Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies

A review of automatic sleep stage classification using machine learning algorithms based on heart rate variability

Over the past few decades, the use of heart rate variability (HRV) has expanded significantly due to its ease of collection, affordability, and its clinical relevance to psychophysiological processes and psychopathological disorders. This study aims to demonstrate the effectiveness of an artificial intelligence approach based on HRV signals for automatic sleep stage classification. This review examines machine learning algorithms for HRV-based sleep stage classification over the past 15 years. It also compares the HRV features extracted, the classification algorithms used, and the evaluation parameters employed. Existing studies indicate that with advances in technology, machine learning algorithms utilizing HRV features for sleep staging achieve high accuracy, sensitivity, and specificity. The use of HRV for sleep analysis via machine learning algorithms is an active area of research with broad application potential. As technology progresses and data accumulation increases, this approach is expected to offer more accurate and personalized solutions for sleep medicine and health management.

The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea

Introduction

Obstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. Heart rate variability (HRV) is a simple and non-invasive approach used as a probe to evaluate cardiac autonomic modulation, with a variety of newly developed indices lacking studies with OSA patients.

Objectives

We aimed to evaluate numerous HRV indices, derived from linear but mainly nonlinear indices, combined or not with oxygen saturation indices, for detecting the presence and severity of OSA using machine learning models.

Methods

ECG waveforms were collected from 291 PSG recordings to calculate 34 HRV indices. Minimum oxygen saturation value during sleep (SatMin), the percentage of total sleep time the patient spent with oxygen saturation below 90% (T90), and patient anthropometric data were also considered as inputs to the models. The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. Since the OSA severity groups were unbalanced, we used the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority classes.

Results

Multiclass models achieved a mean area under the ROC curve (AUROC) of 0.92 and 0.86 in classifying normal individuals and severe OSA patients, respectively, when using all attributes. When the groups were dichotomized into normal-to-mild OSA vs. moderate-to-severe OSA, an AUROC of 0.83 was obtained. As revealed by RF, the importance of features indicates that all feature modalities (HRV, SpO2, and anthropometric variables) contribute to the top 10 ranks.

Conclusion

The present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.

Relationship between sleep-breathing events induced nocturnal blood pressure surge and sympathetic nervous activity in patients with obstructive sleep apnea

OBJECTIVE

Nocturnal blood pressure (BP) surge is a characteristic phenomenon in patients with obstructive sleep apnea (OSA) associated with sympathetic nerve overactivity. This study aimed to explore the relationship between the sleep-breathing events induced nocturnal BP surge and sympathetic nerve activity.

METHODS

A total of 85 patients with moderate-to-serve OSA and 44 controls were included in the study between April 2022 and October 2023 based on the inclusion and exclusion criteria. Full-night BP and heart rate variability (HRV) were monitored continuously and synchronized with polysomnography (PSG). The average of nocturnal BPs was taken as the asleep BP and the average of the highest BPs induced by all sleep-breathing events as the asleep peak BP. Nocturnal short-term BP variability (BPV) was calculated as follows: event-related systolic BP elevation (ΔSBP) as the gap between the peak and the lowest value of post-apneic SBP, BP index as the number of ΔSBP ≥ 12 mm Hg within 30 s/h, and the percentage of BP fluctuation induced by sleep-breathing events (PBPF) as the ratio of BP index and apnea-hypopnea index. Patients with OSA were divided into two subgroups (high- and low-BP surge groups) according to the median PBPF. The sympathetic nerve activity was reflected by plasma norepinephrine (NE) level and HRV. The PSG and BP parameters were compared among three groups, and the correlation between nocturnal short-term BPV and sympathetic nerve activity was analyzed.

RESULTS

Patients with OSA were fatter and suffered from dyslipidemia and sympathetic nerve overactivity compared to controls. The high-BP surge group displayed higher sympathetic nerve activity and more severe hypoxia compared with the low-BP surge group. The Pearson correlation analysis showed a positive correlation of the higher nocturnal short-term BPV with increased sympathetic nerve activity (all P < 0.05). After excluding confounding factors, such as age, body mass index, and smoking history, the multiple linear regression revealed a positive correlation of the LF/HF (ratio of low-frequency to high-frequency power, indicating the activity of sympathetic nerve activity) with the BP index (β = 7.337, P < 0.001), ΔSBP (β = 2.797, P < 0.001), and PBPF (β = 9.036, P < 0.001). The plasma NE level also had a positive correlation with the BP index (β = 3.939, P = 0.022) and PBPF (β = 8.752, P < 0.001).

CONCLUSION

The sleep-breathing events induced nocturnal BP surge was positively correlated with sympathetic nerve activity in patients with moderate-to-serve OSA.

Association of electrocardiogram features with risk of obstructed sleep apnea: a population-based cohort study

BACKGROUND

There is limited investigation on the longitudinal association between common electrocardiogram (ECG) features and the incidence of obstructive sleep apnea (OSA). This study aimed to examine the association of common ECG features with the incidence of OSA in a prospective cohort.

METHODS

2,563 participants aged 60 years or more were selected from the baseline survey of the Guangzhou Heart Study. OSA was evaluated by the Berlin Questionnaire. Eight electrocardiogram features including PR interval, QRS duration, QT interval, QTc interval, heart rate, P-wave, R-wave, and T-wave were extracted from 24-hour single-lead Holter. Relative risk (RR) with 95% confidence interval (CI) was estimated using the multivariate logistic regression model. A receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of ECG features.

RESULTS

397 (15.5%) participants were divided into the OSA group and 2,166 (84.5%) into the OSA non-group. When comparing the highest with the lowest quartiles, heart rate was related to a 30% reduced risk of OSA (RR: 0.70, 95%CI: 0.51-0.97) after adjustment for possible confounders. Participants with prolonged PR interval were more likely to be at risk of OSA (RR: 2.68, 95%CI: 1.02-6.55). No significant association was found between the other six ECG features and OSA risk. Area under ROC curve was 0.676 (95% CI: 0.648-0.704), 0.676 (95%CI: 0.648-0.704), and 0.678 (95%CI: 0.651-0.706) for heart rate, PR interval, and their combination, respectively.

