Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer

Indoor air pollution impacts cardiovascular autonomic control during sleep and the inflammatory profile

The present study explores the modifications of cardiovascular autonomic control (CAC) during wake and sleep time and the systemic inflammatory profile associated with exposure to indoor air pollution (IAP) in a cohort of healthy subjects. Twenty healthy volunteers were enrolled. Indoor levels of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and volatile organic compounds (VOCs) were monitored using a portable detector for 7 days. Together, a 7-day monitoring was performed through a wireless patch that continuously recorded electrocardiogram, respiratory activity and actigraphy. Indexes of CAC during wake and sleep time were derived from the biosignals: heart rate and low-frequency to high-frequency ratio (LF/HF), index of sympathovagal balance with higher values corresponding to a predominance of the sympathetic branch. Cyclic variation of heart rate index (CVHRI events/hour) during sleep, a proxy for the evaluation of sleep apnea, was assessed for each night. After the monitoring, blood samples were collected to assess the inflammatory profile. Regression and correlation analyses were performed. A positive association between VOC exposure and the CVHRI (Δ% = +0.2% for 1 μg/m3 VOCs, p = 0.008) was found. The CVHRI was also positively associated with LF/HF during sleep, thus higher CVHRI values corresponded to a shift of the sympathovagal balance towards a sympathetic predominance (r = 0.52; p = 0.018). NO2 exposure was positively associated with both the pro-inflammatory biomarker TREM-1 and the anti-inflammatory biomarker IL-10 (Δ% = +1.2% and Δ% = +2.4%, for 1 μg/m3 NO2; p = 0.005 and p = 0.022, respectively). The study highlights a possible causal relationship between IAP exposure and higher risk of sleep apnea events, associated with impaired CAC during sleep, and a pro-inflammatory state counterbalanced by an increased anti-inflammatory response in healthy subjects. This process may be disrupted in vulnerable populations, leading to a harmful chronic pro-inflammatory profile. Thus, IAP may emerge as a critical and often neglected risk factor for the public health that can be addressed through targeted preventive interventions.

Study on OSA screening and influencing factors in community-based elderly hypertensive patients based on single-lead wearable ECG devices

PURPOSE

Assessing whether single-lead ECG can be effectively and relatively inexpensively used in large-scale OSA screening, and identifying factors influencing moderate-to-severe OSA among elderly hypertensive patients without atypical symptoms in primary care.

METHODS

The study gathered data from 15 medical institutions in Ningxia between January and December 2022 using cloud platforms. The dataset included basic information and 72-h ECG monitoring for 2573 hypertensive patients over 65. OSA screening was conducted using the single-lead wearable ECG devices based on the ACAT algorithm. A multivariable logistic regression identified the main factors affecting OSA severity in these patients, and the AUC was used to assess the model's predictive accuracy.

RESULTS

The study found an OSA detection rate of 87.10%, with 55.42% being moderate to severe cases. Key risk factors associated with developing moderate-to-severe OSA included cardiac irregularities like supraventricular extrasystole and atrioventricular block, male gender, lifestyle factors like alcohol consumption and smoking, and health indicators such as SDNN ≤ 100 ms, abnormal LF/HF ratio, BMI, and age. The model's accuracy for predicting OSA, indicated by a ROAUC of 0.625, was moderate. Factors like gender, tea consumption, stroke history, and ventricular tachycardia were also independently linked to OSA severity.

CONCLUSION

This study combines single-lead wearable ECG devices with the ACAT algorithm for OSA screening in Ningxia, China. Initial screening identified 87.10% of participants as having OSA, with 55.42% being moderate to severe cases. This suggests a convenient, low-cost, and repeatable ECG-based method for OSA screening, potentially improving early detection and management of OSA by identifying potential risk factors.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity

STUDY OBJECTIVES

The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards.

METHODS

We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared.

RESULTS

A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%.

CONCLUSIONS

The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.

