Belun Ring Platform: a novel home sleep apnea testing system for assessment of obstructive sleep apnea

Diagnostic accuracy of the Belun ring in children at risk of obstructive sleep apnea

STUDY OBJECTIVES

The Belun ring is a new home sleep apnea testing device using a pulse oximeter sensor and a neural network algorithm, but its data in children are limited. This study aims to evaluate the correlation and agreement of the Belun ring, compared with polysomnography (PSG) and determine the diagnostic accuracy of the Belun ring for moderate-to-severe obstructive sleep apnea (OSA).

METHODS

This is a cross-sectional observational study in children aged 5-18 years with suspected OSA between June 2023 and February 2024. The Belun ring and PSG were undertaken on eligible participants to assess apnea-hypopnea index (AHI) in the same sleep test session.

RESULTS

Of 75 children enrolled, OSA was diagnosed in 74 children by PSG. The Belun AHI (B-AHI) was moderately correlated with the PSG AHI (P-AHI) (r = .63, P < .001) with mean difference (standard deviation) -7.8 (13.91) events/h. The area under the receiver operating characteristic curve of the B-AHI to identify moderate-to-severe OSA (P-AHI > 5 events/h) was 0.66, and the B-AHI cut-off of 3 events/h yielded 74.1% sensitivity and 52.4% specificity. The B-AHI cut-off of 2 events/h yielded 92.6% sensitivity, and 7 events/h yielded 95.2% specificity.

CONCLUSIONS

Despite the correlation, the difference in AHI between the Belun ring and PSG in children was noted. Either single or multiple B-AHI cut-offs to diagnose, include or exclude moderate-to-severe OSA may be valuable, but their implementation must be approached with caution.

CLINICAL TRIAL REGISTRATION

Registry: Thai Clinical Trials Registry; Name: Diagnostic Accuracy of the Belun Ring in Children at Risk of Obstructive Sleep Apnea; URL: https://www.thaiclinicaltrials.org/show/TCTR20240604003; Identifier: TCTR20240604003.

CITATION

Panichapat N, Niyomkarn W, Boonjindasup W, Thiamrakij P, Sritippayawan S, Deerojanawong J. Diagnostic accuracy of the Belun ring in children at risk of obstructive sleep apnea. J Clin Sleep Med. 2025;21(1):123-128.

Screening moderate to severe obstructive sleep apnea with wearable device

BACKGROUND

Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder usually diagnosed through polysomnography (PSG). Moderate to severe OSA can significantly increase morbidity and mortality. Existing screening tools have limited accuracy. This study aimed to evaluate the Wellue O2 ring, a commercial pulse oximeter ring, for screening moderate to severe OSA.

METHODS

A cross-sectional study included adults aged 18 and older suspected of having OSA who underwent PSG while wearing the Wellue O2 ring on their thumb. The oxygen desaturation index (ODI) from both the O2 ring and PSG was recorded. Sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to determine the optimal ODI cutoff value for predicting moderate to severe OSA.

RESULTS

The study included 190 participants (53.2% male) with an average age of 43 years and an average apnea-hypopnea index (AHI) of 50.4 events per hour. Among the participants, 84.7% had moderate to severe OSA. The optimal cutoff value for 11% ODI was 1.25 events per hour lasting 20 s, with a sensitivity of 87.30% and a specificity of 78.70%. The area under the ROC curve for identifying moderate to severe OSA was 0.91.

CONCLUSIONS

The Wellue O2 ring demonstrated high accuracy in detecting moderate to severe OSA and could be a viable alternative for screening in clinical settings due to its accessibility and ease of use. However, larger studies are required to validate its clinical utility for diagnosing and managing moderate to severe OSA.

Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements

PURPOSE

This review aims to highlight the pivotal role of the mandibular jaw movement (MJM) signal in advancing artificial intelligence (AI)-powered technologies for diagnosing obstructive sleep apnea (OSA).

METHODS

A scoping review was conducted to evaluate various aspects of the MJM signal and their contribution to improving signal proficiency for users.