CONCLUSIONS

The results suggest that heart rate and PR interval are related to OSA incidence. Future studies should be carried out in different populations, and consider the use of portable monitors together with scales to comprehensively determine OSA, and comprehensively elucidate the relationship of various ECG features and their changes with OSA occurrence.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Allergic rhinitis may attenuate the sympathovagal imbalances in patients with severe obstructive sleep apnea: pilot study using a heart rate variability analysis

PURPOSE

The effect of allergic rhinitis (AR) on autonomic nervous system in patients with obstructive sleep apnea (OSA) remains unclear. We utilized heart rate variability (HRV) analysis to assess cardiac autonomic activity in patients with OSA, comparing those with and without allergic rhinitis (AR).

METHODS

We enrolled 182 patients who visited our sleep clinic complaining of habitual snoring or apnea during sleep. All patients underwent full-night polysomnography (PSG) and multiple allergen simultaneous tests. We calculated the HRV extracted from the electrocardiography of the PSG. Participants were divided into a normal group and an AR group, and HRV indices were compared according to OSA severity in each group.

RESULTS

The low-frequency (LF) to high-frequency (HF) ratio (LF/HF; r = 0.336, p < 0.001), LF normalised unit (LFnu; r = 0.345, p < 0.001), and HFnu (r = -0.345, p < 0.001) were significantly correlated with the apnea-hypopnea index. The HRV index comparison between non-severe and severe OSA in the normal group showed significant differences in LFnu (64.7 ± 12.5 in non-severe and 72.4 ± 11.7 in severe, p < 0.001), LF/HF (2.3 ± 1.6 in non-severe and 3.3 ± 2.0 in severe, p = 0.002), and HFnu (35.3 ± 12.5 in non-severe and 27.6 ± 11.7 in severe, p < 0.001). However, in the AR group, LFnu (p = 0.648), LF/HF (p = 0.441), and HFnu (p = 0.648) were comparable between non-severe and severe OSA.

CONCLUSION

Considering that LFnu, HFnu, and LF/HF represent sympathetic activity, parasympathetic activity, and sympathovagal balance, respectively, AR may attenuate the sympathetic predominance and sympathovagal imbalance associated with cardiovascular morbidity in severe OSA.

IPCT-Net: Parallel information bottleneck modality fusion network for obstructive sleep apnea diagnosis

Obstructive sleep apnea (OSA) is a common sleep breathing disorder and timely diagnosis helps to avoid the serious medical expenses caused by related complications. Existing deep learning (DL)-based methods primarily focus on single-modal models, which cannot fully mine task-related representations. This paper develops a modality fusion representation enhancement (MFRE) framework adaptable to flexible modality fusion types with the objective of improving OSA diagnostic performance, and providing quantitative evidence for clinical diagnostic modality selection. The proposed parallel information bottleneck modality fusion network (IPCT-Net) can extract local-global multi-view representations and eliminate redundant information in modality fusion representations through branch sharing mechanisms. We utilize large-scale real-world home sleep apnea test (HSAT) multimodal data to comprehensively evaluate relevant modality fusion types. Extensive experiments demonstrate that the proposed method significantly outperforms existing methods in terms of participant numbers and OSA diagnostic performance. The proposed MFRE framework delves into modality fusion in OSA diagnosis and contributes to enhancing the screening performance of artificial intelligence (AI)-assisted diagnosis for OSA.

Effects of Auto-Titrating Mandibular Advancement Device on Autonomic Nervous System in Obstructive Sleep Apnea

Background/Objectives: One prior study revealed that a newly developed auto-titrating mandibular advancement device (AMAD) could potentially enhance polysomnographic outcomes in individuals with obstructive sleep apnea (OSA). However, evidence regarding its impact on autonomic nervous system dysregulation in OSA remains limited. In this study, we aimed to compare the effects of conventional mandibular advancement devices (MADs) and AMDA on autonomic function. Methods: We retrospectively reviewed data from patients who visited a sleep center with complaints of snoring and sleep apnea (30 and 15 patients in the conventional MAD and AMAD groups, respectively). We assessed heart rate variability (HRV) frequency-domain metrics such as total power (TP), very low frequency (VLF), low frequency (LF), and high frequency (HF) using ultra-short-term and short-term modalities, assessing sympathetic and parasympathetic activity changes across treatment groups. Results: Conventional MAD treatment was associated with reductions in LF and LF/HF ratios, whereas AMAD treatment was linked to decreases in TP, VLF, LF, and LF/HF ratios. Notably, in patients with moderate OSA, LF values were significantly lower in the AMAD group than in the conventional MAD group. Conclusions: These findings suggest that both devices could reduce sympathetic over-activity in patients with OSA, with AMAD demonstrating greater efficacy, particularly in those with moderate OSA.

The bidirectional relationship between obstructive sleep apnea and atrial fibrillation: Pathophysiology, diagnostic challenges, and strategies - A narrative review

Atrial fibrillation (AF), is an irregular heart rhythm disorder that increases the risk of stroke, heart failure, and death. Obstructive sleep apnea is typified by intermittent airway blockages which results in low oxygen levels and disrupted sleep. These two conditions often coexist, with each worsening the other. Understanding this connection is critical to improve diagnosis and treatment. The relationship between atrial fibrillation and obstructive sleep apnea appears bidirectional. Obstructive sleep apnea increases the risk of atrial fibrillation through various mechanisms which are arrhythmogenic. Conversely, patients with atrial fibrillation are more likely to have undiagnosed obstructive sleep apnea, complicating their treatment. Screening modalities for obstructive sleep apnea are often inadequate. Polysomnography remains the most reliable tool but is costly and not practical for routine screening of all patients which limits early diagnosis and management. Continuous positive airway pressure (CPAP) therapy is the primary treatment for obstructive sleep apnea and can reduce atrial fibrillation recurrence by decreasing oxygen deprivation and sympathetic activity. However, adherence to continuous positive airway pressure is often low due to patient discomfort. Alternative therapies, such as mandibular advancement devices and hypoglossal nerve stimulation, offer promising options for patients who cannot tolerate continuous positive airway pressure. The interplay between atrial fibrillation and obstructive sleep apnea requires an integrated approach to diagnosis and treatment. Improving screening tools, enhancing treatment adherence, and evaluating alternative therapies are critical steps to reducing the impact of these conditions and improving patient outcomes.

Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm

PURPOSE

More than 80% of patients with moderate to severe obstructive sleep apnea (OSA) are still not diagnosed timely. The prediction model based on random forest (RF) algorithm was established by using heart rate variability (HRV), clinical and demographic features so as to screen for the patients with high risk of moderate and severe obstructive sleep apnea.

METHODS

The sleep monitoring data of 798 patients were randomly divided into training set (n = 558) and test set (n = 240) in 7:3 proportion. Grid search was applied to determine the best parameters of the RF model. 10-fold cross validation was utilized to evaluate the predictive performance of the RF model, which was then compared to the performance of the Logistic regression model.

RESULTS

Among the 798 patients, 638 were males and 160 were females, with the average age of 43.51 years old and the mean body mass index (BMI) of 25.92 kg/m2. The sensitivity, specificity, accuracy, F1 score and the area under receiver operating characteristic curve for RF model and Logistic regression model were 94.68% vs. 73.94%; 73.08% vs. 86.54%; 90.00% vs. 76.67%; 0.94 vs. 0.83 and 0.83 vs. 0.80 respectively.

CONCLUSIONS

The RF prediction model can effectively distinguish patients with moderate to severe OSA, which is expected to carry out in a large-scale population in order to screening for high-risk patients, and helps to evaluate the effect of OSA treatment continuously.

Sleep-Disordered Breathing in Patients with Chronic Heart Failure and Its Implications on Real-Time Hemodynamic Regulation, Baroreceptor Reflex Sensitivity, and Survival

Background: Impairment in autonomic activity is a prognostic marker in patients with heart failure (HF), and its involvement has been suggested in cardiovascular complications of obstructive sleep apnea syndrome (OSAS) and Cheyne-Stokes respiration (CSR). This prospective observational study aims to investigate the implications of sleep-disordered breathing (SDB) on hemodynamic regulation and autonomic activity in chronic HF patients. Methods: Chronic HF patients, providing confirmation of reduced ejection fraction (≤35%), underwent polysomnography, real-time hemodynamic, heart rate variability (HRV), and baroreceptor reflex sensitivity (BRS) assessments using the Task Force Monitor. BRS was assessed using the sequencing method during resting conditions and stress testing. Results: Our study population (n = 58) was predominantly male (41 vs. 17), with a median age of 61 (±11) yrs and a median BMI of 30 (±5) kg/m2. Patients diagnosed with CSR were 13.8% (8/58) and 50.0% (29/58) with OSAS. No differences in the real-time assessment of hemodynamic regulation, heart rate variability, or baroreceptor reflex function were found between patients with OSAS, CSR, and patients without SDB. A subgroup analysis of BRS and HRV in patients with severe SDB (AHI > 30/h) and without SDB (AHI < 5) revealed numerically reduced BRS and increased LF/HF-RRI values under resting conditions, as well as during mental testing in patients with severe SDB. Patients with moderate-to-severe SDB had a shorter overall survival, which was, however, dependent upon age. Conclusions: Chronic HF patients with severe SDB may exhibit lower baroreceptor function and impaired cardiovascular autonomic function in comparison with HF patients without SDB.

Enhanced machine learning approaches for OSA patient screening: model development and validation study

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Association between obstructive sleep apnea and arrhythmia and heart rate variability among hypertensive patients

BACKGROUND

The relationship between obstructive sleep apnea (OSA) and the occurrence of arrhythmias and heart rate variability (HRV) in hypertensive patients is not elucidated. Our study investigates the association between OSA, arrhythmias, and HRV in hypertensive patients.

METHODS

We conducted a cross-sectional analysis involving hypertensive patients divided based on their apnea-hypopnea index (AHI) into two groups: the AHI ≤ 15 and the AHI > 15. All participants underwent polysomnography (PSG), 24-hour dynamic electrocardiography (DCG), cardiac Doppler ultrasound, and other relevant evaluations.

RESULTS

The AHI > 15 group showed a significantly higher prevalence of frequent atrial premature beats and atrial tachycardia (P = 0.030 and P = 0.035, respectively) than the AHI ≤ 15 group. Time-domain analysis indicated that the standard deviation of normal-to-normal R-R intervals (SDNN) and the standard deviation of every 5-minute normal-to-normal R-R intervals (SDANN) were significantly higher in the AHI > 15 group (P = 0.020 and P = 0.033, respectively). Frequency domain analysis revealed that the low-frequency (LF), high-frequency (HF) components, and the LF/HF ratio were also significantly elevated in the AHI > 15 group (P < 0.001, P = 0.031, and P = 0.028, respectively). Furthermore, left atrial diameter (LAD) was significantly larger in the AHI > 15 group (P < 0.001). Both univariate and multivariable linear regression analyses confirmed a significant association between PSG-derived independent variables and the dependent HRV parameters SDNN, LF, and LF/HF ratio (F = 8.929, P < 0.001; F = 14.832, P < 0.001; F = 5.917, P = 0.016, respectively).

CONCLUSIONS

Hypertensive patients with AHI > 15 are at an increased risk for atrial arrhythmias and left atrial dilation, with HRV significantly correlating with OSA severity.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets

OBJECTIVE

Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets.

METHODS

We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database.

RESULTS

Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels.

CONCLUSIONS

Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males

OBJECTIVES

Heart rate variability (HRV) is becoming more prevalent as a measurable parameter in wearable sleep-monitoring devices, which are simple and effective instruments for illness evaluation. Currently, most studies on investigating OSA severity and HRV have measured heart rates during wakefulness or sleep. Therefore, the objective of this study was to investigate the circadian rhythm of HRV in male patients with OSA and its value for the estimation of OSA severity using group-based trajectory modeling.

