 75%-78%) and 91% (95% CI, 90%-92%), respectively. The diagnostic accuracy of a STOP-Bang score of at least 3 to detect moderate to severe OSA was high (>0.80) in all regions except East Asia (0.52; 95% CI, 0.48-0.56). Conclusions and relevance The results of this meta-analysis suggest that the STOP-Bang questionnaire can be used as a screening tool to assist in triaging patients with suspected OSA referred to sleep clinics in different global regions.
TL;DR: In this paper, a systematic review was conducted to determine how studies evaluated napping behavior in athletes (frequency, duration, timing and measurement); and explore how napping impacted physical performance, cognitive performance, perceptual measures (e.g., fatigue, muscle soreness, sleepiness and alertness), psychological state and night-time sleep in athletes.
Abstract: Purpose: The objective of this systematic review was to 1) determine how studies evaluated napping behavior in athletes (frequency, duration, timing and measurement); 2) explore how napping impacted physical performance, cognitive performance, perceptual measures (eg, fatigue, muscle soreness, sleepiness and alertness), psychological state and night-time sleep in athletes.
Methods: Five bibliographic databases were searched from database inception to 11 August 2020. Observational and experimental studies comprising able-bodied athletes (mean age ≥ 12 years), published in English, in peer-reviewed journal papers were included. The Downs and Black Quality Assessment Checklist was used for quality appraisal.
Results: Thirty-seven studies were identified of moderate quality. Most studies did not include consistent information regarding nap frequency, duration, and timing. Napping may be beneficial for a range of outcomes that benefit athletes (eg, physical and cognitive performance, perceptual measures, psychological state and night-time sleep). In addition, napping presents athletes with the opportunity to supplement their night-time sleep without compromising sleep quality.
Conclusion: Athletes may consider napping between 20 to 90 min in duration and between 13:00 and 16:00 hours. Finally, athletes should allow 30 min to reduce sleep inertia prior to training or competition to obtain better performance outcomes. Future studies should include comprehensive recordings of nap duration and quality, and consider using sleep over a 24 hour period (daytime naps and night-time sleep period), specifically using objective methods of sleep assessment (eg, polysomnography/actigraphy).
TL;DR: In this article, the authors explore the potential of detecting sleep using Random Forests, which are trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic.
Abstract: Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.
TL;DR: In this article, reboxetine plus hyoscine butylbromide was shown to improve upper airway function during sleep in healthy individuals by increasing pharyngeal muscle responsiveness, improving respiratory control and airway collapsibility without changing the respiratory arousal threshold.
Abstract: Key points Recent animal and human physiology studies indicate that noradrenergic and muscarinic processes are key mechanisms that mediate pharyngeal muscle control during sleep. The noradrenergic agent reboxetine combined with the anti-muscarinic hyoscine butylbromide has recently been shown to improve upper airway function during sleep in healthy individuals. However, whether these findings translate to the clinically relevant patient population of people with obstructive sleep apnoea (OSA), and the effects of the agents on OSA severity, are unknown. We found that reboxetine plus hyoscine butylbromide reduced OSA severity, including overnight hypoxaemia, via increases in pharyngeal muscle responsiveness, improvements in respiratory control and airway collapsibility without changing the respiratory arousal threshold. These findings provide mechanistic insight into the role of noradrenergic and anti-muscarinic agents on upper airway stability and breathing during sleep and are important for pharmacotherapy development for OSA. Abstract The noradrenergic agent reboxetine combined with the anti-muscarinic hyoscine butylbromide has recently been shown to improve upper airway function during sleep in healthy individuals. However, the effects of this drug combination on obstructive sleep apnoea (OSA) severity are unknown. Accordingly, this study aimed to determine if reboxetine plus hyoscine butylbromide reduces OSA severity. Secondary aims were to investigate the effects on key upper airway physiology and endotypic traits. Twelve people with OSA aged 52 ± 13 years, BMI = 30 ± 5 kg/m2 , completed a double-blind, randomised, placebo-controlled, crossover trial (ACTRN12617001326381). Two in-laboratory sleep studies with nasal mask, pneumotachograph, epiglottic pressure sensor and bipolar fine-wire electrodes into genioglossus and tensor palatini muscles were performed separated by approximately 1 week. Each participant received either reboxetine (4 mg) plus hyoscine butylbromide (20 mg), or placebo immediately prior to sleep. Polysomnography, upper airway physiology and endotypic estimates of OSA were compared between conditions. Reboxetine plus hyoscine butylbromide reduced the apnoea/hypopnoea index by (mean ± SD) 17 ± 17 events/h from 51 ± 30 to 33 ± 22 events/h (P = 0.005) and nadir oxygen saturation increased by 6 ± 5% from 82 ± 5 to 88 ± 2% (P = 0.002). The drug combination increased tonic genioglossus muscle responsiveness during non-REM sleep (median [25th, 75th centiles]: -0.007 [-0.0004, -0.07] vs. -0.12 [-0.02, -0.40] %maxEMG/cmH2 O, P = 0.02), lowered loop gain (0.43 ± 0.06 vs. 0.39 ± 0.07, P = 0.01), and improved airway collapsibility (90 [69, 95] vs. 93 [88, 96] %eupnoea, P = 0.02), without changing the arousal threshold (P = 0.39). These findings highlight the important role that noradrenergic and muscarinic processes have on upper airway function during sleep and the potential for pharmacotherapy to target these mechanisms to treat OSA.