RESULTS

The comprehensive literature analysis is structured into four key sections, each addressing factors essential to signal proficiency. These factors include (1) the comprehensiveness of research, development, and application of MJM-based technology; (2) the physiological significance of the MJM signal for various clinical tasks; (3) the technical transparency; and (4) the interpretability of the MJM signal. Comparisons with the photoplethysmography (PPG) signal are made where applicable.

CONCLUSIONS

Proficiency in biosignal interpretation is essential for the success of AI-driven diagnostic tools and for maximizing the clinical benefits through enhanced physiological insight. Through rigorous research ensuring an enhanced understanding of the signal and its extensive validation, the MJM signal sets a new benchmark for the development of AI-driven diagnostic solutions in OSA diagnosis.

Assessing sleep metrics in stroke survivors: a comparison between objective and subjective measures

INTRODUCTION

Stroke survivors are at risk of sleep disturbance, which can be reflected in discrepancies between objective and subjective sleep measures. Given there are limited studies on this phenomenon and using portable monitoring devices is more convenient for stroke survivors to monitor their sleep, this study aimed to compare objectively measured (Belun Ring) and subjectively reported (sleep diary) sleep metrics (total sleep time (TST) and wakefulness after sleep onset (WASO)) in stroke survivors.

METHODS

In this cross-sectional study, thirty-five participants wore a ring-shaped pulse oximeter (Belun Ring) and kept a sleep diary for three consecutive nights in one week. The effects of various factors on TST and WASO were analyzed by linear mixed models. Systematic bias between two measures was examined by the Bland-Altman analysis.

RESULTS

TST and WASO were significantly affected by measures (p <.001), but not night. TST was significantly lower and WASO was significantly higher in the Belun Ring than in the sleep diary (p <.05). Age was the only covariate that had a significant effect on WASO (p <.05). The Bland-Altman analysis demonstrated positive bias in TST (29.55%; 95% CI [16.57%, 42.53%]) and negative bias in WASO (-117.35%; 95% CI [-137.65%, -97.06%]). Proportional bias was exhibited in WASO only (r =.31, p <.05).

CONCLUSION

The findings revealed discrepancies between objective and subjective sleep measures in stroke survivors. It is recommended that objective measures be included when assessing and monitoring their sleep conditions.

Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO2) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO2, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO2, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems.

OSA diagnosis goes wearable: are the latest devices ready to shine?

STUDY OBJECTIVES

From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature.

METHODS

We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible.

RESULTS

In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized.

CONCLUSIONS

In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies.

CITATION

Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med. 2024;20(11):1823-1838.

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.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Comparison of OPPO Watch Sleep Analyzer and Polysomnography for Obstructive Sleep Apnea Screening

Objective

To evaluate the clinical performance of the OPPO Watch (OW) Sleep Analyzer (OWSA) on OSA screening with polysomnography reference.

Methods

We recruited 350 participants using OWSA and PSG simultaneously in a sleep laboratory. The respiratory event index (REI) derived from OWSA and the apnea-hypopnea index (AHI) provided by PSG were compared. SHapley Additive exPlanation (SHAP) values were calculated to explain the model of OWSA.

Results

The OWSA-REI (26.5±18.5 events/h) correlated well with PSG-AHI (33.2±25.7 events/h; r = 0.91, p < 0.001), with an intraclass correlation coefficient (ICC) of 0.83. Using a threshold of AHI ≥15 events/h, the sensitivity, specificity, accuracy, and area under the curve (AUC) were 86.1%, 86.7%, 86.3%, and 0.94, respectively. Bland-Altman analysis showed that OWSA-REI and PSG-AHI were in good agreement (Mean Difference: -6.7, 95% CI:16.0 to -29.3 events/h). In addition, the effectiveness of the models in OWSA were also explained by visualizing SHAP values.

Conclusion

The OWSA demonstrated a reasonable performance for OSA screening in the clinical setting. In light of this, it is possible for smartwatches to become a complementary tool to PSG, which is particularly useful for larger-scale preliminary screenings.