METHODS

Patients with complaints of snoring were enrolled from the Sleep Center of Shandong Qianfoshan Hospital. Patients were divided into 3 groups according to apnea hypopnea index (AHI in events/h), as follows: (<15, 15≤AHI<30, and ≥30). HRV parameters were calculated using 24 h Holter monitoring, which included time-domain and frequency-domain indices. Circadian differences in the standard deviation of normal to normal (SDNN) were evaluated for OSA severity using analysis of variance, trajectory analysis, and multinomial logistic regression.

RESULTS

A total of 228 patients were enrolled, 47 with mild OSA, 48 moderate, and 133 severe. Patients with severe OSA exhibited reduced triangular index and higher very low frequency than those in the other groups. Circadian HRV showed that nocturnal SDNN was considerably higher than daytime SDNN in patients with severe OSA. The difference among the OSA groups was significant at 23, 24, 2, and 3 o'clock sharp between the severe and moderate OSA groups (all P<0.05). The heterogeneity of circadian HRV trajectories in OSA was strongly associated with OSA severity, including sleep structure and hypoxia-related parameters. Among the low-to-low, low-to-high, high-to-low, and high-to-high groups, OSA severity in the low-to-high group was the most severe, especially compared with the low-to-low and high-to-low SDNN groups, respectively.

CONCLUSIONS

Circadian HRV in patients with OSA emerged as low daytime and high nocturnal in SDNN, particularly in men with severe OSA. The heterogeneity of circadian HRV revealed that trajectories with low daytime and significantly high nighttime were more strongly associated with severe OSA. Thus, circadian HRV trajectories may be useful to identify the severity of OSA.

In-Laboratory Polysomnography Worsens Obstructive Sleep Apnea by Changing Body Position Compared to Home Testing

(1) Background: Home sleep apnea testing, known as polysomnography type 3 (PSG3), underestimates respiratory events in comparison with in-laboratory polysomnography type 1 (PSG1). Without head electrodes for scoring sleep and arousal, in a home environment, patients feel unfettered and move their bodies more naturally. Adopting a natural position may decrease obstructive sleep apnea (OSA) severity in PSG3, independently of missing hypopneas associated with arousals. (2) Methods: Patients with suspected OSA performed PSG1 and PSG3 in a randomized sequence. We performed an additional analysis, called reduced polysomnography, in which we blindly reassessed all PSG1 tests to remove electroencephalographic electrodes, electrooculogram, and surface electromyography data to estimate the impact of not scoring sleep and arousal-based hypopneas on the test results. A difference of 15 or more in the apnea-hypopnea index (AHI) between tests was deemed clinically relevant. We compared the group of patients with and without clinically relevant differences between lab and home tests (3) Results: As expected, by not scoring sleep, there was a decrease in OSA severity in the lab test, similar to the home test results. The group of patients with clinically relevant differences between lab and home tests presented more severe OSA in the lab compared to the other group (mean AHI, 42.5 vs. 20.2 events/h, p = 0.002), and this difference disappeared in the home test. There was no difference between groups in the shift of OSA severity by abolishing sleep scoring in the lab. However, by comparing lab and home tests, there were greater variations in supine AHI and time spent in the supine position in the group with a clinically relevant difference, either with or without scoring sleep, showing an impact of the site of the test on body position during sleep. These variations presented as a marked increase or decrease in supine outcomes according to the site of the test, with no particular trend. (4) Conclusions: In-lab polysomnography may artificially increase OSA severity in a subset of patients by inducing marked changes in body position compared to home tests. The location of the sleep test seems to interfere with the evaluation of patients with more severe OSA.

Effect of mandibular advancement splint therapy on cardiac autonomic function in obstructive sleep apnoea

PURPOSE

This study aimed to evaluate the effect of mandibular advancement splint (MAS) therapy on cardiac autonomic function in patients with obstructive sleep apnoea (OSA) using heart rate variability (HRV) analysis.

METHODS

Electrocardiograms (ECG) derived from polysomnograms (PSG) of three prospective studies were used to study HRV of patients with OSA before and after MAS treatment. HRV parameters were averaged across the entire ECG signal during N2 sleep using 2-min epochs shifted by 30 s. Paired t-tests were used to compare PSG and HRV measures before and after treatment, and the percent change in HRV measures was regressed on the percent change in apnoea-hypopnea index (AHI).

RESULTS

In 101 patients with OSA, 72% were Caucasian, 54% men, the mean age was 56 ± 11 years, BMI 29.8 ± 5.3 kg/m2, and treatment duration was 4.0 ± 3.2 months. After MAS therapy, there was a significant reduction in OSA severity (AHI, - 18 ± 16 events per hour, p < 0.001) and trends towards increased low-frequency to high-frequency ratio, low-frequency power, and reduced high-frequency power (LF:HF, - 0.4 ± 1.5, p = 0.01; LF, - 3 ± 16 nu, p = 0.02, HF, 3.5 ± 13.7 nu, p = 0.01). Change in NN intervals correlated with the change in AHI (β(SE) =  - 2.21 (0.01), t =  - 2.85, p = 0.005). No significant changes were observed in the time-domain HRV markers with MAS treatment.

CONCLUSION

The study findings suggest that successful MAS treatment correlates with changes in HRV, specifically the lengthening of NN intervals, a marker for improved cardiac autonomic adaptability.

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

Obstructive sleep apnea is a highly prevalent sleep-related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter-individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea-Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea-Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea-Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.

An Integrative Literature Review of Heart Rate Variability Measures to Determine Autonomic Nervous System Responsiveness Using Pharmacological Manipulation

BACKGROUND

Heart rate variability (HRV) is defined as the difference in the timing of intervals between successive heartbeats and is used as a surrogate measure to the responsiveness of the autonomic nervous system. A review and synthesis of HRV as an indicator of autonomic nervous system responsiveness to pharmacologic stimulation/blockade of sympathetic and/or parasympathetic nervous system branches have not been completed.

PURPOSE

The aim of this integrative review is to synthesize research examining pharmacological modulation of the autonomic nervous system and the response of time domain, frequency domain, and nonlinear measures of HRV.

CONCLUSIONS

Sympathetic nervous system blockade resulted in a consistent decrease in the standard deviation of normal-normal interval metric across studies. Stimulation of the parasympathetic nervous system was associated with an increase in several time, frequency, and nonlinear HRV indices, whereas blockade of the parasympathetic nervous system led to a decrease in similar indices.

