Laboratory-based Polysomnography Research Articles

Gut microbiota changes in healthy individuals, obstructive sleep apnea patients, and patients treated using continuous positive airway pressure: a whole-genome metagenomic analysis.

This study investigated variations in gut microbiota among severe obstructive sleep apnea (OSA) patients and changes in gut microbiota after continuous positive airway pressure (CPAP) treatment. From November 2020 to August 2021, laboratory-based polysomnography (PSG) was used to measure sleep parameters in healthy controls, severe OSA patients, and severe OSA patients treated with CPAP for three months. A fully automated biochemical analyzer was used to evaluate routine blood tests and biochemical indicators. Whole-genome metagenomic analysis was used to determine the microbial composition of gut samples from all participants. The relationships between gut microbiota and hypertension were examined using correlation analysis. The relative abundances of Bacteroides, Firmicutes, and Parabacteroides were significantly lower at the species level. Enterobacterales and Turicibacter were significantly higher in participants with severe OSA than healthy controls. Negative correlations were identified between Bacteroides coprocola and systolic blood pressure (SBP) (r = - 0.710, P = 0.003) and diastolic blood pressure (DBP) (r = - 0.615, P = 0.015). Conversely, a positive correlation was found between Escherichia coli and SBP (r = 0.568, P = 0.027). The metabolic pathways and gut microbiota differed significantly between the control group and individuals with severe OSA. Additionally, CPAP therapy substantially changed the metabolic pathways and gut microbial composition among patients diagnosed with severe OSA. Correlation analysis further revealed a strong association between Escherichia coli, Bacteroides coprocola, and blood pressure levels.

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study

BackgroundSleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals.MethodsThis was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65–74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied.ResultsWe included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration.ConclusionWe observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.

Brain metabolites are associated with sleep architecture and cognitive functioning in older adults.

Sleep deficits are a possible risk factor for development of cognitive decline and dementia in older age. Research suggests that neuroinflammation may be a link between the two. This observational, cross-sectional study evaluated relationships between sleep architecture, neuroinflammation and cognitive functioning in healthy older adults. Twenty-two adults aged ≥60 years underwent whole-brain magnetic resonance spectroscopic imaging (in vivo method of visualizing increased brain temperatures as a proxy for neuroinflammation), supervised laboratory-based polysomnography, and comprehensive neurocognitive testing. Multiple regressions were used to assess relationships between magnetic resonance spectroscopic imaging-derived brain temperature and metabolites related to inflammation (choline; myo-inositol; N-acetylaspartate), sleep efficiency, time and % N3 sleep and cognitive performance. Choline, myo-inositol and N-acetylaspartate were associated with sleep efficiency and cognitive performance. Higher choline and myo-inositol in the bilateral frontal lobes were associated with slower processing speed and lower sleep efficiency. Higher choline and myo-inositol in bilateral frontoparietal regions were associated with better cognitive performance. Higher N-acetylaspartate around the temporoparietal junction and adjacent white matter was associated with better visuospatial function. Brain temperature was not related to cognitive or sleep outcomes. Our findings are consistent with the limited literature regarding neuroinflammation and its relationships with sleep and cognition in older age, which has implicated ageing microglia and astrocytes in circadian dysregulation, impaired glymphatic clearance and increased blood-brain barrier integrity, with downstream effects of neurodegeneration and cognitive decline. Inflammatory processes remain difficult to measure in the clinical setting, but magnetic resonance spectroscopic imaging may serve as a marker of the relationship between neuroinflammation, sleep and cognitive decline in older adults.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Time in Bed Comparisons Between Two Automated Algorithms Applied to activPAL Data and Observations in a Polysomnography Lab

