Showing papers on "Polysomnography published in 2020"
TL;DR: The question is: can the early and adequate treatment of insomnia prevent depression, and current understanding about sleep regulatory mechanisms with knowledge about changes in physiology due to depression are linked.
294 citations
TL;DR: A single-layered, ultra-soft, smart textile for all-around physiological parameters monitoring and healthcare during sleep is presented to pave a new and practical pathway for physiological monitoring during sleep.
201 citations
TL;DR: The 'International Biomarkers Workshop on Wearables in Sleep and Circadian Science' was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies and proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use.
Abstract: The "International Biomarkers Workshop on Wearables in Sleep and Circadian Science" was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: (1) characterize the term "wearable" for use in sleep and circadian science and identify relevant sleep and circadian metrics for wearables to measure; (2) assess the current use of wearables in sleep and circadian science; (3) identify current barriers for applying wearables to sleep and circadian science; and (4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms "wearables," "nearables," and "ingestibles." Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.
140 citations
TL;DR: These results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages and paves the way for, large-scale, longitudinal sleep studies.
Abstract: Study objectives The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical for advancing sleep science. The aim of this study was to assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-electroencephalographic (EEG) device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG) scored by five sleep experts. Methods A total of 25 subjects who completed an overnight sleep study at a sleep center while wearing both a PSG and the DH simultaneously have been included in the analysis. We assessed (1) similarity of measured EEG brain waves between the DH and the PSG; (2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG; and (3) the performance of the DH's automatic sleep staging according to American Academy of Sleep Medicine guidelines versus PSG sleep experts manual scoring. Results The mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of α was 15 ± 3.5%, 16 ± 4.3% for β, 16 ± 6.1% for λ, and 10 ± 1.4% for θ frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm, and 3.2 ± 0.6%, respectively. Automatic sleep staging reached an overall accuracy of 83.5 ± 6.4% (F1 score: 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the 5 sleep experts. Conclusions These results demonstrate the capacity of the DH to both monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for, large-scale, longitudinal sleep studies. Clinical trial registration NCT03725943.
130 citations
TL;DR: The clinical profile of the orexin receptor antagonist suvorexant for treating insomnia in patients with mild‐to‐moderate probable Alzheimer's disease (AD) dementia is evaluated.
Abstract: INTRODUCTION We evaluated the clinical profile of the orexin receptor antagonist suvorexant for treating insomnia in patients with mild-to-moderate probable Alzheimer's disease (AD) dementia. METHODS Randomized, double-blind, 4-week trial of suvorexant 10 mg (could be increased to 20 mg based on clinical response) or placebo in patients who met clinical diagnostic criteria for both probable AD dementia and insomnia. Sleep was assessed by overnight polysomnography in a sleep laboratory. The primary endpoint was change-from-baseline in polysomnography-derived total sleep time (TST) at week 4. RESULTS Of 285 participants randomized (suvorexant, N = 142; placebo, N = 143), 277 (97%) completed the trial (suvorexant, N = 136; placebo, N = 141). At week 4, the model-based least squares mean improvement-from-baseline in TST was 73 minutes for suvorexant and 45 minutes for placebo; (difference = 28 minutes [95% confidence interval 11-45], p < 0.01). Somnolence was reported in 4.2% of suvorexant-treated patients and 1.4% of placebo-treated patients. DISCUSSION Suvorexant improved TST in patients with probable AD dementia and insomnia.
93 citations
TL;DR: An 80% shutdown of sleep apnoea management throughout Europe is identified and most services have been limited to phone-based follow-up and the management of high-priority cases.
Abstract: Sleep disordered breathing (SDB) is highly prevalent with a male to female predominance of two to one, and is more common in middle-aged and elderly subjects [1]. Affected patients often present with comorbidities such as obesity, cardiovascular disease (systemic hypertension, heart failure, atrial fibrillation), and diabetes mellitus Type II [2]. The strong overlap between the profile for SDB patients and the identified risk factors for adverse outcomes of COVID-19 infection that include age, male gender, and cardio-metabolic comorbidity [3] suggest that SDB patients may benefit from effective therapy if confronted with COVID-19 infection [4].
