Validation of a portable monitoring device for the diagnosis of obstructive sleep apnea: electrocardiogram-based cardiopulmonary coupling

Changes in physiological signal entropy in patients with obstructive sleep apnoea: a systematic review

Background and Objective.Obstructive sleep apnoea (OSA) affects an estimated 936 million people worldwide, yet only 15% receive a definitive diagnosis. Diagnosis of OSA poses challenges due to the dynamic nature of physiological signals such as oxygen saturation (SpO2) and heart rate variability (HRV). Linear analysis methods may not fully capture the irregularities present in these signals. The application of entropy of routine physiological signals offers a promising method to better measure variabilities in dynamic biological data. This review aims to explore entropy changes in physiological signals among individuals with OSA.Approach.Keyword and title searches were performed on Medline, Embase, Scopus, and CINAHL databases. Studies had to analyse physiological signals in OSA using entropy. Quality assessment used the Newcastle-Ottawa Scale. Evidence was qualitatively synthesised, considering entropy signals, entropy type, and time-series length.Main results.Twenty-two studies were included. Multiple physiological signals related to OSA, including SpO2, HRV, and the oxygen desaturation index (ODI), have been investigated using entropy. Results revealed a significant decrease in HRV entropy in those with OSA compared to control groups. Conversely, SpO2and ODI entropy values were increased in OSA. Despite variations in entropy types, time scales, and data extraction devices, studies using receiver operating characteristic curves demonstrated a high discriminative accuracy (>80% AUC) in distinguishing OSA patients from control groups.Significance. This review highlights the potential of SpO2entropy analysis in developing new diagnostic indices for patients with OSA. Further investigation is needed before applying this technique clinically.

Screening of preoperative obstructive sleep apnea by cardiopulmonary coupling and its risk factors in patients with plans to receive surgery under general anesthesia: a cross-sectional study

Objective

Preoperative obstructive sleep apnea (OSA) is supposed to be the abnormally high occurrence of OSA the night before surgery under general anesthesia. This study aimed to evaluate the prevalence preoperative OSA using cardiopulmonary coupling (CPC) and its correlation with imbalance of sympathetic/parasympathetic nervous system.

Methods

A total of 550 patients with plans to receive surgery under general anesthesia were enrolled. All patients were assigned to wear CPC on the night before surgery until the next day. Sleep quality characteristics, heart rate variation parameters, and apnea-hypopnea index were acquired. The diagnosis of pre-existing OSA was not considered in the current study.

Results

According to apnea-hypopnea index, 28.4%, 32.2%, 26.2%, and 13.3% patients were assessed as no, mild, moderate, and severe operative OSA, respectively. Multivariate logistic regression model revealed that higher age [p < 0.001, odds ratio (OR) = 1.043] was independently and positively associated with preoperative OSA; heart rate variation parameters representing the imbalance of sympathetic/parasympathetic nervous system, such as higher low-frequency (p < 0.001, OR = 1.004), higher low-frequency/high-frequency ratio (p = 0.028, OR = 1.738), lower NN20 count divided by the total number of all NN intervals (pNN20; p < 0.001, OR = 0.950), and lower high-frequency (p < 0.001, OR = 0.998), showed independent relationships with a higher probability of preoperative OSA. Higher age (p = 0.005, OR = 1.024), higher very-low-frequency (p < 0.001, OR = 1.001), and higher low-frequency/high-frequency ratio (p = 0.003, OR = 1.655) were associated with a higher probability of moderate-to-severe preoperative OSA, but higher pNN10 (p < 0.001, OR = 0.951) was associated with a lower probability of moderate-to-severe preoperative OSA.

Conclusion

Preoperative OSA is prevalent. Higher age and imbalance of sympathetic/parasympathetic nervous system are independently and positively associated with a higher occurrence of preoperative OSA. CPC screening may promote the management of preoperative OSA.

Sleep disturbance exacerbates atherosclerosis in type 2 diabetes mellitus

Background

Short sleep duration and poor sleep quality are important risk factors for atherosclerosis. The use of smart bracelets that measure sleep parameters, such as sleep stage, can help determine the effect of sleep quality on lower-extremity atherosclerosis in patients with type 2 diabetes.

