Diagnosis of obstructive sleep apnoea in primary care.

BACKGROUND: Obstructive sleep apnoea (OSA) is a highly prevalent condition associated with significant adverse health consequences affecting multiple organ systems. As the first point of contact for most patients with OSA, general practitioners (GPs) have an important role in the diagnosis of this common sleep disorder. OBJECTIVE: The aim of this paper is to improve awareness of common risk factors for and clinical presentation of OSA in primary care to improve patient health outcomes. We seek to understand how screening tools, such as the OSA50 questionnaire and the Epworth Sleepiness Scale, can help GPs identify patients who are at high risk for OSA with significant daytime sleepiness. DISCUSSION: Patients at high risk of symptomatic moderate-severe OSA should proceed to further investigation with sleep study testing. Referral to a sleep physician should be considered for patients with complex presentations or other suspected sleep disorders, commercial drivers, and those who fail to comply with or respond to initial OSA treatments.

Treating sleep-disordered breathing of idiopathic pulmonary fibrosis patients with CPAP and nocturnal oxygen treatment. A pilot study : Sleep-disordered breathing treatment in IPF.

INTRODUCTION: Sleep-disordered breathing (SDB) is a major comorbidity in idiopathic pulmonary fibrosis (IPF) and is associated with a poor outcome. There is a lack of knowledge regarding the impact of SDB treatment on IPF. We assessed at one year: (1) the effect of CPAP and/or nocturnal oxygen therapy on IPF regarding lung function, blood mediators, and quality of life; (2) adherence to SDB treatment and SDB changes. METHODOLOGY: This is a prospective study of consecutive newly diagnosed IPF patients initiating anti-fibrotic treatment. Lung function, polysomnography, blood tests and quality of life questionnaires were performed at inclusion and after one year. Patients were classified as obstructive sleep apnoea (OSA), central sleep apnoea (CSA), and sleep-sustained hypoxemia (SSH). SDB therapy (CPAP and/or nocturnal oxygen therapy) was initiated if needed. RESULTS: Fifty patients were enrolled (36% had OSA, 22% CSA, and 12% SSH). CPAP was started in 54% of patients and nocturnal oxygen therapy in 16%. At one-year, polysomnography found improved parameters, though 17% of patients had to add nocturnal oxygen therapy or CPAP, while 33% presented SDB onset at this second polysomnography. CPAP compliance at one year was 6.74 h/night (SD 0.74). After one year, matrix metalloproteinase-1 decreased in OSA and CSA (p = 0.029; p = 0.027), C-reactive protein in OSA (p = 0.045), and surfactant protein D in CSA group (p = 0.074). There was no significant change in lung function. CONCLUSIONS: Treatment of SBD with CPAP and NOT can be well tolerated with a high compliance. IPF patients may exhibit SDB progression and require periodic re-assessment. Further studies to evaluate the impact of SDB treatment on lung function and serological mediators are needed.

Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.

[Study on the application value of portable pulse oximeter in adult patients with obstructive sleep apnea].

Objective: To evaluate the application value of portable pulse oximeter in adult obstructive sleep apnea (OSA). Methods: This study prospectively enrolled adult patients who underwent polysomnography (PSG) due to snoring at the Respiratory and Sleep Medicine Department of Peking University People's Hospital from July 2022 to July 2023. During PSG monitoring, CS-WOxi was continuously used to monitor blood oxygen levels. The consistency between 3% oxygen desaturation index (ODI3) measured by portable pulse oximeter and ODI3 of polysomnography was evaluated using difference test, Pearson's correlation coefficient, and Bland-altman method. Receiver operating characteristic curve was used to determine the optimal threshold for diagnosing OSA. Results: A total of 184 subjects were included, including 121 males (65.8%) and 63 females (34.2%). The mean age was 46.0 (34.3, 59.0) years, body mass index was 26.0 (23.3, 29.6) kg/m², and the apnea-hypopnea index was 18.2 (5.8, 40.8) events/h. There was a significant difference between CS-ODI3 and PSG-ODI3 [17.1(6.2, 42.7) vs. 14.0(2.9, 32.6), P<0.001], and the Pearson correlation coefficient was 0.93 (P<0.001). There was a good correlation between CS-ODI3 and PSG-AHI (r=0.92, P<0.001). Bland-Altman consistency test showed that the average difference between the two was 0.7 events/h, and the 95% consistency limit was (-17.9, 19.3 events/h). When the CS-ODI3≥5 events/h was used to identify OSA, the sensitivity was 94.4%, the specificity was 80.0%, and the accuracy was 91.3%. When PSG-AHI≥5 events/h was used as the diagnostic criteria, the area under the receiver operating characteristic curve was 0.933. Conclusion: Portable pulse oximeter can monitor pulse oxygen saturation accurately and has good sensitivity and specificity for OSA high-risk patients, and is a reliable tool for OSA screening.