85 citations
TL;DR: The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG, and represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring.
Abstract: Objective: Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification. This is supported by a substantial agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The “standardized” one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep—this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
83 citations
TL;DR: The data support the proposal that objective sleep markers could be part of a set of biomarkers that statistically forecast the longitudinal trajectory of cortical Aβ deposition in the human brain, and sleep may represent a potentially affordable, scalable, repeatable, and non-invasive tool for quantifying of Aβ pathological progression, prior to cognitive symptoms of Alzheimer's disease (AD).
82 citations
TL;DR: To evaluate the dose–response relationship of daridorexant, a new dual orexin receptor antagonist, on sleep variables in subjects with insomnia disorder, data are presented that show a positive relationship between dose and response.
Abstract: OBJECTIVE To evaluate the dose-response relationship of daridorexant, a new dual orexin receptor antagonist, on sleep variables in subjects with insomnia disorder. METHODS Adults (≤64 years) with insomnia disorder were randomized (1:1:1:1:1:1) to receive daily oral placebo, daridorexant (5, 10, 25, or 50mg), or 10mg zolpidem for 30 days. The primary efficacy outcome was the change in wake time after sleep onset from baseline to days 1 and 2. Secondary outcome measures were change in latency to persistent sleep from baseline to days 1 and 2, change in subjective wake time after sleep onset, and subjective latency to sleep onset from baseline to week 4. Safety was also assessed. RESULTS Of 1,005 subjects screened, 359 (64% female) were randomized and received ≥1 dose. A significant dose-response relationship (multiple comparison procedure-modeling, 2-sided p < 0.001) was found in the reduction of wake after sleep onset and latency to persistent sleep from baseline to days 1 and 2 with daridorexant. These reductions were sustained through to days 28 and 29 (p = 0.050 and p = 0.042, respectively). Similar dose-dependent relationships were observed for subjective wake after sleep onset and subjective latency to sleep onset. The incidence of treatment-emergent adverse events was 35%, 38%, 38%, and 34% in subjects treated with 5, 10, 25, and 50mg daridorexant, respectively, compared with 30% for placebo, and 40% for 10mg zolpidem. There were no clinically relevant treatment-related serious adverse events. Four subjects withdrew due to adverse events. INTERPRETATION Daridorexant induced a dose-dependent reduction in wake time after sleep onset in subjects with insomnia disorder (Clinicaltrials.gov NCT02839200). Ann Neurol 2020;87:347-356.
77 citations
TL;DR: In men, the hypoxic burden of sleep apnea was associated with incident HF after accounting for demographic factors, smoking, and co-morbidities and the findings suggest that quantification of an easily measured index ofSleep Apnea related hypoxias may be useful for identifying individuals at risk for heart disease while also suggesting targets for intervention.
73 citations
TL;DR: This state-of-the-art review is to provide an update on the evaluation and management of children with OSAS with emphasis on children with complex medical comorbidities and those with residual OSAS following first-line treatment.
Abstract: Obstructive sleep apnea syndrome (OSAS) is a common pediatric disorder characterized by recurrent events of partial or complete upper airway obstruction during sleep which result in abnormal ventilation and sleep pattern. OSAS in children is associated with neurobehavioral deficits and cardiovascular morbidity which highlights the need for prompt recognition, diagnosis, and treatment. The purpose of this state-of-the-art review is to provide an update on the evaluation and management of children with OSAS with emphasis on children with complex medical comorbidities and those with residual OSAS following first-line treatment. Proposed treatment strategies reflecting recommendations from a variety of professional societies are presented. All children should be screened for OSAS and those with typical symptoms (e.g., snoring, restless sleep, and daytime hyperactivity) or risk factors (e.g., neurologic, genetic, and craniofacial disorders) should undergo further evaluation including referral to a sleep specialist or pediatric otolaryngologist and overnight polysomnography, which provides a definitive diagnosis. A cardiology and/or endocrinology evaluation should be considered in high-risk children. For the majority of children, first-line treatment is tonsillectomy with or without adenoidectomy; however, some children exhibit multiple levels of airway obstruction and may require additional evaluation and management. Anti-inflammatory medications, weight loss, and oral appliances may be appropriate in select cases, particularly for mild OSAS. Following initial treatment, all children should be monitored for residual symptoms and polysomnography may be repeated to identify persistent disease, which can be managed with positive airway pressure ventilation and additional surgical approaches if required.
