Hypopnea definitions, determinants and dilemmas: a focused review

Application of non-contact sensors for health monitoring in hospitals: a narrative review

The continuous monitoring of the health status of patients is essential for the effective monitoring of disease progression and the management of symptoms. Recently, health monitoring using non-contact sensors has gained interest. Therefore, this study aimed to investigate the use of non-contact sensors for health monitoring in hospital settings and evaluate their potential clinical applications. A comprehensive literature search was conducted using PubMed to identify relevant studies published up to February 26, 2024. The search terms included "hospital," "monitoring," "sensor," and "non-contact." Studies that used non-contact sensors to monitor health status in hospital settings were included in this review. Of the 38 search results, five studies met the inclusion criteria. The non-contact sensors described in the studies were radar, infrared, and microwave sensors. These non-contact sensors were used to obtain vital signs, such as respiratory rate, heart rate, and body temperature, and were then compared with the results from conventional measurement methods (polysomnography, nursing records, and electrocardiography). In all the included studies, non-contact sensors demonstrated a performance similar to that of conventional health-related parameter measurement methods. Non-contact sensors are expected to be a promising solution for health monitoring in hospital settings.

Non-sleep related outcomes of maxillomandibular advancement, a systematic review

Maxillomandibular advancement has been shown to be an effective treatment for obstructive sleep apnea; however, the literature focuses mainly on sleep-related parameters such as apnea-hypopnea index, respiratory disturbance index and Epworth sleepiness scale. Other factors that may be important to patients, such as esthetics, patient satisfaction, nasality, swallowing problems and so forth have been reported in the literature but have not been systematically studied. Together with an information specialist, an extensive search in Medline, Embase and Scopus yielded 1592 unique articles. Titles and abstracts were screened by two blinded reviewers. In total, 75 articles were deemed eligible for full-text screening and 38 articles were included for qualitative synthesis. The most common categories of non-sleep related outcomes found were surgical accuracy, facial esthetics, functional outcomes, quality of life, patient satisfaction, and emotional health. All categories were reported using heterogenous methods, such that meta-analysis could not be performed. There was lack of consistent methods to assess these outcomes. This work is the first to systematically review non-sleep related outcomes of maxillomandibular advancement. Despite growing interest in evaluating surgical outcomes through patient subjective experiences, this review points to the need of standardized, validated methods to report these outcomes.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity

BACKGROUND

/Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening.

METHODS

An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data.

RESULTS

Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants.

CONCLUSIONS

The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

Obstructive Sleep Apnea Resolution in Hypopnea-Predominant versus Apnea-Predominant Patients after Maxillomandibular Advancement

This retrospective cohort study aimed: (1) to analyze the influence of apnea-predominant versus hypopnea-predominant obstructive sleep apnea (OSA) on surgical outcome after maxillomandibular advancement (MMA); and (2) to evaluate whether MMA alters the presence of apnea-predominant to hypopnea-predominant OSA more than vice versa. In total 96 consecutive moderate to severe OSA patients, who underwent MMA between 2010 and 2021, were included. The baseline apnea−hypopnea index, apnea index, and oxygen desaturation index were significantly higher in apnea-predominant group, while the hypopnea index was significantly higher in hypopnea-predominant group (p < 0.001). No significant difference was found between apnea-predominant group and hypopnea-predominant group in the degree of advancement of A-point, B-point, and pogonion. Surgical success and cure were significantly higher in the hypopnea-predominant group compared to the apnea-predominant group, 57.4% versus 82.1% (p = 0.021) and 13.2% versus 55.5% (p = 0.012), respectively. Of the 68 (70.8%) apnea-predominant patients, 37 (54.4%) shifted to hypopnea-predominant after MMA. Of the 28 (29.2%) hypopnea-predominant patients, 7 (25%) shifted to apnea-predominant postoperatively. These findings suggest that preoperative hypopnea-predominant OSA patients might be more suitable candidates for MMA compared to preoperative apnea-predominant OSA patients. Additionally, MMA proved to alter the presence of apnea-predominant to hypopnea-predominant OSA to a larger extend than vice versa.

Sleep-disordered breathing, sleep apnea, and other obesity-related sleep disorders: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of sleep-disordered breathing, (e.g., sleep-related hypopnea, apnea), and other obesity-related sleep disorders.

Methods

The scientific support for this CPS is based upon published citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.