[Advances in the detection of arousal in obstructive sleep apnea].

Obstructive sleep apnea (OSA) is primarily characterized by intermittent nocturnal hypoxia and sleep fragmentation. Arousals interrupt sleep continuity and lead to sleep fragmentation, which can lead to cognitive dysfunction, excessive daytime sleepiness, and adverse cardiovascular outcome events, making arousals important for diagnosing OSA and reducing the risk of complications, including heart disease and cognitive impairment. Traditional arousal interpretation requires sleep specialists to manually score PSG recordings throughout the night, which is time consuming and has low inter-specialist agreement, so the search for simple, efficient, and reliable arousal detection methods can be a powerful tool to clinicians. In this paper, we systematically reviewed different methods for recognizing arousal in OSA patients, including autonomic markers (pulse conduction time, pulse wave amplitude, peripheral arterial tone, heart rate, etc.) and machine learning-based automated arousal detection systems, and found that autonomic markers may be more beneficial in certain subgroups, and that deep artificial networks will remain the main research method for automated arousal detection in the future.

Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

Comparative evaluation of the sleep quality metrics between a cardboard bed and a camp cot: a randomized controlled crossover study.

Background: Health-beneficial emergency bedding has become increasingly important for dealing with natural disasters such as the anticipated Nankai Trough earthquake in Japan. When the Great East Japan Earthquake occurred, cardboard beds were provided to evacuees. However, there were concerns about lower back pain and sleep disturbances, as cardboard beds offer insufficient pressure distribution. This study aimed to compare the effects of cardboard beds with those of foldable camp cots on sleep quality. Methods: A randomized controlled crossover study involving 20 healthy participants aged 18-45 years was conducted between June 2022 and January 2023. Participants were asked to sleep for one night on a camp cot and for another night on a cardboard bed, with a minimum three-day washout period between the two nights. Body pressure distribution and sleep metrics obtained from polysomnography (PSG) and questionnaires were compared between the two-bed types (P < 0.05). Results: The camp cot exhibited better body pressure distribution than a cardboard bed, leading to improved sleep satisfaction, bedding comfort, and reduced morning sleepiness. Nevertheless, polysomnography revealed no notable differences in sleep metrics or sleep architecture between the two types of beds. Conclusions: Our findings indicate that cardboard beds have lower pressure dispersion capabilities than camp cots, leading to an increased number of position changes during sleep. Additionally, subjective sleep quality, such as alertness on waking, sleep comfort, and sleep satisfaction, was lower for cardboard beds, suggesting that camp cots might offer a more comfortable bedding option for evacuees. However, there were no discernible differences between the two-bed types in terms of objective sleep metrics derived from PSG. The potential for sleep disturbances caused by lower back pain from a hard mattress has been noted, and it is possible that a single night's experience in healthy individuals might not be enough for sleep issues to manifest.

Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

BACKGROUND: Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes. RESEARCH QUESTION: To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification. STUDY DESIGN AND METHODS: A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality using Cox regression analysis, adjusted for potential confounders, and visualized with Kaplan-Meier estimates. RESULTS: Among 9992 patients with COPD and a clinically indicated baseline polysomnogram, we identified five distinct clusters based on age, comorbidity burden and sleep parameters. Overall mortality increased from 9.4 % to 42 % and short-term mortality (<5.3 years) ranged from 3.4 % to 24.3 % in Cluster 1 to 5. In Cluster 1 younger age, in 5 high comorbidity burden and in the other three clusters, total sleep time and sleep efficiency had significant associations with mortality. INTERPRETATION: We identified five distinct clinical clusters and highlighted the significant association between total sleep time and sleep efficiency on mortality. The identified clusters highlight the importance of objective sleep parameters in determining mortality risk and phenotypic characterization in this population.

Manual scoring of periodic limb movements in children: is it still necessary?

