Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China\'s sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.
睡眠呼吸障碍疾病（sleep disordered breathing, SDB）是常见的睡眠疾病，其患病率逐年上升。目前SDB诊断的金标准为多导睡眠监测（polysomnography, PSG），但现有的PSG监测技术存在人工判读时间长、缺乏数据质控、缺少气体代谢及血流动力学监测等问题。因此，研制具有数据质控、气体代谢及血流动力学监测功能的新型智能PSG是我国睡眠临床应用中迫切需要解决的问题。硬件方面，新系统检测鼻气流、血氧、心电、脑电、肌电、眼电等传统参数，且新增评估气体代谢功能的呼气末CO 2、O 2浓度及评估血流动力学功能的心阻抗检测模块。软件方面，基于深度学习方法研究智能数据质控、智能疾病诊断技术。目标是输出详细的睡眠质量评价报告，以有效地辅助医生充分评估SDB患者的睡眠质量。.

The choice of interface used to deliver noninvasive ventilation (NIV) is a critical element in successfully and safely establishing home NIV in people with sleep hypoventilation syndromes. Both patient-related and equipment-related factors need to be considered when selecting an interface. Recognizing specific issues that can occur with a particular style of mask is important when troubleshooting NIV problems and attempting to minimize side effects. Access to a range of mask styles and designs to use on a rotational basis is especially important for patients using NIV on a more continuous basis, those at risk of developing pressure areas, and children.

We performed a matched-pair analysis of the content of GDF11 and GDF15 proteins in the plasma of patients (56 middle-aged men) with obstructive sleep apnea syndrome (OSAS) and healthy volunteers (27 men with no complaints of sleep disorders). The groups were comparable in terms of age and presence of chronic diseases. No statistically significant differences in GDF11 content in the studied groups were revealed, while the content of GDF15 in the OSAS group was 1.3 times higher. These results require further research from the viewpoint of geriatric somnology and molecular biology.

Pediatric sleep-disordered breathing disorders are a group of common conditions, from habitual snoring to obstructive sleep apnea (OSA) syndrome, affecting a significant proportion of children. The present article summarizes the current knowledge on diagnosis and treatment of pediatric OSA focusing on therapeutic and surgical advancements in the field in recent years. Advancements in OSA such as biomarkers, improving continuous pressure therapy adherence, novel pharmacotherapies, and advanced surgeries are discussed.

BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods.
METHODS: This study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal\'s dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model\'s efficacy in identifying SLA occurrences.
RESULTS: The proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %.
CONCLUSIONS: These results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.

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This study explores the feasibility of a wearable system to monitor vital signs during sleep. The system incorporates five inertial measurement units (IMUs) located on the waist, the arms, and the legs. To evaluate the performance of a novel framework, twenty-three participants underwent a sleep study, and vital signs, including respiratory rate (RR) and heart rate (HR), were monitored via polysomnography (PSG). The dataset comprises individuals with varying severity of sleep-disordered breathing (SDB). Using a single IMU sensor positioned at the waist, strong correlations of more than 0.95 with the PSG-derived vital signs were obtained. Low inter-participant mean absolute errors of about 0.66 breaths/min and 1.32 beats/min were achieved, for RR and HR, respectively. The percentage of data available for analysis, representing the time coverage, was 98.3% for RR estimation and 78.3% for HR estimation. Nevertheless, the fusion of data from IMUs positioned at the arms and legs enhanced the inter-participant time coverage of HR estimation by over 15%. These findings imply that the proposed methodology can be used for vital sign monitoring during sleep, paving the way for a comprehensive understanding of sleep quality in individuals with SDB.

Pediatric obstructive sleep apnea poses a significant health risk, with potential long-term consequences on cardiovascular health. This study explores the dichotomous nature of neonatal cardiac response to chronic intermittent hypoxia (CIH) between males and females, aiming to fill a critical knowledge gap in the understanding of sex-specific cardiovascular consequences of sleep apnea in early life. Neonates were exposed to CIH until p28 and underwent comprehensive in vivo physiological assessments, including whole-body plethysmography, treadmill stress-tests, and echocardiography. Results indicated that male CIH rats weighed 13.7% less than age-matched control males (p = 0.0365), while females exhibited a mild yet significant increased respiratory drive during sleep (93.94 ± 0.84 vs. 95.31 ± 0.81;p = 0.02). Transcriptomic analysis of left ventricular tissue revealed a substantial sex-based difference in the cardiac response to CIH, with males demonstrating a more pronounced alteration in gene expression compared to females (5986 vs. 3174 genes). The dysregulated miRNAs in males target metabolic genes, potentially predisposing the heart to altered metabolism and substrate utilization. Furthermore, CIH in males was associated with thinner left ventricular walls and dysregulation of genes involved in the cardiac action potential, possibly predisposing males to CIH-related arrhythmia. These findings emphasize the importance of considering sex-specific responses in understanding the cardiovascular implications of pediatric sleep apnea.

BACKGROUND: Sleep apnea syndrome, characterized by recurrent cessation (apnea) or reduction (hypopnea) of breathing during sleep, is a major risk factor for postoperative respiratory depression. Challenges in sleep apnea assessment have led to the proposal of alternative metrics derived from oxyhemoglobin saturation (SpO2), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO2 below 90% (CT90), as predictors of postoperative respiratory depression. However, their performance has been limited with area under the curve of 0.60 for ODI and 0.59 for CT90. Our objective was to propose novel features from preoperative overnight SpO2 which are correlated with sleep apnea severity and predictive of postoperative respiratory depression.
METHODS: Preoperative SpO2 signals from 235 surgical patients were retrospectively analyzed to derive seven features to characterize the sleep apnea severity. The features included entropy and standard deviation of SpO2 signal; below average burden characterizing the area under the average SpO2; average, standard deviation, and entropy of desaturation burdens; and overall nocturnal desaturation burden. The association between the extracted features and sleep apnea severity was assessed using Pearson correlation analysis. Logistic regression was employed to evaluate the predictive performance of the features in identifying postoperative respiratory depression.
RESULTS: Our findings indicated a similar performance of the proposed features to the conventional apnea-hypopnea index (AHI) for assessing sleep apnea severity, with average area under the curve ranging from 0.77 to 0.81. Notably, entropy and standard deviation of overnight SpO2 signal and below average burden showed comparable predictive capability to AHI but with minimal computational requirements and individuals\' burden, making them promising for screening purposes. Our sex-based analysis revealed that compared to entropy and standard deviation, below average burden exhibited higher sensitivity in detecting respiratory depression in women than men.
CONCLUSIONS: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.