Objective

To investigate the correlation between sleep disorders and lower-extremity atherosclerosis in patients with type 2 diabetes.

Methods

After admission, all patients were treated with lower-extremity arterial ultrasound and graded as having diabetic lower-extremity vascular lesions according to the results. A smart bracelet was used to obtain the patient sleep data. The correlation between sleep patterns and diabetic lower-extremity atherosclerosis, diabetic foot, and various metabolic indices was verified.

Results

Between August 2021 and April 2022, we screened 100 patients with type 2 diabetes, with 80 completing sleep monitoring. Univariate ordered logistic regression analysis indicated that patients with a sleep score below 76 (OR = 2.707, 95%CI: 1.127-6.488), shallow sleep duration of 5.3 h or more (OR=3.040, 95 CI: 1.005-9.202), wakefulness at night of 2.6 times or more (OR = 4.112, 95%CI: 1.513-11.174), and a deep sleep continuity score below 70 (OR = 4.141, 95%CI: 2.460-615.674) had greater risk of high-grade lower limb atherosclerosis. Multivariate ordinal logistic regression analysis revealed that the risk of high-grade lower limb atherosclerosis was higher in patients with 2.6 or more instances of nighttime wakefulness (OR = 3.975, 95%CI: 1.297-12.182) compared with those with fewer occurrences. The sleep duration curve of patients with different grades of diabetic lower-extremity atherosclerosis was U-shaped. According to the results of the one-way analysis of variance, the higher the deep sleep continuity score, the lower the Wagner scale score for diabetic foot (P < 0.05).

Conclusions

Sleep disorders (long, shallow sleep duration, frequent wakefulness at night, and poor continuity of deep sleep) can worsen lower limb atherosclerosis in patients with type 2 diabetes. This finding can provide a new method for medical professionals to prevent and treat diabetic lower-extremity vascular lesions.

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.

A novel deep domain adaptation method for automated detection of sleep apnea/hypopnea events

Objective.Sleep apnea-hypopnea syndrome (SAHS) is a common sleep-related respiratory disorder that is generally assessed for severity using polysomnography (PSG); however, the diversity of sampling devices and patients makes this not only costly but may also degrade the performance of the algorithms.Approach.This paper proposes a novel deep domain adaptation module which uses a long short-term memory-convolutional neural network embedded with the channel attention mechanism to achieve autonomous extraction of high-quality features. Meanwhile, a domain adaptation module was built to achieve domain-invariant feature extraction for reducing the differences in data distribution caused by different devices and other factors. In addition, during the training process, the algorithm used the last second label as the label of the PSG segment, so that second-by-second evaluation of respiratory events could be achieved.Main results.The algorithm applied the two datasets provided by PhysioNet as the source and target domains. The accuracy, sensitivity and specificity of the algorithm on the source domain were 86.46%, 86.11% and 93.17%, respectively, and on the target domain were 83.63%, 82.52%, 91.62%, respectively. The proposed algorithm showed strong generalization ability and the classification results were comparable to the current advanced methods. Besides, the apnea-hypopnea index values estimated by the proposed algorithm showed a high correlation with the manual scoring values on both domains.Significance.The proposed algorithm can effectively perform SAHS detection and evaluation with certain generalization.

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

Comparison of polysomnography, sleep apnea screening test and cardiopulmonary coupling in the diagnosis of pediatric obstructive sleep apnea syndrome

OBJECTIVE

The study aims to investigate into the correlation between clinical characteristics of pediatric obstructive sleep apnea (OSA) and the results of polysomnography (PSG), sleep apnea screening test (SAST) and cardiopulmonary coupling (CPC) respectively and compare their diagnostic values for pediatric OSA patients.

METHODS

We recruited 239 pediatric OSA patients aged between 2 and 12 from Jan 1, 2017 to Jun 30, 2018. All the patients received PSG, SAST and CPC simultaneously and the results of these three different tests were compared and analyzed together with their clinical features. The relationship between the size of adenoid/tonsil and the severity of OSA was also analyzed.