CLINICAL IMPLICATIONS

Recommendations to improve the reproducibility of future HRV research are provided for standardization of recording, analysis, and metric decisions and more thorough reporting of HRV indices in published studies. Alterations in autonomic nervous system input to the cardiovascular system are associated with an increased risk for adverse patient outcomes and increased mortality; therefore, understanding the influence of pharmacologic autonomic nervous system modulation on HRV indices and important considerations for reproducible HRV research design will inform future translational research on cardiovascular risk reduction.

Clinical, polysomnographic, and heart rate variability in highland obstructive sleep apnea patients responding to one-night nocturnal oxygen supplementation: A post-hoc analysis from a randomized, crossover trial

OBJECTIVE

/Background: This study aimed to explore the clinical, polysomnographic, and heart rate variability (HRV) characteristics of highland obstructive sleep apnea (OSA) patients receiving one-night nocturnal oxygen supplementation (NOS) and to identify factors predicting response.

PATIENTS/METHODS

Thirty-four highland OSA patients living in Shangri-La were randomly assigned to receive NOS and sham oxygen in a randomized, placebo-controlled, crossover trial. Clinical assessments, polysomnography, and HRV were measured. A responder was defined as a ≥50% reduction in apnea-hypopnea index (AHI) with NOS compared with sham oxygen.

RESULTS

Eighteen participants responded and 16 did not respond, with a median (interquartile range [IQR]) age of 46.5 (36.5-53.0) and 48.0 (44.3-53.3) years, respectively. The median treatment effect (95% CI) on total AHI was -23.2/h (-30.0 to -17.5) and -12.0/h (-16.6 to -7.6) in responders and non-responders (p = 0.004), with similar effects on oxygen desaturation index. The mean OAH duration was prolonged by 7 s in responders together with improved sleep quality and daytime blood pressure. The mean OAH duration at baseline predicted responses to NOS with a sensitivity and specificity of 88.9% and 68.7% (AUC 0.809) at a cut-off point of 24.9 s. Changes in HRV parameters were negatively correlated with changes in mean oxygen saturation and daytime systolic blood pressure only in responders.

CONCLUSIONS

NOS significantly improved OSA severity and clinical outcomes in responders, which was related to improvements in parasympathetic activity. Highlanders with shorter mean OAH may be suitable candidates for NOS. These findings provide new information about tailored treatment strategies for highland OSA patients.

Anthropometric Determinants of Autonomic Control in Obstructive Sleep Apnea: A Large-Scale Study

OBJECTIVE

Autonomic dysfunction is an independent risk factor for cardiovascular disease (CVD). Both obesity and obstructive sleep apnea (OSA) are associated with heart rate variability (HRV) (a hall marker of sympathetic arousal) and increased risk of CVD. This study aims to investigate whether anthropometric parameters could predict reduced HRV in adult OSA during wakefulness.

STUDY DESIGN

Cross-sectional study.

SETTING

Sleep center of Shanghai Jiao Tong University Affiliated Sixth Hospital from 2012 to 2017.

METHODS

Total of 2134 subjects (503 non-OSA and 1631 OSA) were included. Anthropometric parameters were recorded. HRV was recorded during a 5-minute wakefulness period and analyzed by using time-domain method and frequency-domain method. Multiple step-wise linear regressions were performed to determine significant predictors of HRV with and without adjustments. Multiplicative interactions between gender, OSA, and obesity on HRV were also determined and evaluated.

RESULTS

Waist circumference (WC) was significant negative determinant of root mean square of successive NN intervals (β = -.116, p < .001) and high-frequency power (β = -.155, p < .001). Age was the strongest determining factor of HRV. Significant multiplicative interactions between obesity and OSA on HRV, gender, and obesity on cardiovascular parameters were observed.

CONCLUSION

Anthropometric parameters could predict reduced HRV during wakefulness in patients with OSA, especially WC was the strongest influenceable factor. Obesity and OSA had significant multiplicative interaction on HRV. Gender and obesity had significant multiplicative interaction on cardiovascular parameters. Early intervention for obesity, especially centripetal obesity, may improve reduction of autonomic function and risk of CVD.

Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate-blood pressure coupling quantified by entropy-based indices

We introduce an entropy-based classification method for pairs of sequences (ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood pressure recordings. The purpose of the method is to build a classifier for data in which each item consists of two intertwined data series taken for each subject. The method is based on ordinal patterns and uses entropy-like indices. Machine learning is used to select a subset of indices most suitable for our classification problem in order to build an optimal yet simple model for distinguishing between patients suffering from obstructive sleep apnea and a control group.

Daylily (Hemerocallis fulva Linn.) flowers improve sleep quality in human and reduce nitric oxide and interleukin-6 production in macrophages

The flowers of daylily (Hemerocallis fulva Linn.) have been used as vegetable and medicinal herb for thousands of years in Taiwan and eastern Asia. Daylily flowers have been demonstrated to exert several biomedical properties. In this study, we provided the evidences show that daylily flowers exert anti-inflammatory activity in vitro and improved the sleep quality in vivo. We demonstrated that adult volunteers received water extract of daylily flowers improved sleep quality, sleep efficiency and daytime functioning, while sleep latency was reduced, compared to the adult volunteers received water. In addition, we demonstrated that aqueous and ethanol extracts of daylily flowers inhibited nitric oxide and interleukin-6 production in lipopolysaccharide-activated macrophages. Furthermore, the quantitative high performance liquid chromatography-based analysis showed the rutin content of the aqueous extract, ethanolic extract, ethyl acetate fractions of ethanolic extract, and water fractions of ethanolic extract were 7.27, 23.30, 14.71, and 57.43 ppm, respectively. These results indicate that daylily flowers have the potential to be a nutraceutical for improving inflammatory-related diseases and sleep quality in the future.

Heart Rate Variability as a Surrogate Marker of Severe Chronic Coronary Syndrome in Patients with Obstructive Sleep Apnea

BACKGROUND AND OBJECTIVES

Obstructive sleep apnea (OSA) is a known risk factor for chronic coronary syndrome (CCS). CCS and OSA are separately associated with significant changes in heart rate variability (HRV). In this proof-of-concept study, we tested whether HRV values are significantly different between OSA patients with concomitant severe CCS, and OSA patients without known CCS.