TL;DR: This sleep measurement method could be deployed in a simple wearable device to accurately estimate sleep onset and administer Intensive Sleep Retraining, power naps, and objective daytime sleepiness tests outside the laboratory setting.
TL;DR: There are significant sex differences in NREM-AHI levels and in physiological endotypes, and definitions that use 4%-desaturation criteria under-estimate AHI in women.
Abstract: STUDY OBJECTIVES The bases for sex disparities in obstructive sleep apnea (OSA), is poorly understood. We quantified the influences of event definitions, sleep-state, and body position on apnea-hypopnea indices (AHIs) in men and women, and evaluated sex differences in pathophysiological endotypes. METHODS Polysomnography (PSG) data were analyzed from 2057 participants from the multi-ethnic study of atherosclerosis. Alternative AHIs were compared using various desaturation and arousal criteria. Endotypes (loop gain, airway collapsibility, arousal threshold) were derived using breath-by-breath analysis of PSG signals. Regression models estimated the extent to which endotypes explained sex differences in AHI. RESULTS The sample (mean 68.5 ± 9.2 years) included 54% women. OSA (AHI4P ≥15/h, defined by events with ≥4% desaturations) was found in 41.1% men and 21.8% women. Compared to AHI4P, male/female AHI ratios decreased by 5%-10% when using 3%-desaturation and/or arousal criteria; p < 0.05. REM-OSA (REM-AHI ≥15/h) was similar in men and women regardless of event desaturation criteria. REM-AHI4P ≥15/h was observed in 57% of men and women each. In NREM, AHI4P in men was 2.49 (CI95: 2.25, 2.76) of that in women. Women demonstrated lower loop gain, less airway collapsibility, and lower arousal threshold in NREM (ps < 0.0005). Endotypes explained 30% of the relative sex differences in NREM-AHI4P. CONCLUSIONS There are significant sex differences in NREM-AHI levels and in physiological endotypes. Physiological endotypes explained a significant portion of the relative sex differences in NREM-AHI. Definitions that use 4%-desaturation criteria under-estimate AHI in women. Combining NREM and REM events obscures OSA prevalence in REM in women.
TL;DR: This review article summarizes the current state of knowledge concerning sleep-related endogenous cannabinoid function derived from research on humans and rodent models, and points out the potential benefits of acute cannabinoids for sleep improvement, but also the potential sleep-disruptive effects of withdrawal following chronic cannabinoid drug use.
Abstract: Sleep is a vital function of the nervous system that contributes to brain and bodily homeostasis, energy levels, cognitive ability, and other key functions of a variety of organisms. Dysfunctional sleep induces neural problems and is a key part of almost all human psychiatric disorders including substance abuse disorders. The hypnotic effects of cannabis have long been known and there is increasing use of phytocannabinoids and other formulations as sleep aids. Thus, it is crucial to gain a better understanding of the neurobiological basis of cannabis drug effects on sleep, as well as the role of the endogenous cannabinoid system in sleep physiology. In this review article, we summarize the current state of knowledge concerning sleep-related endogenous cannabinoid function derived from research on humans and rodent models. We also review information on acute and chronic cannabinoid drug effects on sleep in these organisms, and molecular mechanisms that may contribute to these effects. We point out the potential benefits of acute cannabinoids for sleep improvement, but also the potential sleep-disruptive effects of withdrawal following chronic cannabinoid drug use. Prescriptions for future research in this burgeoning field are also provided.
TL;DR: A neural network based on a convolutional network and attention mechanism to perform automatic sleep staging and the attention mechanism excels in learning inter- and intra-epoch features is proposed.
Abstract: Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods.