RESULTS

No statistically significant differences were noted between SAST and PSG in the oxygen desaturation index (ODI₃) and lowest oxygen saturation (LsO₂) respectively. No significant statistical difference was noted in the proportion of rapid eye movement sleep between CPC and PSG. The apnea-hypopnea index (AHI) from CPC was significantly lower than that from PSG. In the severe OSA group, no significant statistical difference was noted in AHI between these two tests. However, AHI from CPC was significantly lower than that from PSG in other groups. No statistically significant difference was noted in AHI and ODI₃ among different groups graded by the size of adenoid or tonsil, suggesting that the size of adenoid/tonsil may not be highly related to the severity of OSA.

CONCLUSION

SAST is an acceptable fast screening tool in the assessments of blood oxygen desaturation and further pediatric OSA screening. CPC is capable to screen severe pediatric OSA, but its results should be interpreted with caution for pediatric patients with non-severe OSA. The size of adenoid/tonsil may not be highly related to the severity of OSA.

Evaluation of a non-contact ultra-wideband bio-radar sleep monitoring device for screening of sleep breathing disease

PURPOSE

Ultra-wideband bio-radar (UWB) is a new non-contact technology that can be used to screen for obstructive sleep apnea (OSA). However, little information is available regarding its reliability. This study aimed to evaluate the effectiveness of UWB and to determine if UWB could provide a novel and reliable method for the primary screening of sleep-related breathing disorders.

METHOD

Subjects with suspected OSA from the sleep center of the First Hospital of the China Medical University were assessed over the period of September 2018 to April 2019 for enrollment in the study. Three detection methods were simultaneously used, including the STOP-Bang questionnaire (SBQ), UWB, and standard polysomnography (PSG). The data were analyzed using a fourfold table, receiver operating characteristic curves, Spearman rank correlation coefficients, Bland-Altman plots, and epoch-by-epoch analysis.

RESULT

Of 67 patients, 56 were men, mean age was 43 ± 11 years, mean body mass index was 27.8 ± 4.8 kg/m², and mean SBQ score was 4.8 ± 1.6. The apnea-hypopnea index (AHI) (r = 0.82, p < 0.01) and minimum arterial oxygen saturation (r = 0.80, p < 0.01) of the UWB were positively correlated with those obtained from the PSG. UWB performed better than SBQ, as indicated by the larger area under the curve (0.85 vs. 0.632). The sensitivity and specificity of the UWB-AHI were good (100%, 70%, respectively).

CONCLUSIONS

UWB performs well in the screening of OSA and can provide reliable outcomes for the screening of OSA at the primary level.

Toward standardizing the clinical testing protocols of point-of-care devices for obstructive sleep apnea diagnosis

PURPOSE

In recent years, point-of-care (POC) devices, especially smart wearables, have been introduced to provide a cost-effective, comfortable, and accessible alternative to polysomnography (PSG)-the current gold standard-for the monitoring, screening, and diagnosis of obstructive sleep apnea (OSA). Thorough validation and human subject testing are essential steps in the translation of these device technologies to the market. However, every device development group tests their device in their own way. No standard guidelines exist for assessing the performance of these POC devices. The purpose of this paper is to critically distill the key aspects of the various protocols reported in the literature and present a protocol that unifies the best practices for testing wearable and other POC devices for OSA.

METHODS

A limited review and graphical descriptive analytics of literature-including journal articles, web sources, and clinical manuscripts by authoritative agencies in sleep medicine-are performed to glean the testing and validation methods employed for POC devices, specifically for OSA.

RESULTS

The analysis suggests that the extent of heterogeneity of the demographics, the performance metrics, subject survey, hypotheses, and statistical analyses need to be carefully considered in a systematic protocol for testing POC devices for OSA.

CONCLUSION

We provide a systematic method and list specific recommendations to extensively assess various performance criteria for human subject testing of POC devices. A rating scale of 1-3 is provided to encourage studies to put a focus on addressing the key elements of a testing protocol.