Objective: This study compared estimates of time in bed (TIB) and TIB start and end from two algorithms applied to activPAL data with observations of TIB in a polysomnography (PSG) lab. Methods: Twenty-five healthy volunteers (age: 32.4 ± 7.4 years, body mass index: 25.2 ± 3.7 kg/m2) wore a thigh-worn activPAL accelerometer during one-night laboratory-based PSG assessment. TIB and TIB start and end estimates were generated using automated algorithms within Processing PAL and PAL Technologies (PAL Batch software, version 8) applications. Agreement was determined using pairwise 95% equivalence tests (±10% equivalence zone), mean percentage absolute error, intraclass correlation coefficients (ICCs), and 95% limits of agreement. Results: The results for the algorithms were similar. TIB start and end were within the proposed ±10% equivalence zone of the PSG-lab observations, but TIB was not when using either algorithm. Mean percentage absolute errors for both algorithms were approximately 10%, 3%, and 1% for TIB and TIB start and end, respectively. Reliability between both algorithms and PSG lab was poor for TIB (ICC ≥ .39) and TIB start (ICC ≥ .38) and good for TIB end (ICC ≥ .81). Both algorithms recorded more TIB by about 42 ± 60 min and detected earlier TIB start and later TIB end by about 18 ± 62 and 14 ± 9 min, respectively, compared with PSG. Negative fixed biases (PSG–activPAL) were observed for TIB and TIB end from both algorithms (p &lt; .05). For both algorithms, 95% limits of agreement were ±120 min for TIB and ±125 min for TIB start. Removing two outliers improved the agreement between both algorithms and PSG-lab observations. Conclusions: Processing PAL and PAL Technologies algorithms recorded more TIB by ∼40 min compared with PSG-lab TIB. Researchers should be aware of this discrepancy when using these algorithms and utilize predefined quality control protocols to enhance data quality. Future studies should continue refining these algorithms.

Evaluation of Minimum Recording Time and the Influence of Time in the Supine Position on Out-of-Center Sleep Tests.

Introduction The prevalence of moderate to severe sleep-disordered breathing is of 17% among men aged between 50 and 70-years, and of 9% among women in the same age group. In Brazil, obstructive sleep apnea (OSA) is also highly prevalent, and it is associated with metabolic and cardiovascular impacts, excessive daytime sleepiness, and increasing risk of traffic accidents. Laboratory-based polysomnography is the gold standard test for OSA diagnosis. However, its complexity has led to the search for alternatives to simplify the diagnosis, such as the out-of-center sleep test (OCST). Objectives To discusses the minimum OCST recording time and the potential effects of the supine position on this parameter. Data Synthesis We conducted a search on the PubMed, Web of Science, Scopus, and Embase databases to identify relevant studies on OCST recording time and a possible association with body position. We used a combination of terms, including Obstructive Sleep Apnea and Home Monitoring OR Home Care Services OR Portable Monitoring AND Supine OR Position OR Recording Time OR Positional Obstructive Sleep Apnea . The references of the selected articles were also reviewed to find other relevant studies. Through our approach, eighteen articles were retrieved and included in the present study. Conclusion Since OCSTs are conducted in an unattended environment, with potential signal loss during the night, it is crucial to determine the minimum recording time to validate the test and assess how the time spent in the supine position affects this parameter. After reviewing the literature, this topic remains to be clarified, and additional studies should focus on that matter.

Sleep characteristics and parameters of bone turnover and strength in the adult population: results from the Study of Health in Pomerania-TREND.

Poor sleep quality or sleep deprivation may be related to decreased bone mineral density. We aimed to assess whether associations of sleep characteristics and bone turnover or strength are present in adults from the general population and whether these are independent of common risk factors such as sex, age, and obesity. A total of 1037 participants from the Study of Health in Pomerania-TREND underwent laboratory-based polysomnography and quantitative ultrasound measurements at the heel. Of these participants, 804 completed standardised questionnaires to assess daytime sleepiness, insomnia, and sleep quality. Serum concentrations of two bone turnover markers, intact amino-terminal propeptide of type 1 procollagen (P1NP) and carboxy-terminal telopeptide of type 1 collagen (CTX) were measured. Cross-sectional associations of polysomnography variables (total sleep time, sleep efficiency, time spent wake after sleep onset, oxygen desaturation index, apnea-hypopnea index, and obstructive sleep apnea [OSA]), as well as sleep questionnaire scores with the bone turnover markers and the ultrasound-based stiffness index were assessed in linear regression models. In adjusted models, higher insomnia scores and lower sleep quality scores were related to a higher bone turnover in women but not in men. However, associations between polysomnography variables or questionnaire scores and the stiffness index were absent. Our study provides limited evidence for relationships between sleep characteristics and bone turnover and strength independent of common risk factors for OSA and osteoporosis. Nevertheless, women reporting poor sleep or insomnia in combination with risk factors for osteoporosis might benefit from an evaluation of bone health.