University of Wollongong1, Flinders University2, Royal Adelaide Hospital3, University of Sydney4, Royal North Shore Hospital5, Royal Perth Hospital6, Flinders Medical Centre7, Sir Charles Gairdner Hospital8, Memorial Hospital of South Bend9, University of Western Australia10, University of Washington11, Veterans Health Administration12
TL;DR: In this preliminary study of adults with moderate or severe OSA in whom conventional therapy had failed, combined palatal and tongue surgery, compared with medical management, reduced the number of apnea and hypopnea events and patient-reported sleepiness at 6 months.
Abstract: Importance Many adults with obstructive sleep apnea (OSA) use device treatments inadequately and remain untreated. Objective To determine whether combined palatal and tongue surgery to enlarge or stabilize the upper airway is an effective treatment for patients with OSA when conventional device treatment failed. Design, Setting, and Participants Multicenter, parallel-group, open-label randomized clinical trial of upper airway surgery vs ongoing medical management. Adults with symptomatic moderate or severe OSA in whom conventional treatments had failed were enrolled between November 2014 and October 2017, with follow-up until August 2018. Interventions Multilevel surgery (modified uvulopalatopharyngoplasty and minimally invasive tongue volume reduction; n = 51) or ongoing medical management (eg, advice on sleep positioning, weight loss; n = 51). Main Outcomes and Measures Primary outcome measures were the apnea-hypopnea index (AHI; ie, the number of apnea and hypopnea events/h; 15-30 indicates moderate and >30 indicates severe OSA) and the Epworth Sleepiness Scale (ESS; range, 0-24; >10 indicates pathological sleepiness). Baseline-adjusted differences between groups at 6 months were assessed. Minimal clinically important differences are 15 events per hour for AHI and 2 units for ESS. Results Among 102 participants who were randomized (mean [SD] age, 44.6 [12.8] years; 18 [18%] women), 91 (89%) completed the trial. The mean AHI was 47.9 at baseline and 20.8 at 6 months for the surgery group and 45.3 at baseline and 34.5 at 6 months for the medical management group (mean baseline-adjusted between-group difference at 6 mo, −17.6 events/h [95% CI, −26.8 to −8.4];P Conclusions and Relevance In this preliminary study of adults with moderate or severe OSA in whom conventional therapy had failed, combined palatal and tongue surgery, compared with medical management, reduced the number of apnea and hypopnea events and patient-reported sleepiness at 6 months. Further research is needed to confirm these findings in additional populations and to understand clinical utility, long-term efficacy, and safety of multilevel upper airway surgery for treatment of patients with OSA. Trial Registration Australian New Zealand Clinical Trials Registry:ACTRN12614000338662
TL;DR: It is shown that wearables provide acceptable sleep monitoring but with poor reliability, however, wearable mHealth devices appear to be promising tools for ecological monitoring.
Abstract: Background: Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health.
Objective: The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic.
Methods: We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults.
Results: The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable–based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography.
Conclusions: This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring.
TL;DR: This study suggests that PD patients have poor sleep quality and quantity, and identifies factors that contributed to heterogeneity between studies.
TL;DR: The single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method, which makes it computationally efficient and can be used for real-time sleep apnea detection.
TL;DR: It is demonstrated that yoga intervention in women can be beneficial when compared to non-active control conditions in term of managing sleep problems and moderator analyses suggest that participants in the non-breast cancer subgroup and participants inThe non-peri/postmenopausal subgroup were associated with greater benefits.
