Sleep-related Respiratory Disorders Research Articles

Prevalence of Obstructive Sleep Apnea in 6–9-Year-Old Children of Visnagar: A Cross-Sectional Study

Background and Objective The prevalence of sleep-disordered breathing is extremely high, yet it is largely ignored. Significant adverse medical and psychological outcomes are associated with sleep-related respiratory disorders, including obstructive sleep apnea (OSA). Treatment and an early diagnosis will aid in averting additional severe consequences. Our study aimed to assess prevalence of OSA among 6–9-year-old children in Visnagar.Methods The approval of the school authorities and ethics committee was received. A sample size of 455 was determined. Clinical inspection was used to determine the prevalence of OSA in children aged 6 to 9 years using the “FAIREST-6” and BEARS criteria. The cases were classified as mild, moderate, or severe. By employing the chi-square test and Fisher’s exact test, inferential statistics were calculated.Results Prevalence of 8.13% (n = 37) among sample of 453 participants was observed. Gender predilection was found to be in 43.2% (n = 16) among females and 56.8% (n = 21) among males. Those diagnosed with OSA were categorized as mild 17 (45.94%), moderate 12 (32.43%), and severe 8 (21.62%).Conclusions OSA prevalence was determined 8.13% in the Visnagar population of children and adolescents aged 6 to 9 years using the FAIREST and BEARS questionnaire.

Evaluation of the effects of sleep-related respiratory disorders (primary snoring-severe obstructive sleep apnea syndrome) and epilepsy clinic and their treatments on each other

Evaluation of the effects of sleep-related respiratory disorders (primary snoring-severe obstructive sleep apnea syndrome) and epilepsy clinic and their treatments on each other

Obezite Cerrahisi Hastalarında Obstrüktif Uyku Apne Sıklığı

Objective: Obesity is one of the most important risk factors and also consequences of obstructive sleep apnea (OSA). Weight loss and positive airway pressure therapy are the main approaches in the treatment of OSA. In this study, it was aimed to evaluate the prevalence of OSA in obesity patients scheduled for bariatric surgery.
 Methods: In the University Hospital Obesity Center, 141 consecutive patients who were candidates for bariatric surgery in 2015 were clinically evaluated preoperatively for sleep-related respiratory disorders. Preoperative polysomnographic examination was recommended to all cases.
 Results: Of 141 bariatric surgery candidates with a mean age of 37±10 years, and 103 (73%) were female. The mean body mass index of the cases was 46.9±6.4 kg/m2. Among the major symptoms of OSA, snoring was present in 119 (84.4%), daytime sleepiness in 63 (44.7%) and witnessed apnea in 49 (34.8%) patients. OSA was detected because AHI>5/hour was found in 75.7% (84/111) of the patients who accepted the polysomnographic evaluation. 24.3% (27/111) of the cases who underwent polysomnography had non OSA, 29.7% (33/111) mild OSA, 17.1% (19/111) moderate OSA, and 28.8% (32 /111), severe OSA was detected. AHI was positively correlated with age (p=0.003), neck and waist diameter (p

Characteristics of Patients With Severe Obstructive Sleep Apnea Who Maintain Slow-Wave Sleep

Background and Objectives Obstructive sleep apnea (OSA) is a complex sleep-related respiratory disorder with various pathophysiology and clinical manifestations. Diagnosis and treatment considering only the number of respiratory events, such as sleep duration, may have limitations. The purpose of this study is to analyze the clinical and polysomnography (PSG) characteristics of patients with severe OSA in whom slow wave sleep (SWS) was well-maintained.Subjects and Method This study retrospectively analyzed 203 patients who underwent PSG due to suspected OSA. Severe OSA patients were classified into a SWS maintained group (SWS≥15%) and a SWS reduced group (SWS<15%), and their clinical records, PSG results, and heart rate variability (HRV) were compared.Results The SWS maintained group (n=27), compared to the SWS reduced group (n=58), had a significantly longer total sleep time (<i>p</i>=0.027) and lower wake after sleep onset (<i>p</i>=0.001), rapid eye movoement (REM) latency (<i>p</i>=0.007), non-supine apnea-hypopnea index (AHI, <i>p</i>=0.006), non-REM AHI (<i>p</i><0.001), arousal index (<i>p</i><0.001) and Epworth Sleepiness Scale (<i>p</i>=0.048). In addition, the SWS maintained group had a significantly low value in very low frequency in HRV than that of the SWS reduced group (<i>p</i>=0.046).Conclusion The SWS maintained group in the severe OSA showed better PSG and HRV results and reduced daytime sleepiness than those of the SWS reduced group.

