Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.

BACKGROUND: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. OBJECTIVE: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. METHODS: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. RESULTS: Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. CONCLUSIONS: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

Causal Associations of Sleep Apnea With Alzheimer Disease and Cardiovascular Disease: A Bidirectional Mendelian Randomization Analysis.

BACKGROUND: Sleep apnea (SA) has been linked to an increased risk of dementia in numerous observational studies; whether this is driven by neurodegenerative, vascular, or other mechanisms is not clear. We sought to examine the bidirectional causal relationships between SA, Alzheimer disease (AD), coronary artery disease (CAD), and ischemic stroke using Mendelian randomization. METHODS AND RESULTS: Using summary statistics from 4 recent, large genome-wide association studies of SA (n=523 366), AD (n=94 437), CAD (n=1 165 690), and stroke (n=1 308 460), we conducted bidirectional 2-sample Mendelian randomization analyses. Our primary analytic method was fixed-effects inverse variance-weighted (IVW) Mendelian randomization; diagnostics tests and sensitivity analyses were conducted to verify the robustness of the results. We identified a significant causal effect of SA on the risk of CAD (odds ratio [ORIVW]=1.35 per log-odds increase in SA liability [95% CI=1.25-1.47]) and stroke (ORIVW=1.13 [95% CI=1.01-1.25]). These associations were somewhat attenuated after excluding single-nucleotide polymorphisms associated with body mass index (ORIVW=1.26 [95% CI=1.15-1.39] for CAD risk; ORIVW=1.08 [95% CI=0.96-1.22] for stroke risk). SA was not causally associated with a higher risk of AD (ORIVW=1.14 [95% CI=0.91-1.43]). We did not find causal effects of AD, CAD, or stroke on risk of SA. CONCLUSIONS: These results suggest that SA increased the risk of CAD, and the identified causal association with stroke risk may be confounded by body mass index. Moreover, no causal effect of SA on AD risk was found. Future studies are warranted to investigate cardiovascular pathways between sleep disorders, including SA, and dementia.

Oximetry and carbon dioxide screening for ventilatory requirements in children with spinal muscular atrophy type 1-3.

INTRODUCTION: Despite disease modifying treatments (DMT), assisted ventilation is commonly required in children with Spinal Muscular Atrophy (SMA). Guidelines suggest screening with oximetry and transcutaneous carbon dioxide (TcCO2) for sleep disordered breathing (SDB). AIM: To determine the utility of pulse oximetry and TcCO2 as a screen for SDB and the need for Non-Invasive Ventilation (NIV) in children with SMA type 1-3. METHODS: A prospective cohort study was conducted in Queensland, Australia. Full diagnostic PSG was completed in DMT naïve children with SMA. Pulse oximetry and TcCO2 were extracted from PSG. Apnoea-hypopnoea indices (AHI) criteria were applied to PSG results to define the need for NIV. Abnormal was defined as: ≤3 months of age [mo] AHI≥10 events/hour; >3mo AHI ≥5 events/hour. Receiver operating characteristic curves were calculated for abnormal PSG and pulse oximetry/TcCO2 variables, and diagnostic statistics were calculated. RESULTS: Forty-seven untreated children with SMA were recruited (type 1 n = 13; 2 n = 21; 3 n = 13) ranging from 0.2 to 18.8 years old (median 4.9 years). Oxygen desaturation index ≥4 % (ODI4) ≥20events/hour had sensitivity 82.6 % (95 % CI 61.2-95.0) and specificity of 58.3 % (95 % CI 36.6-77.9). TcCO2 alone and combinations of oximetry/TcCO2 had low diagnostic ability. The same methodology was applied to 36 children who were treated (type 1 n = 7; type 2 n = 17; type n = 12) and oximetry±TcCO2 had low diagnostic ability. CONCLUSION: ODI4 ≥20events/hour can predict the need for NIV in untreated children with SMA. TcCO2 monitoring does not improve the PPV. If normal however, children may still require a diagnostic PSG. Neither oximetry nor TcCO2 monitoring were useful screening tests in the children treated with DMT.

[Arrhythmia in sleep apnea]. / Arrhythmien bei Schlafapnoe.

