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.

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.

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.

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.

Bidirectional Association Among the Type of Atrial Fibrillation, Sleep-Disordered Breathing Severity, Heart Failure Progression, and Left Atrial Enlargement, in Patients with Atrial Fibrillation.

This study aimed to clarify (1) the association among the atrial fibrillation (AF) type, sleep-disordered breathing (SDB), heart failure (HF), and left atrial (LA) enlargement, (2) the independent predictors of LA enlargement, and (3) the effects of ablation on those conditions in patients with AF. The study's endpoint was LA enlargement (LA volume index [LAVI] ≥ 78 mL/m2).Of 423 patients with nonvalvular AF, 236 were enrolled. We evaluated the role of the clinical parameters such as the AF type, SDB severity, and HF in LA enlargement. Among them, 141 patients exhibiting a 3% oxygen desaturation index (ODI) of ≥ 10 events/hour underwent polysomnography to evaluate the SDB severity measured by the apnea-hypopnea index (AHI). The LA enlargement and HF were characterized by the LA diameter/LAVI, an increase in the B-type natriuretic peptide level, and a lower left ventricular ejection fraction.This study showed that non-paroxysmal AF (NPAF) rather than paroxysmal AF (PAF), the SDB severity, LA enlargement, and HF progression had bidirectional associations and exacerbated each other, which generated a vicious cycle that contributed to the LA enlargement. NPAF (OR = 4.55, P < 0.001), an AHI of ≥ 25.10 events/hour (OR = 1.55, P = 0.003), and a 3% ODI of ≥ 15.43 events/hour (OR = 1.52, P = 0.003) were independent predictors of an acceleration of the LA enlargement. AF ablation improved the HF and LA enlargement.To break this vicious cycle, AF ablation may be the basis for suppressing the LA enlargement and HF progression subsequently eliminating the substrates for AF and SDB in patients with AF.

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.

[Changes the level of differentiating growth factor - GDF 15 in patients with sleep apnea syndrome after aPAP-therapy: pilot study]. / Izmenenie urovnya differentsirovochnogo faktora rosta ­ GDF 15 u patsientov s sindromom apnoe sna na fone aPAP-terapii: pilotnoe issledovanie.

OBJECTIVE: Comparative assessment of the level of differentiating growth factor 15 (GDF 15 ) against the background of a 6-month course of respiratory support in the mode of automatic positive pressure in the airways therapy (aPAP therapy) in patients with obstructive sleep apnea syndrome (OSA). MATERIAL AND METHODS: 59 men participated in the study, the average age was 51.9±2.4 years. The main group (MG1) consisted of 30 patients with a verified diagnosis of moderate OSA. 29 men of comparable age and body weight made up the control group (CG) without an objectively confirmed diagnosis of OSA. After the stage of introduction into the study, the type of respiratory support with individual pressure settings was selected for patients with MG1. After 6 months of aPAP therapy with high compliance (at least 85%), the same patients who made up MG2 after treatment underwent repeated polysomnography (PSG) and the GDF 15 content was evaluated. Methods: questionnaire, examination, polysomnography, enzyme immunoassay of blood serum to determine the content of GDF 15. RESULTS: A 6-month course of aPAP therapy with a high degree of compliance significantly improved the sleep structure and breathing pattern: the representation of NREM 3 increased from 79.2±15.6 to 102.6±21.6 minutes and the REM phase from 56.9± 13.6 to 115.6±26.8. Episodes of apnea were eliminated - apnea-hypopnea index decreased from 21.1 [17.3; 39.1] to 2.5 [1.8; 4.6] and the average values of SaO2 increased from 85.9% to 91.5%. At the same time, a statistically significant excess of GDF 15 was revealed in MG1 - 20.4 [14.16; 31.71] and MG2 - 17.2 [13.63; 24.44]) in comparison with CG - 13.65 [10.7; 17.09]. Despite the lack of statistical significance, a change in the level of GDF 15 was revealed in the form of a decrease in its concentration after a 6-month course of aPAP therapy. CONCLUSION: A 6-month course of aPAP therapy made it possible to eliminate intermittent nocturnal hypoxia and improve sleep structure in patients with OSA, as well as reduce the content of GDF 15 protein in blood serum in patients with OSA. However, the tendency to decrease the content of this protein, despite the lack of statistical reliability, confirms the effectiveness of OSA therapy and the possibility of preventing early and pathological aging from the standpoint of somnology and molecular biogerontology.