CITATION

Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

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.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

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.

Upper Airway Surgery or Weight Control? Modified Drug-Induced Sleep Endoscopy for Obstructive Sleep Apnea

OBJECTIVE

To identify the value of head rotation in the supine position and oral appliance (OA) use in drug-induced sleep endoscopy (DISE).

STUDY DESIGN

Eighty-three sleep apnea adults undergoing target-controlled infusion-DISE (TCI-DISE) were recruited from a tertiary academic medical center.

SETTING

During DISE, 4 positions were utilized: supine position (position 1), head rotation (position 2), mandibular advancement using an OA (position 3), and head rotation with an OA (position 4).

METHODS

Polysomnography (PSG) data and anthropometric variables during DISE were analyzed.

RESULTS

Eighty-three patients (65 men and 18 women; mean [standard deviation, SD], 48.5 [11.0] years) who underwent PSG and TCI-DISE were included. The mean (SD) apnea-hypopnea index (AHI) was 35.5 (22.4) events/h. Twenty-three patients had persistent complete concentric velopharyngeal collapse in the supine position, even with concurrent head rotation and OA (position 4). Their mean (SD) AHI was 54.7 (24.6) events/h, significantly higher than that of the 60 patients without such collapse in position 4 (p < .001). Their mean (SD) body mass index (BMI) was 29.0 (4.1) kg/m2 , also significantly higher (p = .005). After adjustment for age, BMI, tonsil size, and tongue position, the degree of velum and tongue base obstruction was significantly associated with sleep apnea severity in positions 2, 3, and 4.

CONCLUSION

We showed the feasibility, safety, and usefulness of using simple edge-to-edge, reusable OA in DISE. Patients who are not responsive to head rotation and OA during TCI-DISE may need upper airway surgery and/or weight control.

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.

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection

Introduction

The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome.

Methods

In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments.

Results and Discussion

The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.

At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch

Obstructive sleep apnea (OSA) affects more than 900 million adults globally and can create serious health complications when untreated; however, 80% of cases remain undiagnosed. Critically, current diagnostic techniques are fundamentally limited by low throughputs and high failure rates. Here, we report a wireless, fully integrated, soft patch with skin-like mechanics optimized through analytical and computational studies to capture seismocardiograms, electrocardiograms, and photoplethysmograms from the sternum, allowing clinicians to investigate the cardiovascular response to OSA during home sleep tests. In preliminary trials with symptomatic and control subjects, the soft device demonstrated excellent ability to detect blood-oxygen saturation, respiratory effort, respiration rate, heart rate, cardiac pre-ejection period and ejection timing, aortic opening mechanics, heart rate variability, and sleep staging. Last, machine learning is used to autodetect apneas and hypopneas with 100% sensitivity and 95% precision in preliminary at-home trials with symptomatic patients, compared to data scored by professionally certified sleep clinicians.

Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch

Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices—namely, the apnoea–hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson’s correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients’ OSA severity. The high supine percentage of sleep may cause OSA indices’ overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.

Assessment of autonomic function by long-term heart rate variability: beyond the classical framework of LF and HF measurements

In the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate.

Night-to-night variability of sleep apnea detected by cyclic variation of heart rate during long-term continuous ECG monitoring

Background

Sleep apnea is common in patients with cardiovascular disease and is a factor that worsens prognosis. Holter 24‐h ECG screening for sleep apnea is beneficial in the care of these patients, but due to high night‐to‐night variability of sleep apnea, it can lead to misdiagnosis and misclassification of disease severity.

Methods

To investigate the long‐term dynamic behavior of sleep apnea, seven‐day ECGs recorded with a patch ECG recorder in 120 patients were analyzed for the cyclic variation of heart rate (CVHR) during sleep periods as determined by a built‐in three‐axis accelerometer.