Abstract: To examine the effectiveness and safety of yoga of women with sleep problems by performing a systematic review and meta-analysis. Medline/PubMed, ClinicalKey, ScienceDirect, Embase, PsycINFO, and the Cochrane Library were searched throughout the month of June, 2019. Randomized controlled trials comparing yoga groups with control groups in women with sleep problems were included. Two reviewers independently evaluated risk of bias by using the risk of bias tool suggested by the Cochrane Collaboration for programming and conducting systematic reviews and meta-analyses. The main outcome measure was sleep quality or the severity of insomnia, which was measured using subjective instruments, such as the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), or objective instruments such as polysomnography, actigraphy, and safety of the intervention. For each outcome, a standardized mean difference (SMD) and confidence intervals (CIs) of 95% were determined. Nineteen studies in this systematic review included 1832 participants. The meta-analysis of the combined data conducted according to Comprehensive Meta-Analysis showed a significant improvement in sleep (SMD = − 0.327, 95% CI = − 0.506 to − 0.148, P < 0.001). Meta-analyses revealed positive effects of yoga using PSQI scores in 16 randomized control trials (RCTs), compared with the control group in improving sleep quality among women using PSQI (SMD = − 0.54; 95% CI = − 0.89 to − 0.19; P = 0.003). However, three RCTs revealed no effects of yoga compared to the control group in reducing insomnia among women using ISI (SMD = − 0.13; 95% CI = − 0.74 to 0.48; P = 0.69). Seven RCTs revealed no evidence for effects of yoga compared with the control group in improving sleep quality for women with breast cancer using PSQI (SMD = − 0.15; 95% CI = − 0.31 to 0.01; P = 0.5). Four RCTs revealed no evidence for the effects of yoga compared with the control group in improving the sleep quality for peri/postmenopausal women using PSQI (SMD = − 0.31; 95% CI = − 0.95 to 0.33; P = 0.34). Yoga was not associated with any serious adverse events. This systematic review and meta-analysis demonstrated that yoga intervention in women can be beneficial when compared to non-active control conditions in term of managing sleep problems. The moderator analyses suggest that participants in the non-breast cancer subgroup and participants in the non-peri/postmenopausal subgroup were associated with greater benefits, with a direct correlation of total class time with quality of sleep among other related benefits.
TL;DR: Death and brain damage were more likely to occur with unwitnessed events, no supplemental oxygen, lack of respiratory monitoring, and coadministration of opioids and sedatives in OSA patients following surgery.
Abstract: Background Obstructive sleep apnea (OSA) patients are at increased risk for pulmonary and cardiovascular complications; perioperative mortality risk is unclear. This report analyzes cases submitted to the OSA Death and Near Miss Registry, focusing on factors associated with poor outcomes after an OSA-related event. We hypothesized that more severe outcomes would be associated with OSA severity, less intense monitoring, and higher cumulative opioid doses. Methods Inclusion criteria were age ≥18 years, OSA diagnosed or suspected, event related to OSA, and event occurrence 1992 or later and Results Sixty-six cases met inclusion criteria with known OSA diagnosed in 55 (83%). Patients were middle aged (mean = 53, standard deviation [SD] = 15 years), American Society of Anesthesiologists (ASA) III (59%, n = 38), and obese (mean body mass index [BMI] = 38, SD = 9 kg/m); most had inpatient (80%, n = 51) and elective (90%, n = 56) procedures with general anesthesia (88%, n = 58). Most events occurred on the ward (56%, n = 37), and 14 (21%) occurred at home. Most events (76%, n = 50) occurred within 24 hours of anesthesia end. Ninety-seven percent (n = 64) received opioids within the 24 hours before the event, and two-thirds (41 of 62) also received sedatives. Positive airway pressure devices and/or supplemental oxygen were in use at the time of critical events in 7.5% and 52% of cases, respectively. Sixty-five percent (n = 43) of patients died or had brain damage; 35% (n = 23) experienced other critical events. Continuous central respiratory monitoring was in use for 3 of 43 (7%) of cases where death or brain damage resulted. Death or brain damage was (1) less common when the event was witnessed than unwitnessed (OR = 0.036; 95% CI, 0.007-0.181; P Conclusions Death and brain damage were more likely to occur with unwitnessed events, no supplemental oxygen, lack of respiratory monitoring, and coadministration of opioids and sedatives. It is important that efforts be directed at providing more effective monitoring for OSA patients following surgery, and clinicians consider the potentially dangerous effects of opioids and sedatives-especially when combined-when managing OSA patients postoperatively.
TL;DR: It is argued that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences, and age-adapted methodological approaches are required to foster theDevelopment of valid and reliable biomarkers of age-associated cognitive pathologies.
Abstract: In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences. Age-adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age-associated cognitive pathologies.
22 Jul 2020
TL;DR: It is highlighted that state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from obstructive sleep apnea.