15 Sep 2021
TL;DR: In this paper, a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3).
Abstract: Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.
TL;DR: A model for using multidisciplinary, patient-centered care is recommended to optimize the clinical management of patients with COMISA and patient- centered considerations that integrate patient characteristics, treatment preferences, and accessibility to treatment in the context of COMISA are discussed as opportunities to improve patient care.
TL;DR: The proposed sleep staging system based on an ensemble learning stacking model that integrates Random Forest and eXtreme Gradient Boosting has an excellent improvement in classification accuracy for the six-two sleep states classification.
01 May 2021
TL;DR: The causes of excessive daytime sleepiness are varied, and include inadequate sleep, sleep disordered breathing, circadian rhythm sleep-wake disorders, and central disorders of hypersomnolence (narcolepsy, idiopathic hypersomnia, and Kleine-Levin syndrome) as discussed by the authors.
Abstract: Excessive daytime sleepiness (EDS) is a highly prevalent condition that is associated with significant morbidity. The causes of EDS are varied, and include inadequate sleep, sleep disordered breathing, circadian rhythm sleep-wake disorders, and central disorders of hypersomnolence (narcolepsy, idiopathic hypersomnia, and Kleine-Levin syndrome). Additionally, EDS could represent a symptom of an underlying medical or psychiatric disorder. Assessment of EDS includes a thorough sleep, medical, and psychiatric history, targeted clinical examination, and appropriate use of actigraphy to measure sleep duration and sleep-wake patterns, polysomnography to assess for associated conditions such as sleep-related breathing disorders or other factors that might disrupt nighttime sleep, multiple sleep latency testing to ascertain objective sleepiness and diagnose central disorders of hypersomnolence, and measurement of cerebrospinal fluid hypocretin-1 concentration. Treatment of EDS secondary to central disorders of hypersomnolence is primarily pharmacologic with wakefulness-promoting agents such as modafinil, stimulants such as methylphenidate and amphetamines, and newer agents specifically designed to improve wakefulness; behavioral interventions can provide a useful adjunct to pharmacologic treatment. When excessive sleepiness is secondary to other conditions, the treatment should focus on targeting the primary disorder. This review discusses current epidemiology, provides guidance on clinical assessments and testing, and discusses the latest treatment options. For this review, we collated the latest evidence using the search terms excessive sleepiness, hypersomnia, hypersomnolence, treatment from PubMed and MEDLINE and the latest practice parameters from the American Academy of Sleep Medicine.
TL;DR: In this paper, a systematic review and meta-analysis was conducted to compare insomnia disorder with objective short and normal sleep duration, and subsequently, objective short sleep duration with and without insomnia disorder, and their associations with hypertension, type 2 diabetes and body mass index.
TL;DR: In this paper, a meta-analysis of the literature on the heritability of sleep duration and sleep quality in the general population was conducted. And the authors concluded that 46% of the variability in sleep duration was genetically determined.
TL;DR: The results support the use of exercise to improve sleep quality in persons with PD and reinforce the importance of achieving vigorous exercise intensities.