OBJECTIVE: Although manual scoring has been classically considered the gold standard to identify periodic leg movements (PLM), it is a very time consuming and expensive process, also subject to variability in interpretation. In the last decades, different authors have observed reasonably good agreement between automated PSG scoring algorithms and manual scoring in adults, according to established criteria. We aim to compare the automatic software analysis of our polysomnogram with the manual staging in children with sleep-disordered breathing. METHODS: We performed a semiautomatic method, in which an experienced technician watched the video recording and removed from the automatic analysis those movements that did not correspond to true candidate leg movement (LM). RESULTS: A total of 131 PSGs were studied; applying the established criteria, 65 children were diagnosed of obstructive sleep apnea, and 66 presented snoring but with no sleep apnea. The mean age was 6.7 years (±1.7) and twenty-five children (19.08 %) had a PLMI >5/h. Statistical differences were found not only for PLMI (manual: 2.20 (0.7, 4.1) vs automatic (6.4 (3.85,9.5); p < 0.001), but for almost of all indexes assessed between the automatic and the manual scoring analysis. The level of concordance was only moderate for PLM index (0.63 [0.51-0.72]); showing that, unlike the articles published in the adult population, automatic analysis is not accurate in children and, manually or semi-automatically analysis as ours need to be done. CONCLUSION: It seems that PLM detection algorithm might work accurately but, the real need would be a true LM detection algorithm.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network.

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification-further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

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

OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Artificial intelligence augmented home sleep apnea testing device study (AISAP study).

STUDY OBJECTIVE: This study aimed to prospectively validate the performance of an artificially augmented home sleep apnea testing device (WVU-device) and its patented technology. METHODOLOGY: The WVU-device, utilizing patent pending (US 20210001122A) technology and an algorithm derived from cardio-pulmonary physiological parameters, comorbidities, and anthropological information was prospectively compared with a commercially available and Center for Medicare and Medicaid Services (CMS) approved home sleep apnea testing (HSAT) device. The WVU-device and the HSAT device were applied on separate hands of the patient during a single night study. The oxygen desaturation index (ODI) obtained from the WVU-device was compared to the respiratory event index (REI) derived from the HSAT device. RESULTS: A total of 78 consecutive patients were included in the prospective study. Of the 78 patients, 38 (48%) were women and 9 (12%) had a Fitzpatrick score of 3 or higher. The ODI obtained from the WVU-device corelated well with the HSAT device, and no significant bias was observed in the Bland-Altman curve. The accuracy for ODI > = 5 and REI > = 5 was 87%, for ODI> = 15 and REI > = 15 was 89% and for ODI> = 30 and REI of > = 30 was 95%. The sensitivity and specificity for these ODI /REI cut-offs were 0.92 and 0.78, 0.91 and 0.86, and 0.94 and 0.95, respectively. CONCLUSION: The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder.

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both cognitive symptoms and SD are related to MDD progression. However, there are only a few studies investigating the connection between cognitive symptoms and SD in patients with MDD, and only preliminary evidence suggests a significant association between cognitive symptoms and SD in patients with mood disorders. This study investigates the relationship between cognitive symptoms and sleep quality in patients with major depressive disorder. Patients (n = 20) with MDD were enrolled; their mean Hamilton Depression Scale-17 score was 21.95 (±2.76). Gold standard polysomnography (PSG) was used to assess sleep quality, and the validated THINC-integrated tool (the cognitive screening tool) was used to evaluate cognitive function in MDD patients. Overall, the results showed significant correlations between the cognitive screening tool's total score and sleep latency, wake-after-sleep onset, and sleep efficiency. These findings indicate that cognitive symptoms are associated with poor sleep quality among patients with MDD.

In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing.

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea-hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA.

Predictors of moderate to severe obstructive sleep apnea in patients with lung cancer.

BACKGROUND AND OBJECTIVES: OSA is a known medical condition that is associated with several comorbidities and affect patients' quality of life. The association between OSA and lung cancer remains debated. Some studies reported increased prevalence of OSA in patients with lung cancer. We aimed to assess predictors of moderate-to-severe OSA in patients with lung cancer. METHODS: We enrolled 153 adult patients who were newly diagnosed with lung cancer. Cardiorespiratory monitoring was performed using home sleep apnea device. We carried out Univariate and multivariate logistic regression analysis on multiple parameters including age, gender, smoking status, neck circumference, waist circumference, BMI, stage and histopathology of lung cancer, presence of superior vena cava obstruction, and performance status to find out the factors that are independently associated with a diagnosis of moderate-to-severe OSA. RESULTS: Our results suggest that poor performance status is the most significant predictor of moderate to severe OSA in patients with lung cancer after controlling for important confounders. CONCLUSION: Performance status is a predictor of moderate to severe OSA in patients with lung cancer in our population of middle eastern ethnicity.