Results

Obesity contributes to sleep-disordered breathing, with the most prevalent manifestation being obstructive sleep apnea. Obesity is also associated with other sleep disorders such as insomnia, primary snoring, and restless legs syndrome. This CPS outlines the evaluation, diagnosis, and treatment of sleep apnea and other sleep disorders, as well as the clinical implications of altered circadian system.

Conclusions

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) on "Sleep-Disordered Breathing, Sleep Apnea, and Other Obesity-Related Sleep Disorders" is one of a series of OMA CPSs designed to assist clinicians in the care of patients with the disease of obesity.

Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals

Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects. In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO₂) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique. Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.

Prevalence and Assessment of Sleep-Disordered Breathing in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis

BACKGROUND

In this study, we sought to estimate the prevalence of concomitant sleep-disordered breathing (SDB) in patients with atrial fibrillation (AF) and to systematically evaluate how SDB is assessed in this population.

METHODS

We searched Medline, Embase and Cinahl databases through August 2020 for studies reporting on SDB in a minimum 100 patients with AF. For quantitative analysis, studies were required to have systematically assessed for SDB in consecutive AF patients. Pooled prevalence estimates were calculated with the use of the random effects model. Weighted mean differences and odds ratios were calculated when possible to assess the strength of association between baseline characteristics and SDB.

RESULTS

The search yielded 2758 records, of which 33 studies (n = 23,894 patients) met the inclusion criteria for qualitative synthesis and 13 studies (n = 2660 patients) met the meta-analysis criteria. The pooled SDB prevalence based on an SDB diagnosis cutoff of apnea-hypopnea index (AHI) ≥ 5/h was 78% (95% confidence interval [CI] 70%-86%; P < 0.001). For moderate-to-severe SDB (AHI ≥ 15/h), the pooled SDB prevalence was 40% (95% CI 32%-48%; P < 0.001). High degrees of heterogeneity were observed (I² = 96% and 94%, respectively; P < 0.001). Sleep testing with the use of poly(somno)graphy or oximetry was the most common assessment tool used (in 22 studies, 66%) but inconsistent diagnostic thresholds were used.

CONCLUSIONS

SDB is highly prevalent in patients with AF. Wide variation exists in the diagnostic tools and thresholds used to detect concomitant SDB in AF. Prospective systematic testing for SDB in unselected cohorts of AF patients may be required to define the true prevalence of SDB in this population.

Obstructive sleep apneas naturally occur in mice during REM sleep and are highly prevalent in a mouse model of Down syndrome

STUDY OBJECTIVES

The use of mouse models in sleep apnea study is limited by the belief that central (CSA) but not obstructive sleep apneas (OSA) occur in rodents. We aimed to develop a protocol to investigate the presence of OSAs in wild-type mice and, then, to apply it to a validated model of Down syndrome (Ts65Dn), a human pathology characterized by a high incidence of OSAs.

METHODS

In a pilot study, nine C57BL/6J wild-type mice were implanted with electrodes for electroencephalography (EEG), neck electromyography (nEMG), and diaphragmatic activity (DIA), and then placed in a whole-body-plethysmographic (WBP) chamber for 8 h during the rest (light) phase to simultaneously record sleep and breathing activity. CSA and OSA were discriminated on the basis of WBP and DIA signals recorded simultaneously. The same protocol was then applied to 12 Ts65Dn mice and 14 euploid controls.

RESULTS

OSAs represented about half of the apneic events recorded during rapid-eye-movement-sleep (REMS) in each experimental group, while the majority of CSAs were found during non-rapid eye movement sleep. Compared with euploid controls, Ts65Dn mice had a similar total occurrence rate of apneic events during sleep, but a significantly higher occurrence rate of OSAs during REMS, and a significantly lower occurrence rate of CSAs during NREMS.

CONCLUSIONS

Mice physiologically exhibit both CSAs and OSAs. The latter appear almost exclusively during REMS, and are highly prevalent in Ts65Dn. Mice may, thus, represent a useful model to accelerate the understanding of the pathophysiology and genetics of sleep-disordered breathing and to help the development of new therapies.

Diagnostic accuracy of screening questionnaires for obstructive sleep apnoea in adults in different clinical cohorts: a systematic review and meta-analysis

PURPOSE

The majority of individuals with clinically significant obstructive sleep apnoea (OSA) are undiagnosed and untreated. A simple screening tool may support risk stratification, identification, and appropriate management of at-risk patients. Therefore, this systematic review and meta-analysis evaluated and compared the accuracy and clinical utility of existing screening questionnaires for identifying OSA in different clinical cohorts.