TL;DR: In this article, a longitudinal management of obstructive sleep apnea is proposed. But, the longitudinal management is crucial to patient health and sleep-related qualitatively and empirically.
Abstract: Introduction:Obstructive sleep apnea is an important and common disorder with associated health risks. Assuring successful longitudinal management is vital to patient health and sleep-related quali...
Charité1, University of Pennsylvania2, University of Kansas3, University of Valladolid4, Humboldt University of Berlin5, Federal University of São Paulo6, Royal North Shore Hospital7, University of Sydney8, University of Western Australia9, Sir Charles Gairdner Hospital10, Memorial Hospital of South Bend11, University of Iceland12, The Ohio State University Wexner Medical Center13
TL;DR: Time-domain and non-linear HRV measures during wakefulness were associated with OSA severity, with severe patients having remarkably reduced and less complex HRV.
Abstract: STUDY OBJECTIVES Patients with obstructive sleep apnea (OSA) exhibit heterogeneous heart rate variability (HRV) during wakefulness and sleep. We investigated the influence of OSA severity on HRV parameters during wakefulness in a large international clinical sample. METHODS 1247 subjects (426 without OSA and 821 patients with OSA) were enrolled from the Sleep Apnea Global Interdisciplinary Consortium. HRV parameters were calculated during a 5-minute wakefulness period with spontaneous breathing prior to the sleep study, using time-domain, frequency-domain and nonlinear methods. Differences in HRV were evaluated among groups using analysis of covariance, controlling for relevant covariates. RESULTS Patients with OSA showed significantly lower time-domain variations and less complexity of heartbeats compared to individuals without OSA. Those with severe OSA had remarkably reduced HRV compared to all other groups. Compared to non-OSA patients, those with severe OSA had lower HRV based on SDNN (adjusted mean: 37.4 vs. 46.2 ms; p < 0.0001), RMSSD (21.5 vs. 27.9 ms; p < 0.0001), ShanEn (1.83 vs. 2.01; p < 0.0001), and Forbword (36.7 vs. 33.0; p = 0.0001). While no differences were found in frequency-domain measures overall, among obese patients there was a shift to sympathetic dominance in severe OSA, with a higher LF/HF ratio compared to obese non-OSA patients (4.2 vs. 2.7; p = 0.009). CONCLUSIONS Time-domain and nonlinear HRV measures during wakefulness are associated with OSA severity, with severe patients having remarkably reduced and less complex HRV. Frequency-domain measures show a shift to sympathetic dominance only in obese OSA patients. Thus, HRV during wakefulness could provide additional information about cardiovascular physiology in OSA patients. CLINICAL TRIAL INFORMATION A Prospective Observational Cohort to Study the Genetics of Obstructive Sleep Apnea and Associated Co-Morbidities (German Clinical Trials Register - DKRS, DRKS00003966) https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00003966.
TL;DR: The results suggest that TSD and SR induce sustained, differential biological, physiological, and/or neural changes, which remarkably are not reversed with chronic, long-duration recovery sleep.
Abstract: STUDY OBJECTIVES The amount of recovery sleep needed to fully restore well-established neurobehavioral deficits from sleep loss remains unknown, as does whether the recovery pattern differs across measures after total sleep deprivation (TSD) and chronic sleep restriction (SR). METHODS In total, 83 adults received two baseline nights (10-12-hour time in bed [TIB]) followed by five 4-hour TIB SR nights or 36-hour TSD and four recovery nights (R1-R4; 12-hour TIB). Neurobehavioral tests were completed every 2 hours during wakefulness and a Maintenance of Wakefulness Test measured physiological sleepiness. Polysomnography was collected on B2, R1, and R4 nights. RESULTS TSD and SR produced significant deficits in cognitive performance, increases in self-reported sleepiness and fatigue, decreases in vigor, and increases in physiological sleepiness. Neurobehavioral recovery from SR occurred after R1 and was maintained for all measures except Psychomotor Vigilance Test (PVT) lapses and response speed, which failed to completely recover. Neurobehavioral recovery from TSD occurred after R1 and was maintained for all cognitive and self-reported measures, except for vigor. After TSD and SR, R1 recovery sleep was longer and of higher efficiency and better quality than R4 recovery sleep. CONCLUSIONS PVT impairments from SR failed to reverse completely; by contrast, vigor did not recover after TSD; all other deficits were reversed after sleep loss. These results suggest that TSD and SR induce sustained, differential biological, physiological, and/or neural changes, which remarkably are not reversed with chronic, long-duration recovery sleep. Our findings have critical implications for the population at large and for military and health professionals.