MATERIAL AND METHODS

The study comprised a retrospective assessment of the historical and raw polysomnography (PSG) data of 32 patients who presented to a tertiary university hospital with clinical complaints of OSA. A total of 16 patients (four females, mean age 62.94 ± 2.74 years, mean body mass index (BMI) 31.93 ± 1.65 kg/m2) with OSA (median apnea-hypopnea index (AHI) 39.1 (30.5-70.6)/h) and severe CCS were compared to 16 patients (four females, mean age 62.35 ± 2.06 years, mean BMI 32.19 ± 1.07 kg/m2) with OSA (median AHI 40 (30.6-44.5)/h) but without severe CCS. The short-long-term HRV (in msec) was calculated based on the data of a single-lead electrocardiogram (ECG) provided by one full-night PSG, using the standard deviation of the NN, normal-to-normal intervals (SDNN) and the heart rate variability triangular index (HRVI) methods, and compared between the two groups.

RESULTS

A significant reduction (p < 0.05) in both SDNN and HRVI was found in the OSA group with CCS compared to the OSA group without CCS.

CONCLUSIONS

Severe CCS has a significant impact on short-long-term HRV in OSA patients. Further studies in OSA patients with less-severe CCS may shed more light onto the involved mechanistic processes. If confirmed in future larger studies, this physiologic metric has the potential to provide a robust surrogate marker of severe CCS in OSA patients.

Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use?

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.

An Obstructive Sleep Apnea - A Novel Public Health Threat

In patients with obstructive sleep apnea (OSA) during obstructive events, episodes of hypoxia and hypercapnia may modulate the autonomic nervous system (ANS) by increasing sympathetic tone and irritability, which contributes to sympathovagal imbalance and ultimately dysautonomia. Because OSA can alter ANS function through biochemical changes, we can assume that heart rate variability (HRV) will be altered in patients with OSA. Most studies show that in both the time and frequency domains, patients with OSA have higher sympathetic components and lower parasympathetic dominance than healthy controls. These results confirm autonomic dysfunction in these patients, but also provide new therapeutic directions. Respiratory methods that modulate ANS, e.g., cardiorespiratory biofeedback, could be beneficial for these patients. Heart rate variability assessment can be used as a tool to evaluate the effectiveness of OSA treatment due to its association with autonomic impairment.

Mechanisms of Atrial Fibrillation in Obstructive Sleep Apnoea

Obstructive sleep apnoea (OSA) is a strong independent risk factor for atrial fibrillation (AF). Emerging clinical data cite adverse effects of OSA on AF induction, maintenance, disease severity, and responsiveness to treatment. Prevention using continuous positive airway pressure (CPAP) is effective in some groups but is limited by its poor compliance. Thus, an improved understanding of the underlying arrhythmogenic mechanisms will facilitate the development of novel therapies and/or better selection of those currently available to complement CPAP in alleviating the burden of AF in OSA. Arrhythmogenesis in OSA is a multifactorial process characterised by a combination of acute atrial stimulation on a background of chronic electrical, structural, and autonomic remodelling. Chronic intermittent hypoxia (CIH), a key feature of OSA, is associated with long-term adaptive changes in myocyte ion channel currents, sensitising the atria to episodic bursts of autonomic reflex activity. CIH is also a potent driver of inflammatory and hypoxic stress, leading to fibrosis, connexin downregulation, and conduction slowing. Atrial stretch is brought about by negative thoracic pressure (NTP) swings during apnoea, promoting further chronic structural remodelling, as well as acutely dysregulating calcium handling and electrical function. Here, we provide an up-to-date review of these topical mechanistic insights and their roles in arrhythmia.

Nocturnal Heart Rate Variability Might Help in Predicting Severe Obstructive Sleep-Disordered Breathing

Obstructive sleep apnea (OSA) can have long-term cardiovascular and metabolic effects. The identification of OSA-related impairments would provide diagnostic and prognostic value. Heart rate variability (HRV) as a measure of cardiac autonomic regulation is a promising candidate marker of OSA and OSA-related conditions. We took advantage of the Physionet Apnea-ECG database for two purposes. First, we performed time- and frequency-domain analysis of nocturnal HRV on each recording of this database to evaluate the cardiac autonomic regulation in patients with nighttime sleep breathing disorders. Second, we conducted a logistic regression analysis (backward stepwise) to identify the HRV indices able to predict the apnea–hypopnea index (AHI) categories (i.e., “Severe OSA”, AHI ≥ 30; “Moderate-Mild OSA”, 5 ≥ AHI < 30; and “Normal”, AHI < 5). Compared to the “Normal”, the “Severe OSA” group showed lower high-frequency power in normalized units (HFnu) and higher low-frequency power in normalized units (LFnu). The standard deviation of normal R–R intervals (SDNN) and the root mean square of successive R–R interval differences (RMSSD) were independently associated with sleep-disordered breathing. Our findings suggest altered cardiac autonomic regulation with a reduced parasympathetic component in OSA patients and suggest a role of nighttime HRV in the characterization and identification of sleep breathing disorders.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.

Association of Heart Rate Variability with Obstructive Sleep Apnea in Adults

Background and Objectives: Heart rate variability (HRV) analysis is a noninvasive method used to examine autonomic system function, and the clinical applications of HRV analysis have been well documented. The aim of this study is to investigate the association between HRV and the apnea–hypopnea index (AHI) in patients referred for polysomnography (PSG) for obstructive sleep apnea (OSA) diagnosis. Materials and Methods: Patients underwent whole-night PSG. Data on nocturnal HRV and AHI were analyzed. We determined the correlation of time- and frequency-domain parameters of HRV with the AHI. Results: A total of 62 participants (50 men and 12 women) were enrolled. The mean age, body mass index (BMI), neck circumference, and AHI score of the patients were 44.4 ± 11.5 years, 28.7 ± 5.2, 40.2 ± 4.8 cm, and 32.1 ± 27.0, respectively. The log root mean square of successive differences between normal heartbeats (RMSSD) were negatively correlated with BMI (p = 0.034) and neck circumference (p = 0.003). The log absolute power of the low-frequency band over high-frequency band (LF/HF) ratio was positively correlated with the AHI (p = 0.006). A higher log LF/HF power ratio (β = 5.01, p = 0.029) and BMI (β = 2.20, p < 0.001) were associated with a higher AHI value in multiple linear regression analysis. Conclusions: A higher log LF/HF power ratio and BMI were positively and significantly associated with the AHI during whole-night PSG in adult patients.