Abstract: Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85% only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches to a new deep learning method, SimpleSleepNet, which reach state-of-the-art performances while being more lightweight. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89.9% vs 86.8% on average for human scorers on DOD-H, and an F1 of 88.3% vs 84.8% on DOD-O. Our study highlights that state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Considerations could be made to use automated approaches in the clinical setting.
TL;DR: The SDB-associated brain changes in older adults who are cognitively unimpaired include greater amyloid deposition and neuronal activity in Alzheimer disease-sensitive brain regions, notably the posterior cingulate cortex and precuneus.
Abstract: Importance Increasing evidence suggests that sleep-disordered breathing (SDB) increases the risk of developing Alzheimer clinical syndrome. However, the brain mechanisms underlying the link between SDB and Alzheimer disease are still unclear. Objective To determine which brain changes are associated with the presence of SDB in older individuals who are cognitively unimpaired, including changes in amyloid deposition, gray matter volume, perfusion, and glucose metabolism. Design, Setting, and Participants This cross-sectional study was conducted using data from the Age-Well randomized clinical trial of the Medit-Ageing European project, acquired between 2016 and 2018 at Cyceron Center in Caen, France. Community-dwelling older adults were assessed for eligibility and were enrolled in the Age-Well clinical trial if they did not meet medical or cognitive exclusion criteria and were willing to participate. Participants who completed a detailed neuropsychological assessment, polysomnography, a magnetic resonance imaging, and florbetapir and fluorodeoxyglucose positron emission tomography scans were included in the analyses. Main Outcomes and Measures Based on an apnea-hypopnea index cutoff of 15 events per hour, participants were classified as having SDB or not. Voxelwise between-group comparisons were performed for each neuroimaging modality, and secondary analyses aimed at identifying which SDB parameter (sleep fragmentation, hypoxia severity, or frequency of respiratory disturbances) best explained the observed brain changes and assessing whether SDB severity and/or SDB-associated brain changes are associated with cognitive and behavioral changes. Results Of 157 participants initially assessed, 137 were enrolled in the Age-Well clinical trial, and 127 were analyzed in this study. The mean (SD) age of the 127 participants was 69.1 (3.9) years, and 80 (63.0%) were women. Participants with SDB showed greater amyloid burden (t114 = 4.51; familywise error–correctedP = .04; Cohend,0.83), gray matter volume (t119 = 4.12; familywise error–correctedP = .04; Cohend, 0.75), perfusion (t116 = 4.62; familywise error–correctedP = .001; Cohend, 0.86), and metabolism (t79 = 4.63; familywise error–correctedP = .001; Cohend, 1.04), overlapping mainly over the posterior cingulate cortex and precuneus. No association was found with cognition, self-reported cognitive and sleep difficulties, or excessive daytime sleepiness symptoms. Conclusions and Relevance The SDB-associated brain changes in older adults who are cognitively unimpaired include greater amyloid deposition and neuronal activity in Alzheimer disease–sensitive brain regions, notably the posterior cingulate cortex and precuneus. These results support the need to screen and treat for SDB, especially in asymptomatic older populations, to reduce Alzheimer disease risk. Trial Registration ClinicalTrials.gov Identifier:NCT02977819
TL;DR: Dose-dependent improvements in WASO and LPS were statistically significant (dose range 10–50 mg) in elderly people with insomnia disorder, and daridorexant was well tolerated.
Abstract: Objective To assess the dose-response of daridorexant, a new dual orexin receptor antagonist, on wake after sleep onset (WASO). Methods Elderly (≥65 years) participants (n = 58) with insomnia were randomly allocated (Latin square design) to receive 5 treatments (5, 10, 25, and 50 mg daridorexant and placebo) during 5 treatment periods, each consisting of 2 treatment nights followed by a 5- to 12-day washout period. Main efficacy endpoints were the absolute change from baseline in WASO (primary) and latency to persistent sleep (LPS; secondary) to days 1 and 2 (mean of 2 treatment nights assessed by polysomnography) in each period. Safety and tolerability were also assessed. Results Of 58 participants included, 67% were female, and the median age was 69 years (range 65–85 years). WASO and LPS were dose-dependently reduced from baseline to days 1 and 2 after daridorexant administration (multiple comparison procedure modeling, p Conclusions Daridorexant was well tolerated. Dose-dependent improvements in WASO and LPS were statistically significant (dose range 10–50 mg) in elderly people with insomnia disorder. ClinicalTrials.gov identifier: NCT02841709. Classification of evidence This study provides Class I evidence that, for elderly people with insomnia, daridorexant reduced WASO.