METHODS

We conducted a systematic review and meta-analysis of observational studies assessing the diagnostic value of OSA screening questionnaires. We identified prospective studies, validated against polysomnography, and published to December 2020 from online databases. To pool the results, we used random effects bivariate binomial meta-analysis.

RESULTS

We included 38 studies across three clinical cohorts in the meta-analysis. In the sleep clinic cohort, the Berlin questionnaire's pooled sensitivity for apnoea-hypopnoea index (AHI) ≥ 5, ≥ 15, and ≥ 30 was 85%, 84%, and 89%, and pooled specificity was 43%, 30%, and 33%, respectively. The STOP questionnaire's pooled sensitivity for AHI ≥ 5, ≥ 15, and ≥ 30 was 90%, 90%, and 95%, and pooled specificity was 31%, 29%, and 21%. The pooled sensitivity of the STOP-Bang questionnaire for AHI ≥ 5, ≥ 15, and ≥ 30 was 92%, 95%, and 96%, and pooled specificity was 35%, 27%, and 28%. In the surgical cohort (AHI ≥ 15), the Berlin and STOP-Bang questionnaires' pooled sensitivity were 76% and 90% and pooled specificity 47% and 27%.

CONCLUSION

Among the identified questionnaires, the STOP-Bang questionnaire had the highest sensitivity to detect OSA but lacked specificity. Subgroup analysis considering other at-risk populations was not possible. Our observations are limited by the low certainty level in available data.

PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies

The sleep apnea syndrome is a chronic condition that affects the quality of life and increases the risk of severe health conditions such as cardiovascular diseases. However, the prevalence of the syndrome in the general population is considered to be heavily underestimated due to the restricted number of people seeking diagnosis, with the leading cause for this being the inconvenience of the current reference standard for apnea diagnosis: Polysomnography. To enhance patients' awareness of the syndrome, a great endeavour is conducted in the literature. Various home-based apnea detection systems are being developed, profiting from information in a restricted set of polysomnography signals. In particular, breathing sound has been proven highly effective in detecting apneic events during sleep. The development of accurate systems requires multitudinous datasets of audio recordings and polysomnograms. In this work, we provide the first open access dataset, comprising 212 polysomnograms along with synchronized high-quality tracheal and ambient microphone recordings. We envision this dataset to be widely used for the development of home-based apnea detection techniques and frameworks.

Incidence of hypertension in obstructive sleep apnea using hypopneas defined by 3 percent oxygen desaturation or arousal but not by only 4 percent oxygen desaturation

STUDY OBJECTIVES

This analysis determined ∼5-year incident hypertension rates using the 2017 American College of Cardiology/American Heart Association blood pressure (BP) guidelines in individuals with obstructive sleep apnea (OSA) with hypopneas defined by a ≥ 3% oxygen desaturation or arousal but not by a hypopnea criterion of ≥ 4% oxygen desaturation (4% only).

METHODS

Data were analyzed from participants in the Sleep Heart Health Study exam 2 (n = 1219) who were normotensive (BP ≤ 120/80 mm Hg) at exam 1. The AHI at exam 1 was classified into 4 categories of OSA severity: < 5, 5 ≤ 15, 15 ≤ 30, and ≥ 30 events/h using both the 3% oxygen desaturation or arousal and the 4% only definitions. Three definitions of hypertension-elevated BP (> 120/80 mm Hg), stage 1 (> 130/80 mm Hg), and stage 2 (> 140/90 mm Hg)-were used to determine incidence rates at exam 2.

RESULTS

Five-year follow-up was available for 476 participants classified as having OSA by the 3% oxygen desaturation or arousal criterion but not by the 4% only standard at exam 1. Incident hypertension using American College of Cardiology/American Heart Association-defined BP categories in these discordantly classified individuals were 15% (elevated BP), 15% (stage 1), and 6% (stage 2). Hypertensive medications were used in 4% of participants who were normotensive. The overall incidence rate of at least an elevated BP was 40% (191/476) in those with OSA defined using the 3% oxygen desaturation or arousal criterion but not by the 4% only criterion.

CONCLUSIONS

Use of the 4% only hypopnea definition resulted in the failure to identify a significant number of individuals with OSA who eventually developed hypertension and could have benefited from earlier diagnosis and treatment.