Heart rate variability changes in patients with obstructive sleep apnea: A systematic review and meta-analysis

Obstructive sleep apnea is a common sleep breathing disorder related to autonomic nervous function disturbances. Heart rate variability is an important non-invasive indicator of autonomic nervous system function. The PubMed, Embase, Medline and Web of Science databases were systematically searched for English literature comparing patients with obstructive sleep apnea with controls up to May 2021. Heart rate variability outcomes, including integrated indices (parasympathetic function and total variability), time domain indices (the standard deviation of NN intervals and the root mean square of the successive differences between normal heartbeats) and frequency domain indices (high-frequency, low-frequency, very-low-frequency and the ratio of low-frequency to high-frequency) were derived from the studies. Twenty-two studies that included 2565 patients with obstructive sleep apnea and 1089 healthy controls were included. Compared with controls, patients with obstructive sleep apnea exhibited significantly reduced parasympathetic function. For the obstructive sleep apnea severity subgroup meta-analysis, patients with severe obstructive sleep apnea had significantly lower parasympathetic function, high-frequency, root mean square of the successive differences between normal heartbeats and standard deviation of NN intervals, and higher low-frequency and ratios of low-frequency to high-frequency. However, only the ratio of low-frequency to high-frequency was significantly higher in patients with moderate obstructive sleep apnea than in controls. Finally, for the collection time analysis, patients with obstructive sleep apnea had significantly higher low-frequency and ratio of low-frequency to high-frequency at night, significantly lower parasympathetic function, high-frequency, root mean square of the successive differences between normal heartbeats and standard deviation of NN intervals, and a higher ratio of low-frequency to high-frequency during the day than controls. Autonomic function impairment was more serious in patients with severe obstructive sleep apnea. During sleep, low-frequency can well reflect the impairment of autonomic function in obstructive sleep apnea, and the ratio of low-frequency to high-frequency may play an important role in obstructive sleep apnea diagnosis.

Photoplethysmography-new applications for an old technology: a sleep technology review

Education is integral to the American Academy of Sleep Medicine (AASM) mission. The AASM Emerging Technology Committee identified an important and evolving piece of technology that is present in many of the consumer and clinical technologies that we review on the AASM #SleepTechnology (https://aasm.org/consumer-clinical-sleep-technology/) resource-photoplethysmography. As more patients with sleep tracking devices ask clinicians to view their data, it is important for sleep providers to have a general understanding of the technology, its sensors, how it works, targeted users, evidence for the claimed uses, and its strengths and weaknesses. The focus in this review is photoplethysmography-a sensor type used in the familiar pulse oximeter that is being developed for additional utilities and data outputs in both consumer and clinical sleep technologies.

CITATION

Ryals S, Chang A, Schutte-Rodin S, et al. Photoplethysmography-new applications for an old technology: a sleep technology review. J Clin Sleep Med. 2023;19(1):189-195.

Role of Wearable Sensing Technology to Manage Long COVID

Long COVID consequences have changed the perception towards disease management, and it is moving towards personal healthcare monitoring. In this regard, wearable devices have revolutionized the personal healthcare sector to track and monitor physiological parameters of the human body continuously. This would be largely beneficial for early detection (asymptomatic and pre-symptomatic cases of COVID-19), live patient conditions, and long COVID monitoring (COVID recovered patients and healthy individuals) for better COVID-19 management. There are multitude of wearable devices that can observe various human body parameters for remotely monitoring patients and self-monitoring mode for individuals. Smart watches, smart tattoos, rings, smart facemasks, nano-patches, etc., have emerged as the monitoring devices for key physiological parameters, such as body temperature, respiration rate, heart rate, oxygen level, etc. This review includes long COVID challenges for frequent monitoring of biometrics and its possible solution with wearable device technologies for diagnosis and post-therapy of diseases.

Targeting autonomic nervous system as a biomarker of well-ageing in the prevention of stroke

Stroke prediction is a key health issue for preventive medicine. Atrial fibrillation (AF) detection is well established and the importance of obstructive sleep apneas (OSA) has emerged in recent years. Although autonomic nervous system (ANS) appears strongly implicated in stroke occurrence, this factor is more rarely considered. However, the consequences of decreased parasympathetic activity explored in large cohort studies through measurement of ANS activity indicate that an ability to improve its activity level and equilibrium may prevent stroke. In support of these observations, a compensatory neurostimulation has already proved beneficial on endothelium function. The available data on stroke predictions from ANS is based on many long-term stroke cohorts. These data underline the need of repeated ANS evaluation for the general population, in a medical environment, and remotely by emerging telemedicine digital tools. This would help uncovering the reasons behind the ANS imbalance that would need to be medically adjusted to decrease the risk of stroke. This ANS unbalance help to draw attention on clinical or non-clinical evidence, disclosing the vascular risk, as ANS activity integrates the cumulated risk from many factors of which most are modifiable, such as metabolic inadaptation in diabetes and obesity, sleep ventilatory disorders, hypertension, inflammation, and lack of physical activity. Treating these factors may determine ANS recovery through the appropriate management of these conditions. Natural aging also decreases ANS activity. ANS recovery will decrease global circulating inflammation, which will reinforce endothelial function and thus protect the vessels and the associated organs. ANS is the whistle-blower of vascular risk and the actor of vascular health. Such as, ANS should be regularly checked to help draw attention on vascular risk and help follow the improvements in response to our interventions. While today prediction of stroke relies on classical cardiovascular risk factors, adding autonomic biomarkers as HRV parameters may significantly increase the prediction of stroke.