0280 Unlocking the Potential of Consumer Wearables for Predicting Sleep in Children: A Device-Agnostic Machine Learning Approach

Abstract Introduction Consumer wearables use accelerometry and heart rate to predict sleep, the same data signals used by research-grade devices to classify sleep/wake patterns. However, metrics predicted by consumer wearables are based on black box algorithms limiting their use in research. The objective of this study was to develop algorithms for predicting sleep in children based on the raw accelerometry and heart rate data from a popular consumer wearable device, therefore bypassing the onboard black box algorithms. Methods 38 children (M=8.5 years, SD=2.4, 42% black, 61% male) underwent overnight laboratory-based polysomnography (PSG) while wearing an Apple Watch Series 7. Heart rate and accelerometry data were collected via the Apple Watch application program interface. Features extracted included (1) age, (2) heart rate, (3) y-axis offset angle, (4) y-axis angle relative to x-axis (y-angle), (5) x-axis fast Fourier transformation (FFT) 4Hz, (6) x-axis FFT 9Hz, (7) vector magnitude (VM) FFT 9Hz, (8) VM FFT 14Hz, (9) mean power dispersion (MPD), (10) bandpass filter followed by Euclidean norm/vector magnitude (BFEN), (11) dominant signal power at 0.6–2.5Hz (PMAXBAND), and (12) activity counts. Four machine learning models: logistic regression (LR), K-nearest neighbor (KNN), random forest (RF), and neural network (NN) predicted sleep or wake. Model performance was evaluated by F1 score, precision, sensitivity, and specificity. Feature importance was evaluated using the RF model. Results LR achieved the best performance according to F1 score (RF: 85.04, LR: 86.50, KNN: 82.52, NN: 85.13). LR also had the highest precision (RF:88.33, LR:88.94, KNN: 86.77, NN:87.95), and nearly the highest sensitivity (RF:84.52, LR:86.11, KNN:84.73, NN:86.55) and specificity (RF:75.72, LR:75.00, KNN:54.01, NN:60.57). BFEN was the most important feature, followed by MPD, age, PMAXBAND, VM FFT 14Hz, x-axis FFT 9Hz, heart rate, y-axis offset angle, y-angle, activity count, VM FFT 9Hz, x-axis FFT 4Hz. Conclusion Raw accelerometry data combined with heart rate data from the Apple Watch Series 7 was able to detect sleep and wake in children. Leveraging the raw accelerometry data is a viable alternative to relying on black-box algorithms for sleep classification in consumer wearable devices. Support (if any)

Overview of a Sleep Monitoring Protocol for a Large Natural Population.

A standard operating procedure for studying the sleep phenotypes in a large population cohort is proposed. It is intended for academic researchers in investigating the sleep phenotypes in conjunction with the clinical sleep disorders assessment guidelines. The protocol refers to the definitive American Academy of Sleep Medicine (AASM) manual for setting polysomnography (PSG) technical specifications, scoring of sleep and associated events, etc. On this basis, it not only provides a standardized procedure of sleep interview, sleep-relevant questionnaires, and laboratory-based PSG test, but also offers a comprehensive process of sleep data analysis, phenotype extraction, and data storage. Both the objective sleep data recorded by PSG test and subjective sleep information obtained by the sleep interview and sleep questionnaires are involved in the data acquisition procedure. Subsequently, sleep phenotypes can be characterized by observable/inconspicuous physiological patterns during sleep from PSG test or can be marked by sleeping habits like sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, daytime dysfunction, etc., from sleep interview or questionnaires derived. In addition, solutions to the problems that may be encountered during the protocol are summarized and addressed. With the protocol, it can significantly improve scientific research efficiency and reduce unnecessary workload in large population cohort studies. Moreover, it is also expected to provide a valuable reference for researchers to conduct systematic sleep research.

Preliminary evidence of transcutaneous vagus nerve stimulation effects on sleep in veterans with post-traumatic stress disorder.