TL;DR: In this paper, the authors evaluated changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control, and found that during lockdown individuals sleep more overall (mean +3·83min; SD: ± 1.3), had less deep sleep (N3), more light sleep (n2) and longer REM sleep (measured: sleep onset duration, total sleep time (TST), duration of sleep stages (N2, N3 and REM), and sleep continuity.
Abstract: STUDY OBJECTIVES: The Covid-19 pandemic has had dramatic effects on society and people's daily habits. In this observational study we recorded objective data on sleep macro- and microarchitecture repeatedly over several nights before and during the Covid-19 government-imposed lockdown. The main objective was to evaluate changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control. METHODS: Participants were regular users of a sleep-monitoring headband that records, stores, and automatically analyses physiological data in real time, equivalent to polysomnography. We measured: sleep onset duration (SOD), total sleep time (TST), duration of sleep stages (N2, N3 and REM), and sleep continuity. Via the user's smartphone application participants filled-in questionnaires on how lockdown changed working hours, eating behaviour, and daily-life at home. They also filled-in the Insomnia Severity Index, reduced Morningness-Eveningness Questionnaire and Hospital Anxiety and Depression Scale questionnaires allowing us to create selected sub-groups. RESULTS: The 599 participants were mainly men (71%) of median age 47 [IQR: 36;59]. Compared to before lockdown, during lockdown individuals slept more overall (mean +3·83 min; SD: ±1.3), had less deep sleep (N3), more light sleep (N2) and longer REM sleep (mean +3·74 min; SD: ±0.8). They exhibited less week-end specific changes, suggesting less sleep restriction during the week. Changes were most pronounced in individuals reporting eveningness preferences, suggesting relative sleep deprivation in this population and exacerbated sensitivity to societal changes. CONCLUSIONS: This unique dataset should help us understand the effects of lockdown on sleep architecture and on our health.
TL;DR: OSA is associated with increased severity of PD-associated cognitive dysfunction and motor symptoms, and further studies are needed to corroborate these findings, assess the underlying mechanisms by which OSA influences the motor and cognitive functions in PD, and investigate whether OSA can accelerate the neurodegenerative process of PD.
Abstract: Introduction Parkinson's disease (PD) is a chronic neurodegenerative disorder that presents with motor and non-motor manifestations. Amongst the non-motor features, various forms of sleep disturbances can occur, and obstructive sleep apnea (OSA) is considered to be a common comorbidity. We conducted this systematic review and meta-analysis to assess the impact of OSA on cognitive and motor functions in PD. Methods The information sources of for this systematic review and meta-analysis were PubMed, SCOPUS, Web of Science, and ScienceDirect. Studies meeting the following criteria were included: (1) studies including idiopathic PD patients, (2) studies using polysomnography to categorize PD patients into PD with OSA and PD without OSA, and (3) studies with observational designs (case-control, cohort, or cross-sectional). Data analysis was performed using RevMan. Results Our meta-analysis showed that OSA was associated with significantly lower scores of Montreal Cognitive Assessments (MoCA) (mean difference (MD) = -0.70, 95% confidence interval (CI) [-1.28, -0.13], P = 0.01) and Mini-Mental State Examination (MMSE) (MD = -0.69, 95% CI [-1.17, -0.21], P = 0.005). Moreover, the score of the motor part of the Unified Parkinson's Disease Rating Scale (UPDRS III) was significantly higher in PD patients with OSA as compared with those without OSA (MD = 1.63, 95% CI [0.03, 3.23], P = 0.049). Conclusions OSA is associated with increased severity of PD-associated cognitive dysfunction and motor symptoms. However, further studies are needed to corroborate these findings, assess the underlying mechanisms by which OSA influences the motor and cognitive functions in PD, and investigate whether OSA can accelerate the neurodegenerative process of PD. © 2020 International Parkinson and Movement Disorder Society.