BACKGROUND: Sleep apnea is a widespread and yet still underdiagnosed condition. Various studies from the past have provided evidence that there is a link between sleep apnea and various cardiovascular diseases, including arrhythmias. OBJECTIVE: The aim of this article is to provide an overview of the current study situation and to point out possible consequences relevant to everyday life. MATERIAL AND METHODS: A systematic search was carried out in various databases using the keywords sleep apnea (OSAS/SA) and arrhythmias/dysrhythmias. RESULTS: There are several pathophysiological links between sleep-related breathing disorders and cardiac arrhythmias, the most important of which appear to be intrathoracic pressure, increased adrenergic tone as well as recurrent hypoxia and hypercapnia. This results in an increased occurrence of clinically relevant arrhythmias, such as atrial fibrillation, symptomatic bradycardia, high-grade atrioventricular (AV) blocks as well as ventricular arrhythmias in patients with untreated sleep apnea. These pathologies also appear to be positively influenced by the treatment of sleep apnea. CONCLUSION: A close correlation between sleep apnea and cardiac arrhythmias is undisputed. Large randomized studies in this respect are so far rare but it is undisputed that a thorough search should be carried out for sleep apnea and consistently treated in patients with a history of cardiac disease as this can have a relevant influence on the treatment and ultimately the prognosis of the patient.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement.

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

[Development of an Intelligent Multi-Parameter Sleep Diagnosis and Analysis System].

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.

Validation of a sleep-disordered breathing screening questionnaire during pregnancy and comparison between mothers and bedpartners prediction of risk.

BACKGROUND: Sleep Disorder Breathing (SDB) in pregnant patients ranges from 3 to 27% and varies depending on gestational age and method used to diagnose. SDB increases the risk of advanced pregnancy complications such as gestational diabetes mellitus, pregnancy-induced hypertension, and preeclampsia. Screening and diagnosis of SDB during pregnancy remains a challenge, with existing screening tools underperforming during pregnancy. This study aimed to validate a previously developed model for predicting SDB during late pregnancy and compare the predictive value of bedpartner responses. METHODS: Ninety-six women in the third trimester of pregnancy underwent polysomnography and completed the Berlin Questionnaire (BQ), with 81 bedpartners completing the BQ about their pregnant partner. A subset of BQ items (snoring volume and tiredness upon awakening) along with BMI > 32 kg/m2 was utilised to calculate the Wilson Optimized Model (WOM), which demonstrated strong predictive properties in development. RESULTS: SDB (RDI/hr ≥ 5) was detected in 43.8% of women. BQ identified 72% of pregnant mothers as high risk for SDB (Sensitivity = 83%, Specificity = 37%), compared to 29% of mothers identified by the WOM (Sensitivity = 45%, Specificity = 83%). At RDI of ≥ 15, the WOM correctly classified more women according to SDB risk than the BQ (76.0% vs. 41.7% cases correct, X2(1) = 23.42, p < .001), with no difference at RDI ≥ 5. Bedpartners were more likely to report high risk for SDB on the WOM than pregnant women themselves (38.3% vs. 28.4%), however predictive ability was not improved by bedpartner input (RDI ≥ 5 bedpartner AUC = 0.69 v mother AUC = 0.73). CONCLUSION: BQ largely overestimates the prevalence of SDB in pregnancy compared to the WOM which underestimates. Utilising bedpartner responses didn't improve screening for SDB in late pregnancy. More work is needed to develop a pregnancy-specific tool for quick and accurate screening for SDB.

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model.

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

An Inertial-Based Wearable System for Monitoring Vital Signs during Sleep.

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.

Advances in Sleep-Disordered Breathing in Children.

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.

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

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. METHOD: 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.

Screening and risk assessment for sleep-related breathing disorders in individuals living with spinal cord injury.

OBJECTIVES: This research work was performed: (1) To assess the accessibility of in-laboratory polysomnography for individuals with spinal cord injury (SCI); (2) to evaluate the validity of four screening questionnaires for sleep-related breathing disorders (SRBDs); and (3) to assess the association between anthropometric features and apnea-hypopnea index (AHI). METHODS: An Environmental scan (E-scan) was performed in the province of Ontario, where all sleep clinics were invited to complete the E-scan survey. Furthermore, a cross-sectional study was performed at a rehabilitation hospital (Canada), where consecutive adults with subacute/chronic (>1 month) SCI were recruited. Using a home-based screening sleep test (HBSST), the validity of the Berlin, STOP, Medical Outcomes Study Sleep Scale [MOS-SS], and STOP-Bang screening questionnaires was assessed. The association between AHI and three features (i.e., neck circumference, body mass index [BMI] and oropharynx opening as assessed using the Modified Mallampati classification [MMC]) was evaluated. RESULTS: According to the E-scan, access to polysomnography is limited for the SCI population in Ontario. Of the 28 participants with SCI (11 females, 17 males; mean age: 54.9 years) included in the cross-sectional study, 32.1 % were diagnosed with moderate-to-severe SRBD. The performance of the questionnaires was considered insufficient for screening of individuals living with SCI. AHI was not associated with neck circumference, BMI, or MMC. CONCLUSIONS: Those results suggest that the use of a HBSST could overcome the barriers for individuals with SCI to access diagnostic testing of SRBDs. The use of screening questionnaires and risk assessment for SRBDs in the SCI population is unreliable.

Change-point detection using diffusion maps for sleep apnea monitoring with contact-free sensors.