TL;DR: Findings show that compared to healthy controls, those with MCI have pronounced changes in sleep macro-architecture with greater wake after sleep onset, reduced total sleep time, lower sleep efficiency, longer sleep onset latency, longer rapid eye movement sleep (REM) latency, reduced REM sleep, greater N1 sleep, and worse severity of hypoxemia.
TL;DR: The proposed approach allows automatic sleep stage classification by multi-channel PSG signals with different criteria standards, signal characteristics, and epoch divisions, and it has the potential to exploit sleep information comprehensively.
Abstract: Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach based on a hierarchical neural network to process multi-channel PSG signals for improving the performance of automatic five-class sleep staging. The proposed hierarchical network contains two stages: comprehensive feature learning stage and sequence learning stage. The first stage is used to obtain the feature matrix by fusing the hand-crafted features and network trained features. A multi-flow recurrent neural network (RNN) as the second stage is utilized to fully learn temporal information between sleep epochs and fine-tune the parameters in the first stage. The proposed model was evaluated by 147 full night recordings in a public sleep database, the Montreal Archive of Sleep Studies (MASS). The proposed approach can achieve the overall accuracy of 0.878, and the F1-score is 0.818. The results show that the approach can achieve better performance compared to the state-of-the-art methods. Ablation experiment and model analysis proved the effectiveness of different components of the proposed model. The proposed approach allows automatic sleep stage classification by multi-channel PSG signals with different criteria standards, signal characteristics, and epoch divisions, and it has the potential to exploit sleep information comprehensively.
TL;DR: Synchronised Somnofy showed a high accuracy staging sleep in healthy individuals and has potential to assess sleep quality and quantity in a sample of healthy, mostly young adults.
TL;DR: FBA underestimates these sleep stages and overestimates light sleep, demonstrating more shallow sleep than actually obtained, and whether FBA could serve as a low‐cost substitute for actigraphy in insomnia requires further investigation.
Abstract: Consumer activity trackers claiming to measure sleep/wake patterns are ubiquitous within clinical and consumer settings. However, validation of these devices in sleep disorder populations are lacking. We examined 1 night of sleep in 42 individuals with insomnia (mean = 49.14 ± 17.54 years) using polysomnography, a wrist actigraph (Actiwatch Spectrum Pro: AWS) and a consumer activity tracker (Fitbit Alta HR: FBA). Epoch-by-epoch analysis and Bland-Altman methods evaluated each device against polysomnography for sleep/wake detection, total sleep time, sleep efficiency, wake after sleep onset and sleep latency. FBA sleep stage classification of light sleep (N1 + N2), deep sleep (N3) and rapid eye movement was also compared with polysomnography. Compared with polysomnography, both activity trackers displayed high accuracy (81.12% versus 82.80%, AWS and FBA respectively; ns) and sensitivity (sleep detection; 96.66% versus 96.04%, respectively; ns) but low specificity (wake detection; 39.09% versus 44.76%, respectively; p = .037). Both trackers overestimated total sleep time and sleep efficiency, and underestimated sleep latency and wake after sleep onset. FBA demonstrated sleep stage sensitivity and specificity, respectively, of 79.39% and 58.77% (light), 49.04% and 95.54% (deep), 65.97% and 91.53% (rapid eye movement). Both devices were more accurate in detecting sleep than wake, with equivalent sensitivity, but statistically different specificity. FBA provided equivalent estimates as AWS for all traditional actigraphy sleep parameters. FBA also showed high specificity when identifying N3, and rapid eye movement, though sensitivity was modest. Thus, it underestimates these sleep stages and overestimates light sleep, demonstrating more shallow sleep than actually obtained. Whether FBA could serve as a low-cost substitute for actigraphy in insomnia requires further investigation.
TL;DR: This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals and improves the accuracy of sleep Apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.
Abstract: Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.