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations.

OBJECTIVE: Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. METHOD: In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. RESULTS: In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. CONCLUSION: The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.

Snoring-generated fluid droplets as a potential mechanistic link between sleep-disordered breathing and pneumonia.

The soft palate and back of the throat represent vulnerable early infection sites for SARS-CoV-2, influenza, streptococci, and many other pathogens. We demonstrate that snoring causes aerosolization of pharyngeal fluid that covers these surfaces, which previously has escaped detection because the inspired airstream carries the micron-sized droplets into the lung, inaccessible to traditional aerosol detectors. While many of these droplets will settle in the lower respiratory tract, a fraction of the respirable smallest droplets remains airborne and can be detected in exhaled breath. We distinguished these exhaled droplets from those generated by the underlying breathing activity by using a chemical tracer, thereby proving their existence. The direct transfer of pharyngeal fluids and their pathogens into the deep lung by snoring represents a plausible mechanistic link between the previously recognized association between sleep-disordered breathing and pneumonia incidence.

Definition of diaphragmatic sleep disordered breathing and clinical meaning in Duchenne muscular dystrophy.

BACKGROUND: Diaphragmatic sleep disordered breathing (dSDB) has been recently identified as sleep dysfunction secondary to diaphragmatic weakness in Duchenne muscular dystrophy (DMD). However, scoring criteria for the identification of dSDB are missing.This study aimed to define and validate dSDB scoring criteria and to evaluate whether dSDB severity correlates with respiratory progression in DMD. METHODS: Scoring criteria for diaphragmatic apnoea (dA) and hypopnoeas (dH) have been defined by the authors considering the pattern observed on cardiorespiratory polygraphy (CR) and the dSDB pathophysiology.10 sleep professionals (physiologists, consultants) blinded to each other were involved in a two-round Delphi survey to rate each item of the proposed dSDB criteria (Likert scale 1-5) and to recognise dSDB among other SDB. The scorers' accuracy was tested against the authors' panel.Finally, CR previously conducted in DMD in clinical setting were rescored and diaphragmatic Apnoea-Hypopnoea Index (dAHI) was derived. Pulmonary function (forced vital capacity per cent of predicted, FVC%pred), overnight oxygen saturation (SpO2) and transcutaneous carbon dioxide (tcCO2) were correlated with dAHI. RESULTS: After the second round of Delphi, raters deemed each item of dA and dH criteria as relevant as 4 or 5. The agreement with the panel in recognising dSDB was 81%, kappa 0.71, sensitivity 77% and specificity 85%.32 CRs from DMD patients were reviewed. dSDB was previously scored as obstructive. The dAHI negatively correlated with FVC%pred (r=-0.4; p<0.05). The total number of dA correlated with mean overnight tcCO2 (r 0.4; p<0.05). CONCLUSIONS: dSDB is a newly defined sleep disorder that correlates with DMD progression. A prospective study to evaluate dSDB as a respiratory measure for DMD in clinical and research settings is planned.

A comparison of caregiver-reported snoring and objective snoring in children from urban neighborhoods.

OBJECTIVE: Sleep Disordered Breathing (SDB) is both prevalent and under-recognized in pediatric minority populations. Recognition of SDB is often triggered by symptoms of caregiver-reported snoring. However, the validity and utility of caregiver reports likely vary across populations. Our objective is to assess the association between caregiver-reported snoring and objectively recorded snoring in a low-income urban community and explore factors associated with agreement between objective and subjective snoring. METHODS: 169 6 to 12 year old participants underwent at-home sleep studies with a WatchPAT device as part of the Environmental Assessment of Sleep in Youth (EASY) cohort study. Differences in subjective snoring, objective snoring, and concordance between subjective and objective snoring based on socioeconomic and clinical characteristics were assessed. RESULTS: The sample had a high proportion of non-white (78.9 %) and low income (39.6 %) children. Caregivers reported snoring for 20.7 % of the children and snoring was measured objectively for 21.9 %. Of those with objective snoring, only 29.7 % were identified as snorers by caregiver report (sensitivity: 0.30; specificity: 0.82). Primary Spanish language and co-sleeping were associated with increased caregiver reported snoring, and allergy was associated with increased objective snoring. Older child age and normal range BMI percentile were associated with higher concordance between caregiver and objective snoring. CONCLUSIONS: Among a community-based, predominantly minority sample, caregiver-reported snoring resulted in under-estimation of prevalence of objectively assessed snoring. Reliance on caregiver report may poorly identify children with snoring or SDB in clinical practice.