Sleep problems are common among veterans with post-traumatic stress disorder and closely associated with hyperarousal symptoms. Transcutaneous vagus nerve stimulation (tVNS) may have potential to improve sleep quality in veterans with PTSD through effects on brain systems relevant to hyperarousal and sleep-wake regulation. The current pilot study examines the effect of 1 h of tVNS administered at "lights out" on sleep architecture, microstructure, and autonomic activity. Thirteen veterans with PTSD completed two nights of laboratory-based polysomnography during which they received 1 h of either active tVNS (tragus) or sham stimulation (earlobe) at "lights out" with randomised order. Sleep staging and stability metrics were derived from polysomnography data. Autonomic activity during sleep was assessed using the Porges-Bohrer method for calculating respiratory sinus arrhythmia (RSAP-B ). Paired t-tests revealed a small decrease in the total sleep time (d=-0.31), increase in N3 sleep (d=0.23), and a small-to-moderate decrease in REM sleep (d=-0.48) on nights of active tVNS relative to sham stimulation. tVNS was also associated with a moderate reduction in cyclic alternating pattern (CAP) rate (d=-0.65) and small-to-moderate increase in RSAP-B during NREM sleep. Greater NREM RSAP-B was associated with a reduced CAP rate and NREM alpha power. This pilot study provides preliminary evidence that tVNS may improve sleep depth and stability in veterans with PTSD, as well as increase parasympathetically mediated nocturnal autonomic activity. These results warrant continued investigation into tVNS as a potential tool for treating sleep disturbance in veterans with PTSD.

Evaluation of Cardiac Findings Before Laboratory-Based Polysomnography

Evaluation of Cardiac Findings Before Laboratory-Based Polysomnography

Ambulatory transcutaneous carbon dioxide monitoring for children with neuromuscular disease

ObjectiveEarly screening and diagnosis of nocturnal hypoventilation can slow progression to diurnal hypercapnia and mortality in children with neuromuscular disease (NMD). However, gold standard, laboratory-based polysomnography (PSG) testing is a limited resource. Therefore, we evaluated the diagnostic accuracy of ambulatory transcutaneous carbon dioxide (tcCO2) monitoring used in the home compared to PSG in children with NMD. MethodsProspective, cross-sectional study in children 0–18 years old with a confirmed diagnosis of NMD and a clinically indicated need for PSG. Ambulatory tcCO2 was assessed by a respiratory therapist in participant's homes. Demographics, and PSG (including tcCO2). ResultsWe enrolled 39 children with NMD; 3 had unusable ambulatory tcCO2 data because of failure of drift correction on the machine (n = 2) or an air bubble (n = 1). The remaining 36 patients aged 11 months to 16 years (median (IQR) 12.5 years (6.0–15.8)) had ambulatory tcCO2 and outpatient level 1 PSG data. Ambulatory tcCO2 monitoring had a sensitivity of 20.0% (95% confidence interval [CI] 0.5–71.6%) and a specificity of 93.5% (95% CI 78.6–99.2%). Almost all children and/or parents (34/36, 94%) preferred ambulatory monitoring over in-hospital PSG. ConclusionsAmbulatory transcutaneous carbon dioxide monitoring was not sufficiently accurate as a clinical tool for the diagnosis of nocturnal hypoventilation our cohort of children with neuromuscular disease despite being preferred over PSG by both children and parents.

Validation of an automated sleep detection algorithm using data from multiple accelerometer brands.

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5[7.2]years, body mass index 25.5[3.7]kg/m2 ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