Telemedicine for obstructive sleep apnea syndrome: An updated review

Telemedicine (TM) is a new medical service model in which computer, communication, and medical technologies and equipment are used to provide "face-to-face" communication between medical personnel and patients through the integrated transmission of data, voice, images, and video. This model has been increasingly applied to the management of patients with sleep disorders, including those with obstructive sleep apnea syndrome (OSAS). TM technology plays an important role in condition monitoring, treatment compliance, and management of OSAS cases. Herein, we review the concept of TM, its application to OSAS, and the related effects and present relevant application suggestions and strategies, which may provide concepts and references for OSAS-related TM development and application.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Technologies for sleep monitoring at home: wearables and nearables

Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea

GOAL AND AIMS

Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.

FOCUS TECHNOLOGY

Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30.

DESIGN

Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule.

CORE ANALYTICS

Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix.

CORE OUTCOMES

The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep.

CORE CONCLUSION

Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.

Comparative study of the SleepImage ring device and polysomnography for diagnosing obstructive sleep apnea

Purpose We aim to evaluate the diagnostic performance of the SleepImage Ring device in identifying obstructive sleep apnea (OSA) across different severity in comparison to standard polysomnography (PSG). Methods Thirty-nine patients (mean age, 56.8 ± 15.0 years; 29 [74.3%] males) were measured with the SleepImage Ring and PSG study simultaneously in order to evaluate the diagnostic performance of the SleepImage device for diagnosing OSA. Variables such as sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive value, and accuracy were calculated with PSG-AHI thresholds of 5, 15, and 30 events/h. Receiver operating characteristic curves were also built according to the above PSG-AHI thresholds. In addition, we analyzed the correlation and agreement between the apnea-hypopnea index (AHI) obtained from the two measurement devices. Results There was a strong correlation (r = 0.89, P < 0.001 and high agreement in AHI between the SleepImage Ring and standard PSG. Also, the SleepImage Ring showed reliable diagnostic capability, with areas under the receiver operating characteristic curve of 1.00 (95% CI, 0.91, 1.00), 0.90 (95% CI, 0.77, 0.97), and 0.98 (95% CI, 0.88, 1.000) for corresponding PSG-AHI of 5, 15 and 30 events/h, respectively. Conclusion The SleepImage Ring could be a clinically reliable and cheaper alternative to the gold standard PSG when aiming to diagnose OSA in adults.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00304-9.

Automatic monitoring of obstructive sleep apnea based on multi-modal signals by phone and smartwatch

Obstructive Sleep Apnea (OSA) is the most common sleep-related breathing disorder, with an overall population prevalence ranging from 9% to 38%, and it is associated with many cardiovascular diseases. The diagnosis of OSA requires polysomnography (PSG) testing, which is unsuitable for large-scale preliminary screening due to its high cost and discomfort to wear. Therefore, a simple and inexpensive screening method would be of great value. This study presents a novel at-home OSA screening method using a smartwatch and a smartphone to obtain several physiological signals, snoring segments, and questionnaire information during a whole night's sleep. The proposed method can distinguish four OSA risk levels based on machine learning (ML) classifications; the system was validated by conducting an in-hospital study on 350 subjects with sleep disorders. The estimated OSA risk levels are in good agreement with the OSA severity diagnosed by PSG (correlation with apnea-hypopnea index (AHI) = 0.92), and an encouraging classification performance is achieved (accuracy = 88.1%, 84.5%, 85.1%, sensitivity = 89.1%, 84.2%, 85.6% for mild, moderate and severe OSA). These findings reveal that wearable devices have the potential for large-scale OSA screening.

The Belun sleep platform to diagnose obstructive sleep apnea in patients with hypertension and high cardiovascular risk

STUDY OBJECTIVE

Current hypertension guidelines recommend that at-risk individuals be screened for obstructive sleep apnea (OSA). The Belun Ring is a wearable OSA diagnostic device worn on the palmar side of the proximal phalanx of the index finger.

METHODS

We recruited 129 participants (age: 60 ± 8 years, male sex: 88%, BMI: 27 ± 4 kg/m 2 ) with hypertension and high cardiovascular risk for a simultaneous polysomnography and Belun Ring monitoring for one night. Epworth Sleepiness Scale score more than 10 was detected in 27 (21.0%) participants.