A novel deep domain adaptation method for automated detection of sleep apnea/hypopnea events

Objective. Sleep apnea–hypopnea syndrome (SAHS) is a common sleep-related respiratory disorder that is generally assessed for severity using polysomnography (PSG); however, the diversity of sampling devices and patients makes this not only costly but may also degrade the performance of the algorithms. Approach. This paper proposes a novel deep domain adaptation module which uses a long short-term memory–convolutional neural network embedded with the channel attention mechanism to achieve autonomous extraction of high-quality features. Meanwhile, a domain adaptation module was built to achieve domain-invariant feature extraction for reducing the differences in data distribution caused by different devices and other factors. In addition, during the training process, the algorithm used the last second label as the label of the PSG segment, so that second-by-second evaluation of respiratory events could be achieved. Main results. The algorithm applied the two datasets provided by PhysioNet as the source and target domains. The accuracy, sensitivity and specificity of the algorithm on the source domain were 86.46%, 86.11% and 93.17%, respectively, and on the target domain were 83.63%, 82.52%, 91.62%, respectively. The proposed algorithm showed strong generalization ability and the classification results were comparable to the current advanced methods. Besides, the apnea–hypopnea index values estimated by the proposed algorithm showed a high correlation with the manual scoring values on both domains. Significance. The proposed algorithm can effectively perform SAHS detection and evaluation with certain generalization.

Evaluation of the effect of CPAP treatment on voice in obstructive sleep apnea with objective and subjective voice analyzes.

Obstructive sleep apnea (OSA) is a multifactorial disease that is the most common among sleep-related respiratory disorders. In our study, we aimed to compare the objective and subjective voice analysis results of the patients with CPAP treatment indication after polysomnography performed in the sleep disorders center before the start of CPAP treatment, the 1st month, and the 3rd month after the treatment. Patients were asked to say the vowels /ɑ:/ for 5 seconds, respectively for voice recording. MPT and S/Z ratios were also recorded. Using the Praat voice analysis program Jitter%, Shimmer%, HNR, f0 values were obtained. VHI-10 questionnaire was applied. The voice analysis results of the patients before and after the treatment were compared. 26 patients were included in the study. Since 8 of these patients did not come to the 3rd month evaluation, the study was completed with the data of 18 patients. In our study, at the end of the 3rd month, no difference was found in terms of the fundamental frequency, Shimmer%, HNR, and S/Z values. There was a statistically significant decrease in Jitter% at the 1st month after treatment and 3rd month after treatment compared to pre-treatment (p=0.05, p=0.018). There was a statistically significant decrease in MPT at the 1st month after treatment and 3rd month after treatment compared to pre-treatment (p<0.001, p<0.001). There was a statistically significant decrease in VHI-10 at the 1st month and 3rd month after treatment compared to pre-treatment. Furthermore, there was statistically significant difference between 3rd month after treatment compared to 1st month after treatment. (p=0.043, p=0.030, p=0.029). Our study showed that CPAP treatment had both objective and subjective positive effects on voice recovery in the OSA patient group.

Variations in Polysomnographic Indices of Obstructive Sleep Apnea following Lingual Tonsil Hypertrophy Excision: Is the Difference Significant?

Background and Objectives: Obstructive sleep apnea (OSA) is a sleep-related respiratory disorder that affects between 5% and 20% of the population. In obstructive sleep apnea, lingual tonsillar hypertrophy (LTH) has been suggested as a contributing factor to airway blockage. Objectives: The aim of this work is to demonstrate the polysomnographic indices and their values in OSA patients with LTH before and after the surgical intervention. Materials and Methods: The study was conducted on eighteen patients endoscopically diagnosed as having LTH, with the main complaints being snoring, sleep apnea, and/or sleep disturbance. Clinical examination, grading of LTH, body mass index (BMI), endoscopic assessment using Muller’s maneuver, and sleep endoscopy were recorded for all patients. The Epworth Sleepiness Scale (ESS) and overnight sleep polysomnography (PSG) were conducted before and after the surgical removal of LTH. All data were submitted for statistical analysis. Results: The mean ± SD of the AHI decreased from 33.89 ± 26.8 to 20.9 ± 19.14 postoperatively, and this decrease was of insignificant statistical value. The average SpO2 (%) mean ± SD was 91.14 ± 5.96, while the mean ± SD of the desaturation index was 34.64 ± 34.2. Following surgery, these indices changed to 96.5 ± 1.47 and 9.36 ± 7.58, respectively. The mean ± SD of the ESS was changed after the surgery, from 17.27 ± 6.48 to 7.16 ± 3.56. The mean ± SD of sleep efficacy was 71.2 ± 16.8 and the snoring index mean ± SD was 277.6 ± 192.37, and both improved postoperatively, to become 88.17 ± 9.1 and 62.167 ± 40.01, respectively. Conclusions: The AHI after lingual tonsillectomy showed no statistically significant change. The changes in the average SpO2 (%), desaturation index, sleep efficiency, snoring index, and Epworth Sleepiness Scale following the surgery were statistically significant.