Sleep Apnea and Heart Failure-Current State-of-The-Art.

Sleep-disordered breathing (SDB), including obstructive and central sleep apnea, significantly exacerbates heart failure (HF) through adverse cardiovascular mechanisms. This review aims to synthesize existing literature to clarify the relationship between SDB and HF, focusing on the pathophysiological mechanisms, diagnostic challenges, and the effectiveness of treatment modalities like continuous positive airway pressure (CPAP) and adaptive servo-ventilation ASV. We analyzed peer-reviewed articles from 2003 to 2024 sourced from PubMed, EMBASE, Scopus, and Web of Science databases. The prevalence of SDB in HF patients is high, often underdiagnosed, and underappreciated. Management strategies, including CPAP and ASV, have been shown to mitigate symptoms and improve cardiac function. However, despite the availability of effective treatments, significant challenges in screening and diagnosis persist, affecting patient management and outcomes. DB significantly impacts HF prognosis. Enhanced screening strategies and broader utilization of therapeutic interventions like CPAP and ASV are essential to improve the management and outcomes of HF patients with concomitant SDB. Future research should focus on refining diagnostic and treatment protocols to optimize care for HF patients with SDB.

Assessing sleep-related breathing disorders among newly diagnosed rheumatoid and psoriatic arthritis patients: a cross-sectional study.

OBJECTIVES: This cross-sectional study aimed to determine the prevalence and risk factors for sleep-related breathing disorders (SRBD) in newly diagnosed, untreated rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients, and to develop a screening algorithm for early detection. METHODS: We evaluated newly diagnosed RA or PsA patients using the Epworth Sleepiness Scale (ESS) questionnaire, cardiorespiratory polygraphy (RPG), and clinical and laboratory assessments. Sleep apnea syndrome (SAS) was diagnosed based on pathological RPG findings excessive daytime sleepiness, defined as ESS score above 10. RESULTS: The study included 39 patients (22 RA, 17 PsA) and 23 controls. In RPG, SRBD was identified in 38.5% of arthritis patients compared to 39.1% of controls (p = 1.00), with male gender (p = .004) and age (p < .001) identified as risk factors. Excessive daytime sleepiness was noted in 36.4% of RA patients, 17.6% of PsA patients, and 21.7% of controls. Of the 24 patients diagnosed with SRBD, 41.6% met the criteria for SAS. SAS prevalence was 31.8% among RA patients, 0% in PsA patients, and 13% in controls. A significant association was observed between excessive daytime sleepiness and SRBD (p = .036). CONCLUSION: Our findings reveal a high prevalence of SRBD in newly diagnosed, untreated RA and PsA patients in ESS and RPG, with excessive daytime sleepiness being a reliable predictor of SRBD. Patients with RA exhibited a higher predisposition to SAS. We therefore suggest incorporating ESS and RPG as screening tools in RA or PsA for early detection and management of SRBD.

The use of pulse transit time in diagnostics of sleep-disordered breathing in children.

INTRODUCTION: Sleep is the physiological state of the body where proper morphology and duration are indispensable for human functions throughout both, physical and mental spheres. Disordered breathing during sleep impairs its morphology and results in major disorders in any age group. Adverse effects of Obstructive Sleep Apnea Syndrome in children and poor availability of centers offering children's polysomnography call for a reliable and easily accessible screening method. AIM: The aim of the study were to evaluate the usefulness of pulse transit time in the diagnostics of disordered sleep breathing in children and to attempt to employ the parameter in screening tests. Pulse transit time is a physiological parameter determining the time needed for the pulse wave to travel between two measurement points. MATERIAL AND METHODS: Enrolled in the retrospective study were 153 patients (100 boys and 53 girls) suspected of obstructive sleep apnea syndrome who underwent polysomnography at I. Moscicki ENT Hospital in Chorzów. RESULTS: Statistically significant relations between apnea/hypopnea index and pulse transit time were observed in both, individual age groups and all of the patients. Pulse transit time results proved a negative correlation with apnea/hypopnea index values commonly accepted as a parameter concluding the polysomnography procedures. CONCLUSIONS: The results of the study indicate that pulse transit time measurements may find application in screening tests of sleep-disordered breathing in children.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.