Fetal heart rate responses to maternal sleep-disordered breathing

Maternal sleep-disordered breathing is associated with adverse pregnancy outcomes and is considered to be deleterious to the developing fetus. Maternal obesity potentiates sleep-disordered breathing, which, in turn, may contribute to the effect of maternal obesity on adverse fetal outcomes. However, only a few empirical studies have evaluated the contemporaneous effects of maternal sleep-disordered breathing events on fetal well-being. These events include apnea and hypopnea with accompanying desaturations in oxyhemoglobin. This study aimed to reconcile contradictory findings on the associations between maternal apnea or hypopnea events and clinical indicators of fetal compromise. It also sought to broaden the knowledge base by examining the fetal heart rate and heart rate variability before, during, and after episodes of maternal apnea or hypopnea. To accomplish this, we employed overnight polysomnography, the gold standard for ascertaining maternal sleep-disordered breathing, and synchronized it with continuous fetal electrocardiography. A total of 84 pregnant women with obesity (body mass index >30 kg/m2) participated in laboratory-based polysomnography with digitized fetal electrocardiography recordings during or near 36 weeks of gestation. Sleep was recorded, on average, for 7 hours. Decelerations in fetal heart rate were identified. Fetal heart rate and heart rate variability were quantified before, during, and after each apnea or hypopnea event. Event-level intensity (desaturation magnitude, duration, and nadir O2 saturation level) and person-level characteristics based on the full overnight recording (apnea-hypopnea index, mean O2 saturation, and O2 saturation variability) were analyzed as potential moderators using linear mixed effects models. A total of 2936 sleep-disordered breathing events were identified, distributed among all but 2 participants. On average, participants exhibited 8.7 episodes of apnea or hypopnea per hour (mean desaturation duration, 19.1 seconds; mean O2 saturation nadir, 86.6% per episode); nearly half (n=39) of the participants met the criteria for obstructive sleep apnea. Only 45 of 2936 apnea or hypopnea events were followed by decelerations (1.5%). Conversely, most (n=333, 88%) of the 378 observed decelerations, including the prolonged ones, did not follow an apnea or a hypopnea event. Maternal sleep-disordered breathing burden, body mass index, and fetal sex were unrelated to the number of decelerations. Fetal heart rate variability increased during events of maternal apnea or hypopnea but returned to initial levels soon thereafter. There was a dose-response association between the size of the increase in fetal heart rate variability and the maternal apnea-hypopnea index, event duration, and desaturation depth. Longer desaturations were associated with a decreased likelihood of the variability returning to baseline levels after the event. The mean fetal heart rate did not change during episodes of maternal apnea or hypopnea. Episodes of maternal sleep apnea and hypopnea did not evoke decelerations in the fetal heart rate despite the predisposing risk factors that accompany maternal obesity. The significance of the modest transitory increase in fetal heart rate variability in response to apnea and hypopnea episodes is not clear but may reflect compensatory, delimited autonomic responses to momentarily adverse conditions. This study found no evidence that episodes of maternal sleep-disordered breathing pose an immediate threat, as reflected in fetal heart rate responses, to the near-term fetus.

Combining Cognitive-behavioral Therapy For Insomnia With Exercise Training In Adults With Insomnia And Short Sleep Duration: Impact On Sleep Outcomes

Cognitive-behavioral therapy for insomnia (CBT-I) is the first-line treatment for insomnia; however, there is mixed evidence as to whether CBT-I efficacy is blunted in adults with insomnia and short sleep duration. Exercise training may be a useful adjunct treatment to CBT-I for this population. PURPOSE: To explore changes in actigraphic- and polysomnographic-based sleep outcomes from a CBT-I intervention augmented by exercise training in a sample of adults with insomnia and short sleep duration. METHODS: Eight adults (50% female, 62.5% white) with insomnia disorder and short sleep duration (self-reported sleep duration <6 h) completed a 12-week single-arm trial. Participants were guided through the online “Sleep Healthy Using the Internet” (SHUT-I) CBT-I program while completing a supervised exercise program (EX; 150 min/wk of moderate-intensity aerobic exercise and 2 d/wk of resistance training). Prior to and following the intervention, participants completed 1 wk of home-based actigraphy monitoring and 1 night of laboratory-based polysomnography (PSG). Post-intervention PSG data were missing for 1 participant due to COVID lockdown restrictions. Differences between baseline and post-intervention sleep were analyzed using Cohen’s d effect size calculations and paired t-tests. RESULTS: At baseline, participants had actigraphy-assessed total sleep time (TST) of 6.9 ± 0.6 h and sleep efficiency (SE) of 86.6 ± 4.1%; PSG-based TST and SE were 6.9 ± 0.9 h and 87.6 ± 6.1%, respectively. At post-intervention, actigraphy-assessed TST decreased by 15.9 min (d = 0.40, p = .29) and SE increased by 1.4% (d = 0.92, p = .08) relative to baseline. For PSG-based outcomes, TST decreased by 27 min (d = -1.48, p = .08) and SE increased by 3.9% (d = 0.91, p = .05) at post-intervention. In addition, the proportion of time spent in non-REM (NREM) stage 1 sleep decreased by 2.0% (d = -1.05, p = .03) and the proportion of time spent in NREM stage 2 sleep increased by 3.0% (d = 0.82, p = .07) at post-intervention. CONCLUSIONS: These preliminary data suggest improvements in sleep continuity following the combination of CBT-I and exercise training, accompanied by small decreases in sleep duration. Future research should disentangle the independent effects of CBT-I and exercise on sleep outcomes in this population. Support: NIH K23 HL118318 (Kline)

CPAP setup by mail: we're not there yet.