Telemedicine in sleep-related breathing disorders and treatment with positive airway pressure devices. Learnings from SARS-CoV-2 pandemic times.

The healthcare system currently faces new challenges, which are to be addressed by finding efficient alternatives. Such factors as the growth of world population, the increase in longevity, and the fact that some diseases which used to be deadly diseases have turned into chronic pathologies, cause the number of people in need for continuous medical care to rise. This results in a healthcare system crisis, which searches for solutions as telemedicine to address the needs of patients and control excessive medical spending. Telemedicine means remote medical assistance delivered by means of technological resources, which streamline the provision of medical care, thus increasing patient’s access to healthcare and saving time and costs. As regards respiratory diseases, telemedicine is a tool that may provide for proper prevention, diagnosis, therapeutic education, monitoring of observance, and therapeutic efficacy, as well as for the early detection of exacerbations. Patients suffering from sleep-related respiratory disorders in need for positive airway pressure devices may be benefited by telemedicine to enhance positive pressure adherence and follow-up to treat their pathologies, thus providing for the delivery of remote care and follow-up, reducing costs, and increasing the chances of receiving attention from specialists in patients who live a long distance from such medical facilities. However, it is a challenging task to find a balance in the doctor-patient virtual relationship.

Chapter 7 - Breathing during sleep

The depth, rate, and regularity of breathing change following transition from wakefulness to sleep. Interactions between sleep and breathing involve direct effects of the central mechanisms that generate sleep states exerted at multiple respiratory regulatory sites, such as the central respiratory pattern generator, respiratory premotor pathways, and motoneurons that innervate the respiratory pump and upper airway muscles, as well as effects secondary to sleep-related changes in metabolism. This chapter discusses respiratory effects of sleep as they occur under physiologic conditions. Breathing and central respiratory neuronal activities during nonrapid eye movement (NREM) sleep and REM sleep are characterized in relation to activity of central wake-active and sleep-active neurons. Consideration is given to the obstructive sleep apnea syndrome because in this common disorder, state-dependent control of upper airway patency by upper airway muscles attains high significance and recurrent arousals from sleep are triggered by hypercapnic and hypoxic episodes. Selected clinical trials are discussed in which pharmacological interventions targeted transmission in noradrenergic, serotonergic, cholinergic, and other state-dependent pathways identified as mediators of ventilatory changes during sleep. Central pathways for arousals elicited by chemical stimulation of breathing are given special attention for their important role in sleep loss and fragmentation in sleep-related respiratory disorders.

Longitudinal association between daytime sleepiness and cognitive decline in dementia: A study protocol

IntroductionDementia is a major cause of disability worldwide. About 25%-40% of patients with mild to moderate dementia are affected by sleep-awake cycle disturbances, including increased daytime sleepiness and insomnia. However, little is known about the specific impact of excessive daytime sleepiness on the cognitive decline of dementia patients.ObjectivesTo evaluate the impact of daytime sleepiness on the cognitive decline of dementia patients. Additionally, longitudinal associations with functional impairment and neuropsychiatric symptoms will be explored.MethodsA longitudinal study will be conducted in a psychogeriatric consultation. Patients will be consecutively invited according to predefined eligibility criteria. Those aged ≥65 years, with dementia diagnosis or Mini-Mental State Examination (MMSE) &lt;24, and with a knowledgeable caregiver, will be included. The exclusion criteria are: a caregiver &lt;18 years, terminally ill, incapable to communicate or with a known diagnosis of insomnia, sleep related respiratory disorders, central hyperinsomnia, restless legs syndrome or sleep paralysis. Participants will undergo an assessment with a comprehensive protocol including: Montreal Cognitive Assessment (MoCA), Barthel and Lawton Index, Epworth Sleepiness Scale (ESS), Neuropsychiatric Inventory (NPI) and Global Deterioration Scale (GDS). Participants will be re-assessed 6 months after the initial evaluation. The Health Ethics Committee of Hospital Universitário de São João granted the study authorization (nº 260/2020).ResultsFindings will be disseminated via publication in peer-reviewed journals and presentations at national and international scientific conferences.ConclusionsThis study will address key questions on the relation of daytime sleepiness and dementia outcomes, in order to undertake corrective and preventive non-pharmacological and pharmacological approaches.

Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home

Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and scored by a sleep expert. We investigate in this work the use of supervised machine learning to automate the analysis of polygraphy data from the A3 study containing more than 7,400 hours of sleep monitoring data from 579 patients. We conduct a systematic comparative study of classification performance and resource use with different combinations of 27 classifiers and four sleep signals. The classifiers achieve up to 0.8941 accuracy (kappa: 0.7877) when using all four signal types simultaneously and up to 0.8543 accuracy (kappa: 0.7080) with only one signal, i.e., oxygen saturation. Methods based on deep learning outperform other methods by a large margin. All deep learning methods achieve nearly the same maximum classification performance even when they have very different architectures and sizes. When jointly accounting for classification performance, resource consumption and the ability to achieve with less training data high classification performance, we find that convolutional neural networks substantially outperform the other classifiers.

Sleep disorders in patients with myastenia gravis

This review analyzes publications that present data on sleep disorders in patients with myasthenia gravis (MG). The analysis is based on domestic and foreign publications that are freely available over the past 30 years. Sleep disorders, the most significant of which are sleep-related respiratory disorders, are one of the factors that cause quality of life decreasing and mortality in patients with neuromuscular diseases. The issues of prevalence of breathing disorders during sleep, relationship of these disorders with clinical and immunological characteristics of disease, demographic indicators are studied. The influence of sleep breathing disorders on quality of life and affective sphere of patients is discussed. Most studies prove that sleep-related respiratory disorders occur in patients with MG significantly more often than in general population. Some studies show a high prevalence of poor sleep quality, excessive daytime sleepiness in patients with MG, while others do not report such associations. However, studies that failed to establish an association with MG and sleep disturbances were of small sample sizes. Thus, given the inconclusive evidence and limited literature, further study of sleep disorders in patients with MG is needed. The topic is relevant and requires further development.

Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

Respiratory and sleep-related complications of multiple system atrophy.

The purpose of this article is to provide a contemporary review of sleep issues affecting patients with multiple system atrophy (MSA). Prodromal symptoms of MSA may occur years prior to diagnosis, including autonomic dysfunction such as orthostatic hypotension, urogenital dysfunction, rapid eye movement (REM) sleep behavior disorder (RBD), and stridor. Patients may also develop sleep-related respiratory disorders such as obstructive sleep apnea (OSA), central sleep apnea (CSA), and stridor. The development of stridor is associated with a shortened lifespan and sudden death, which may be further accelerated by autonomic instability. MSA appears to follow a 'prion-like' disease progression. MSA is a rapidly progressive neurodegenerative disease characterized by a combination of autonomic failure and motor symptoms. MSA is often misdiagnosed as the initial presentation mimics other neurodegenerative disorders. There are diagnostic criteria to identify possible, probable, and definite MSA. Prodromal symptoms may occur years prior to diagnosis, including autonomic dysfunction such as orthostatic hypotension, urogenital dysfunction, REM RBD, and stridor. In previous years, treatment consisted of tracheostomy but did not address the component of CSA, which commonly coexisted or developed later because of destruction of medullary chemoreceptors. Positive airway pressure may be as effective as tracheostomy alone in ameliorating obstruction at the vocal cord level.

Aberrant Awake Spontaneous Brain Activity in Obstructive Sleep Apnea: A Review Focused on Resting-State EEG and Resting-State fMRI.

As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.

Evaluation of sleep-related respiratory disorders in patients with multiple sclerosis.