Age related disparities in sleep apnea diagnosis using a wearable device: Implications of 4% vs. 3% hypopnea scoring criteria.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) diagnosis relies on the Apnea-Hypopnea Index (AHI), with discrepancies arising from the 3% and 4% desaturation criteria. This study investigates age-related variations in OSA severity classification, utilizing data from 1201 adult patients undergoing Home Sleep Apnea Testing (HSAT) with SleepImage Ring@. METHODS: The study employs Bland-Altman analysis to compare AHI values obtained with the 3% and 4% desaturation criteria. Age-stratified analysis explores discrepancies across different age groups. RESULTS: The analysis reveals a systematic bias favoring the 3% criterion, impacting the quantification of apnea events. Age-specific patterns demonstrate diminishing agreement between criteria with increasing age. CONCLUSION: This comprehensive study underscores the importance of standardized criteria in OSA diagnosis. The findings emphasize age-specific considerations and ethical concerns, providing crucial insights for optimizing patient care and advancing sleep medicine practices.

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

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

ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.

Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall performance, they often exhibit low per-class performance for N1-stage, necessitating an adjustment of loss function. However, the efficacy of such adjustment is constrained by the training process. In this study, a pioneering training approach called separating training is introduced, alongside a novel model, to enhance performance. The developed model comprises 15 CNN models with varying loss function weights for feature extraction and 1 BiLSTM for sequence classification. Due to its architecture, this model cannot be trained using an end-to-end approach, necessitating separate training for each component using the Sleep-EDF dataset. Achieving an overall accuracy of 87.02%, MF1 of 82.09%, Kappa of 0.8221, and per-class F1-socres (W 90.34%, N1 54.23%, N2 89.53%, N3 88.96%, and REM 87.40%), our model demonstrates promising performance. Comparison with sleep technicians reveals a Kappa of 0.7015, indicating alignment with reference sleep stags. Additionally, cross-dataset validation and adaptation through training with the SHHS dataset yield an overall accuracy of 84.40%, MF1 of 74.96% and Kappa of 0.7785 when tested with the Sleep-EDF-13 dataset. These findings underscore the generalization potential in model architecture design facilitated by our novel training approach.

Effect of obesity on sleep apnea pathogenesis differs in women versus men: multiple mediation analyses in the retrospective SNOOzzzE cohort.

There are multiple mechanisms underlying obstructive sleep apnea (OSA) development. However, how classic OSA risk factors such as body mass index (BMI) and sex portend to OSA development has not been fully described. Thus we sought to evaluate how obesity leads to OSA and assess how these mechanisms differ between men and women. The San Diego Multi-Outcome OSA Endophenotype (SNOOzzzE) cohort includes 3,319 consecutive adults who underwent a clinical in-laboratory polysomnography at the University of California, San Diego, sleep clinic between January 2017 and December 2019. Using routine polysomnography signals, we determined OSA endotypes. We then performed mediation analyses stratified by sex to determine how BMI influenced the apnea-hypopnea index (AHI) using OSA pathophysiological traits as mediators, adjusting for age, race, and ethnicity. We included 2,146 patients of whom 919 (43%) were women and 1,227 (57%) were obese [body mass index (BMI) > 30 kg/m2]. BMI was significantly associated with AHI in both women and men. In men, the adjusted effect of BMI on AHI was partially mediated by a reduction in upper airway stiffness (ßstandardized = 0.124), a reduction in circulatory delay (ßstandardized = 0.063), and an increase in arousal threshold (ßstandardized = 0.029; Pboot-strapped,all < 0.05). In women, the adjusted effect of BMI on AHI was partially mediated by a reduction in upper airway stiffness (ßstandardized = 0.05) and circulatory delay (ßstandardized = 0.037; Pboot-strapped,all < 0.05). BMI-related OSA pathogenesis differs by sex. An increase in upper airway collapsibility is consistent with prior studies. A reduction in circulatory delay may lead to shorter and thus more events per hour (higher AHI), while the relationship between arousal threshold and OSA is likely complex.NEW & NOTEWORTHY Our data provide important insights into obesity-related obstructive sleep apnea (OSA) pathogenesis, thereby validating, and extending, prior research findings. This is the largest sample size study to examine the relationships between obesity and gender on OSA pathogenesis. The influence of obesity on sleep apnea severity is mediated by different mechanistic traits (endotypes).