Monitoring and improving the quality of sleep are crucial from a public health perspective. In this study, we propose a change-point detection method using diffusion maps for a more accurate detection of respiratory arrest points. Conventional change-point detection methods are limited when dealing with complex nonlinear data structures, and the proposed method overcomes these limitations. The proposed method embeds subsequence data in a low-dimensional space while considering the global and local structures of the data and uses the distance between the data as the score of the change point. Experiments using synthetic and real-world contact-free sensor data confirmed the superiority of the proposed method when dealing with noise, and it detected apnea events with greater accuracy than conventional methods. In addition to improving sleep monitoring, the proposed method can be applied in other fields, such as healthcare, manufacturing, and finance. This study will contribute to the development of advanced monitoring systems that adapt to diverse conditions while protecting privacy.

Non-cardiac comorbidities in heart failure: an update on diagnostic and management strategies.

Managing non-cardiac comorbidities in heart failure (HF) requires a tailored approach that addresses each patient's specific conditions and needs. Regular communication and coordination among healthcare providers is crucial to providing the best possible care for these patients. Poorly controlled hypertension contributes to left ventricular remodeling and diastolic dysfunction, emphasizing the importance of optimal blood pressure control while avoiding adverse effects. Among HF patients with diabetes, SGLT2 inhibitors and mineralocorticoid receptor antagonists have shown promise in reducing HF-related morbidity and mortality. Chronic kidney disease exacerbates HF and vice versa, forming the vicious cardiorenal syndrome, so disease-modifying therapies should be maintained in HF patients with comorbid CKD, even with transient changes in kidney function. Anemia in HF patients may be multifactorial, and there is growing evidence for the benefit of intravenous iron supplementation in HF patients with iron deficiency with or without anemia. Obesity, although a risk factor for HF, paradoxically offers a better prognosis once HF is established, though developing treatment strategies may improve symptoms and cardiac performance. In HF patients with stroke and atrial fibrillation, anticoagulation therapy is recommended. Among HF patients with sleep-disordered breathing, continuous positive airway pressure may improve sleep quality. Chronic obstructive pulmonary disease often coexists with HF, and many patients can tolerate cardioselective beta-blockers. Cancer patients with comorbid HF require careful consideration of cardiotoxicity risks associated with cancer therapies. Depression is underdiagnosed in HF patients and significantly impacts prognosis. Cognitive impairment is prevalent in HF patients and impacts their self-care and overall quality of life.

Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?

Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.

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.

Clinical characteristics, associated comorbidities and hospital outcomes of neonates with sleep disordered breathing: a retrospective cohort study.

OBJECTIVE: Awareness of the need for early identification and treatment of sleep disordered breathing (SDB) in neonates is increasing but is challenging. Unrecognised SDB can have negative neurodevelopmental consequences. Our study aims to describe the clinical profile, risk factors, diagnostic modalities and interventions that can be used to manage neonates with SDB to facilitate early recognition and improved management. METHODS: A single-centre retrospective study of neonates referred for assessment of suspected SDB to a tertiary newborn intensive care unit in New South Wales Australia over a 2-year period. Electronic records were reviewed. Outcome measures included demographic data, clinical characteristics, comorbidities, reason for referral, polysomnography (PSG) data, interventions targeted to treat SDB and hospital outcome. Descriptive analysis was performed and reported. RESULTS: Eighty neonates were included. Increased work of breathing, or apnoea with oxygen desaturation being the most common reasons (46% and 31%, respectively) for referral. Most neonates had significant comorbidities requiring involvement of multiple specialists (mean 3.3) in management. The majority had moderate to severe SDB based on PSG parameters of very high mean apnoea-hypopnoea index (62.5/hour) with a mean obstructive apnoea index (38.7/hour). Ten per cent of patients required airway surgery. The majority of neonates (70%) were discharged home on non-invasive ventilation. CONCLUSION: SDB is a serious problem in high-risk neonates and it is associated with significant multisystem comorbidities necessitating a multidisciplinary team approach to optimise management. This study shows that PSG is useful in neonates to diagnose and guide management of SDB.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. METHODS: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. RESULTS: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. CONCLUSIONS: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

The Evolving Field of Dental Sleep Medicine.

Dental sleep medicine is a dynamic field focused on the relationship between oral health and sleep disorders, particularly sleep apnea. Dentists play a crucial role in diagnosing and treating sleep-related breathing issues. As awareness of the impact of sleep on overall health grows, the field is evolving rapidly with advancements in technology, diagnostic tools, and treatment modalities. Interdisciplinary collaboration between dentists, sleep physicians, and other health care professionals is becoming increasingly important. The integration of innovative approaches and a patient-centric focus make dental sleep medicine a pivotal player in addressing the complex interplay between oral health and sleep quality.