Patients with multiple sclerosis (MS) often report fatigue, poor sleep and complaint of sleep disorders. Neurofilament light chain (NF-L) has been identified as a potential biomarker for disease progression in MS patients. In this study, we aimed to evaluate sleep characteristics in MS patients and its relationship with the level of serum NF-L. In the present study carried out as a prospective and cross-sectional study, 32 relapsing-remitting MS (RRMS) patients and 32 control subjects were included. Epworth Sleepiness Scale and Fatigue Severity Scale tests were applied to the groups and the full night polysomnography was performed. Serum samples were obtained for NF-L analysis. Apnea-hypopnea index (AHI), AHI in rapid eye movement sleep (AHI REM), percentage of NonREM stage 1 (N1) and NonREM stage 3 (N3) values were significantly different in RRMS patients (p < 0.05). There was correlation between AHI and Expanded Disability Status Scale indicating a negative directed moderate relationship (r = - 0.343 p = 0.055). Serum NF-L correlations with sleep efficiency and percentage of NonREM stage 2 (N2) were showed mild significant correlation (r = - 0.342 as - 0.535, p < 0.05). We found that sleep disorders are prevalent in RRMS patients and it has a negative effect on the clinical outcome of disease. In clinical practice, the association of these two diseases should be taken into consideration because sleep disturbances increase the disability of MS disease especially presenting with fatigue.

Catathrenia; Sleep-related Respiratory Disorder Which May Mimic Central Sleep Apnea in Polysomnography: A Case Report

Catathrenia; Sleep-related Respiratory Disorder Which May Mimic Central Sleep Apnea in Polysomnography: A Case Report

Emotional Intelligence in Children with Severe Sleep-Related Breathing Disorders.

Background Obstructive sleep apnea syndrome (OSAS) affects up to 4% of a pediatric population, with many comorbidities in the medium-long term. Functional alterations in the prefrontal cortex (PFC) may explain why OSAS impacts aspects such as executive functions, memory, motor control, attention, visual-spatial skills, learning, and mood regulation. Emotional intelligence (EI) is a complex neuropsychological function that could be impaired in many clinical conditions. Purpose The aim of the study is to evaluate the difference in emotional intelligence skills among children with OSAS and healthy subjects (nOSAS). Methods 129 children (72 males; mean age 7.64 ± 1.98 years) affected by OSAS were compared to 264 non-OSAS (nOSAS) children (138 males; mean age 7.98 ± 2.13) similar for gender, age, and socioeconomic status. In order to assess the emotional quotient, the Bar-On Emotional Quotient Inventory: Youth Version (EQ-i:YV) was used. Results The comparison for means and standard deviation between OSAS children and nOSAS children for EQ-i:YV scores showed significant differences for Interpersonal, Adaptability, and Stress Management scales and EQ Total score. Conclusions Our findings highlighted the role of intermittent hypoxia in the genesis of the effects of sleep-related respiratory disorders, which involves also aspects different from physical impairments.

Variables and biomarkers in patients with sleep-related respiratory disorders

Sleep-related respiratory disorders are a singular risk factor for cardio-vascular diseases. Biomarkers, related to the cardiovascular diseases and heart failure as a their result with the morbidity increasing with the increase of patients ’ longevity, are used as a diagnostic and prognostic factor. The study of patients with sleep-related respiratory disorders may help the construction of a panel of biochemical and instrumental tools, defining the cardiovascular risk of these patients.

Neuroanatomical Basis of State-Dependent Activity of Upper Airway Muscles.

Obstructive Sleep Apnea (OSA) is a common sleep-related respiratory disorder that is associated with cognitive, cardiovascular, and metabolic morbidities. The major cause of OSA is the sleep-related reduction of upper airway muscle tone that leads to airway obstructions in individuals with anatomically narrow upper airway. This reduction is mainly due to the suppressant effect of sleep on hypoglossal motoneurons that innervate upper airway muscles. The hypoglossal motoneurons have state-dependent activity, which is decreased during the transition from wakefulness to non-rapid eye movement sleep and is further suppressed during rapid eye movement sleep. Multiple neurotransmitters and their receptors have been implicated in the control of hypoglossal motoneuron activity across the sleep-wake states. However, to date, the results of the rigorous testing show that withdrawal of noradrenergic excitation and cholinergic inhibition essentially contribute to the depression of hypoglossal motoneuron activity during sleep. The present review will focus on origins of noradrenergic and cholinergic innervation of hypoglossal motoneurons and the functional role of these neurons in the state-dependent activity of hypoglossal motoneurons.