Results

The frequency of CVHR (Fcv) showed considerable night‐to‐night variability (coefficient of variance, 66 ± 35%), which was consistent with the night‐to‐night variability in apnea‐hypopnea index and oxygen desaturation index reported in earlier studies. In patients with presumed moderate‐to‐severe sleep apnea (Fcv > 15 cph at least one night), it was missed on 62% of nights, and on at least one night in 88% of patients. The CV of Fcv was negatively correlated with the average of Fcv, suggesting that patients with mild sleep apnea show greater night‐to‐night variability and would benefit from long‐term assessment. The average Fcv was higher in the supine position, but the night‐to‐night variability was not explained by the night‐to‐night variability of time spent in the supine position.

Conclusions

CVHR analysis of long‐term ambulatory ECG recordings is useful for improving the reliability of screening for sleep apnea without placing an extra burden on patients with cardiovascular disease and their care.

Respiratory Event Detection during Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohens kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohens kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

The novel approach of temporal dependency complexity analysis of heart rate variability in obstructive sleep apnea

Obstructive sleep apnea (OSA) is a serious sleep disorder, which leads to changes in autonomic nerve function and increases the risk of cardiovascular disease. Heart rate variability (HRV) has been widely used as a non-invasive method for assessing the autonomic nervous system (ANS). We proposed the two-dimensional sample entropy of the coarse-grained Gramian angular summation field image (CgSampEn_2D) index. It is a new index for HRV analysis based on the temporal dependency complexity. In this study, we used 60 electrocardiogram (ECG) records from the Apnea-ECG database of PhysioNet (20 healthy records and 40 OSA records). These records were divided into 5-min segments. Compared with the classical indices low-to-high frequency power ratio (LF/HF) and sample entropy (SampEn), CgSampEn_2D utilizes the correlation information between different time intervals in the RR sequences and preserves the temporal dependency of the RR sequences, which improves the OSA detection performance significantly. The OSA screening accuracy of CgSampEn_2D (93.3%) is higher than that of LF/HF (80.0%) and SampEn (73.3%). Additionally, CgSampEn_2D has a significant association with the apnea-hypopnea index (AHI) (R = -0.740, p = 0). CgSampEn_2D reflects the complexity of the OSA autonomic nerve more comprehensively and provides a novel idea for the screening of OSA disease.

Pacemaker guided screening for severe sleep apnea, a possible option for patients with atrial fibrillation: A systematic review and meta-analysis

INTRODUCTION

Obstructive sleep apnea is often underdiagnosed in atrial fibrillation (AF) patients although it is an important risk factor. A systematic review and meta-analysis was performed to assess which techniques cardiac implantable electronic devices (CIED) and Holter monitors use to screen for sleep apnea (SA), and to evaluate if these are suitable for AF patients from a diagnostic accuracy perspective.

METHODS

The search was conducted in accordance with the PRISMA-guidelines. PICO was defined as (P) patients with AF, (I) Holter monitors or CIED suitable for screening for SA, (C) overnight polysomnography (PSG), (O) positive screening with subsequent positive polysomnographic diagnosis of SA. Optimal index test cut-off points corresponding to reference test cut-off for severe SA (PSG-AHI ≥ 30) were compared. Meta-analysis was conducted for the diagnostic odds ratio (DOR), with forest plot and ROC-curve for summary DOR.

RESULTS

A total of five prospective cohort studies (n = 192) were included in the systematic review of which four studies (n = 132) were included in the meta-analysis. All included studies use transthoracic impedance measurement as a screening parameter. No studies evaluating Holter monitors were included. The population consisted of patients indicated for pacemaker implantation. The summary DOR was 27.14 (8.83; 83.37), AUC was 0.8689 (0.6872; 0.9456) and Q* was 0.8390 (0.7482; 0.9013).

CONCLUSION

At optimal pacemaker-cut-off, pacemaker-guided screening for severe SA in patients with AF can be an effective triage tool for clinical practice. Further studies with larger sample sizes are needed to strengthen the evidence for this conclusion.