CPAP setup by mail: we're not there yet.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

Validation of Somno-Art Software, a novel approach of sleep staging, compared with polysomnography in disturbed sleep profiles.

Integrated analysis of heart rate (electrocardiogram [ECG]) and body movements (actimetry) during sleep in healthy subjects have previously been shown to generate similar evaluation of sleep architecture and continuity with Somno-Art Software compared to polysomnography (PSG), the gold standard. However, the performance of this new approach of sleep staging has not yet been evaluated on patients with disturbed sleep. Sleep staging from 458 sleep recordings from multiple studies comprising healthy and patient population (obstructive sleep apnea [OSA], insomnia, major depressive disorder [MDD]) was obtained from PSG visual scoring using the American Academy of Sleep Medicine rules and from Somno-Art Software analysis on synchronized ECG and actimetry. Inter-rater reliability (IRR), evaluated with 95% absolute agreement intra-class correlation coefficient, was rated as "excellent" (ICCAAAvg95% ≥ 0.75) or "good" (ICCAAAvg95% ≥ 0.60) for all sleep parameters assessed, except non-REM (NREM) and N3 sleep in healthy participants (ICCAAAvg95% = 0.43, ICCAAAvg95% = 0.56) and N3 sleep in OSA patients (ICCAAAvg95% = 0.59) rated as "fair" IRR. Overall sensitivity, specificity, accuracy, and Cohen's kappa coefficient of agreement (κ) on the entire sample were respectively of 93.3%, 69.5%, 87.8%, and 0.65 for wake/sleep classification and accuracy and κ were of 68.5% and 0.55 for W/N1+N2/N3/rapid eye movement (REM) classification. These performances were similar in healthy and patient population. The present results suggest that Somno-Art can be a valid sleep-staging tool in both healthy subjects and patients with OSA, insomnia, or MDD. It could complement existing non-attended techniques measuring sleep-related breathing patterns or be a useful alternative to laboratory-based PSG when this latter is not available.

Cohort profile: The Western Australian Sleep health study, a prospective sleep clinic cohort study

Genetic and epidemiologic investigations into obstructive sleep apnoea (OSA) have been limited by a scarcity of sizeable well-characterised sleep clinic cohorts with laboratory-based polysomnography (PSG). This profile reports the characteristics of a prospective clinic cohort study exploring the genotypic and phenotypic features of OSA with ongoing patient follow-up to assess long-term health outcomes. The Western Australian Sleep Health Study (WASHS) recruited patients at a large tertiary hospital sleep clinic in Perth, Australia. Between 2006 and 2010, 5948 consecutive new adult patients attended the clinic and 4914 were eligible to participate following consent and screening. Among eligible patients, 98.5% (n = 4839) had diagnostic PSG available, and 86.0% (n = 4226) were comprehensively phenotyped by clinical questionnaire and anthropometric measurements. Among those comprehensively phenotyped, blood biospecimens for biochemistry and DNA were obtained in 2759 (65.2%), and linked health administrative data was requested in 2017 for 4067 patients (96.2%). The group of most interest, the comprehensively phenotyped patients (n = 4226), were predominantly male (60.6%), middle-aged (mean±SD: 50.5 ± 14.0 years), and obese (32.7 ± 7.7 kg/m2). The majority of this group were diagnosed with OSA (93.8%). The WASHS Prospective Sleep Clinic Cohort is amongst the largest OSA cohorts globally with PSG and long-term morbidity and mortality data. Comprehensive phenotype and genotype data have contributed to numerous publications on the epidemiology and genetics of OSA. Patients have been monitored by ongoing clinic review, where OSA treatment data is collected, and by follow-up studies, such as an Australian National Health and Medical Research Council funded project (2018–2021) investigating cardiovascular outcomes in OSA.