RESULTS

In the 127 participants who completed the study, the apnea-hypopnea index (AHI) derived from polysomnography was 18.1 (interquartile range: 33.0) events/h and that derived from the Belun Ring was 19.5 (interquartile range: 23.3) events/h [intraclass correlation coefficient: 0.882, 95% confidence interval (95% CI): 0.837-0.916]. A Bland-Altman plot showed the difference between the Belun Ring and polysomnography AHIs to be -1.3 ± 10.4 events/h. Area under the receiver operating characteristic for the Belun Ring AHI was 0.961 (95% CI: 0.932-0.990, P  < 0.001). When the Belun Ring AHI of at least 15 events/h was used to diagnose OSA, the sensitivity, specificity, positive predictive value, and negative predictive value were 95.7, 77.6, 85.3, and 93.8%, respectively. The overall accuracy was 87.4%. The Cohen's kappa agreement was 0.74 ± 0.09 ( P  < 0.001). Similar results were obtained when the oxygen desaturation index was used to diagnose OSA.

CONCLUSION

A high prevalence of OSA was detected in patients with hypertension and high cardiovascular risk. The Belun Ring is a reliable device for OSA diagnosis similar to polysomnography.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Sleep Technology

Pediatric sleep providers frequently encounter issues related to sleep technology in clinical settings. In this review article, we discuss technical issues related to standard polysomnography, research on putative complementary novel metrics derived from polysomnographic signals as well as research on home sleep apnea testing in children and consumer sleep devices. Although developments across several of these domains are exciting, it remains a rapidly evolving area. When evaluating innovative devices and home sleep testing approaches, clinicians should be mindful of accurately interpreting diagnostic agreement statistics to apply these technologies appropriately.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis

Objectives

To assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS).

Methods

Six databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool.

Results

Eleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735].

Conclusions

Smartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.

Comparison of Ring Pulse Oximetry Using Reflective Photoplethysmography and PSG in the Detection of OSA in Chinese Adults: A Pilot Study

OBJECTIVE

A novel ring-worn oximeter (Circul) uses reflective photoplethysmography and automated signal processing to calculate oxygen desaturations. We evaluated the ability of Circul to detect obstructive sleep apnea in Chinese adults.

METHODS

We recruited 207 Chinese Han subjects: 70% males, mean age 48.2±14.7 years, mean BMI 27.6±4.8 kg/m2 and mean AHI 28.6±25.2 events/h. All participants underwent simultaneous polysomnography (PSG) and Circul testing in a sleep laboratory. Oxygen desaturation index (ODI), mean oxygen saturation (MSpO₂), cumulative time at SpO2<90% (CT90), cumulative percentage of sleep time spent with SpO2<90% (CT90/TST) were derived and compared for the Circul and the PSG.

RESULTS

The ODI was 25.3±24.5 events/h using PSG and 22.2±24.5 events/h using Circul (P<0.0001), with an intraclass correlation coefficient (ICC) of 0.884. CT90 and CT90/TST between the two methods were not different; the MSpO₂ level calculated by PSG was slightly lower than Circul, 95.0% (93.0-96.0%) vs 95.3% (93.9-96.6%), P<0.0001. Circul-ODI had a good correlation (r=0.91, p<0.0001) and close agreement with PSG-AHI (Bland-Altman analysis: Mean Difference 6.4, 95% CI -14.8 to 27.5 events/h). Using a threshold of AHI ≥5 events/h, the Circul had 87% sensitivity, 83% specificity, 5.09 positive likelihood ratio (LR+), 86% accuracy, and 0.929 area under the curve (AUC).

CONCLUSION

Circul ring pulse oximetry can detect OSA with reasonable reliability. The Circul system is a reliable and comfortable choice for OSA assessment.

Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5-15; 19% had AHI 15-30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703-0.888], 0.931 [95% CI, 0.772-0.992], and 0.735 [95% CI, 0.589-0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828-0.987], 0.944 [95% CI, 0.727-0.999], and 0.933 [95% CI, 0.779-0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.