Screening of plasma exosomal lncRNAs to identify potential biomarkers for obstructive sleep apnea

Background

Obstructive sleep apnea (OSA) is highly prevalent, but frequently undiagnosed. The existing biomarkers of OSA are relatively insensitive and inaccurate. Long non-coding RNAs (lncRNAs) have no protein-coding ability but have a role in regulating gene expression. They are stably expressed in exosomes, easily and rapidly measurable. Changes in expression of exosomal lncRNAs can be useful for disease diagnoses. However, there are few reports on the association of exosomal lncRNAs with OSA. We aimed to investigate the exosomal lncRNA profiles to establish the differences between non-OSA, OSA with or without hypertension (HTN) and serve as a potential diagnostic biomarker.

Methods

This diagnostic test included 63 participants: [normal control (NC) =25], (OSA =23), and (HTN-OSA =15). Expression profiling of lncRNAs in isolated exosomes was performed through high-throughput sequencing in 9 participants. Subsequently, OSA/HTN-OSA related lncRNAs were selected for validation by droplet digital polymerase chain reaction (ddPCR), receiver operating characteristic (ROC) curves were used to determine the diagnostic value. The reliabilities of the screened gene were further validated in another independent cohort: (NC =10), (OSA mild =10), (OSA moderate =11), and (OSA severe =10), the correlation between clinical features and its expression was analyzed. The MiRanda software was used to predict the binding sites of interaction between microRNA (miRNA) and target genes regulated by screened lncRNA.

Results

We identified the differentially expressed lncRNAs and mRNAs in plasma exosomes of the NC, OSA, HTN-OSA groups. Most pathways enriched in differentially expressed lncRNAs and mRNAs had previously been linked to OSA. Among them, ENST00000592016 enables discrimination between NC and OSA individuals [area under curve (AUC) =0.846, 95% confidence interval (CI): 0.72–0.97]. The severity of OSA was associated with changes in the ENST00000592016 expression. Furthermore, ENST00000592016 affected the PI3K-Akt, MAPK, and TNF pathways by regulating miRNA expressions.

Conclusions

This is the first report about differential expression of lncRNA in OSA and HTN-OSA exosomes. ENST00000592016 enables discrimination between NC and OSA individuals. This work enabled characterization of OSA and provided the preliminary work for the study of biomarker of OSA.

Quantitative analysis of heart rate variability parameter and mental stress index

Background

Cardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.

Methods

HRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.

Results

Experimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.

Conclusion

Our algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

Associations between Sleep Quality and Heart Rate Variability: Implications for a Biological Model of Stress Detection Using Wearable Technology

INTRODUCTION

The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals.

AIM

In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices.

METHODS

Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test).

RESULT

Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks.

CONCLUSION

Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.

Sleep staging classification based on a new parallel fusion method of multiple sources signals

APPROACH

First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm (WCA) and an "HYF" RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform (DWT), Autoregressive (AR), and Power Spectral entropy (PSE), and Refined Composite Multiscale Dispersion Entropy (RCMDE). Third, a new "Parallel Fusion Method" (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) classification models is employed for sleep staging.

MAIN RESULTS

Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is testedby evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 82.7% and the kappa coefficient is 0.711, also by using SVM classification model with the PFM is 83.7%, and the kappa coefficient is 0.724. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved. Significance： As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.

Increased sympathetic tone is associated with illness burden in bipolar disorder

BACKGROUND

We recently described an association between reduced heart rate variability (HRV) and illness burden in bipolar disorder (BD) using a novel Illness Burden Index (IBI). We aimed to further characterize this association by using spectral analyses to assess whether the IBI is also associated with autonomic imbalance in BD patients.

METHODS

In this cross-sectional study, 53 participants with BD wore a device for 24 h to assess association between HRV spectral measures and the IBI or each of its components (age of onset, number and type of previous episode(s), duration of the most severe episode, history of suicide attempts or psychotic symptoms during episodes, co-morbid psychiatric disorders, and family history). We ran both unadjusted models and models controlling for age, sex, years of education, marital status, BMI, pharmacotherapy, and baseline functional cardiovascular capacity.

RESULTS

HRV low-frequency (LF) normalized values were almost twice as high as published in healthy controls. Higher IBI was associated with higher LF and lower High Frequency (HF) values, resulting in a higher LF/HF ratio, indicating an increased sympathetic tone. Four individual components of the IBI were similarly associated with measures of increased sympathetic tone: earlier age of onset, number of depressive episodes, co-morbid anxiety disorders, and family history of suicide. Adjusted and unadjusted models had similar results.

LIMITATIONS

Our models used mean LF and HF and do not consider their dynamic variations over 24 h or phase of the illness.

CONCLUSIONS

Burden of illness is associated with increased sympathetic tone in patients with BD, putting them at risk for arrythmias and sudden death.

Prevalence and effects of sleep-disordered breathing on middle-aged patients with sedative-free generalized anxiety disorder: A prospective case-control study

OBJECTIVE

Generalized anxiety disorder (GAD) and sleep-disordered breathing (SDB) share similar symptoms, such as poor sleep quality, irritability, and poor concentration during daily activities. This study aims to investigate the proportion of undiagnosed SDB and its impacts on anxiety severity and autonomic function in newly diagnosed, sedative-free GAD patients.

METHODS

This prospective case-control study included newly diagnosed GAD patients and control participants with matched age, sex, and body mass index (BMI) in Taiwan. All participants completed questionnaires for sleep and mood symptoms and a resting 5-min heart rate variability (HRV) examination during enrollment. The participants also used a home sleep apnea test to detect SDB. An oxygen desaturation index (ODI) ≥ 5 was considered indicative of SDB.

RESULTS

In total, 56 controls and 47 newly diagnosed GAD participants (mean age 55.31 ± 12.36 years, mean BMI 23.41 ± 3.42 kg/m²) were included. There was no significant difference in the proportion of undiagnosed SDB in the control and sedative-free GAD groups (46.43 vs. 51.06%). Sedative-free GAD patients with SDB scored significantly higher on Beck Anxiety Inventory (23.83 ± 11.54) than those without SDB (16.52 ± 10.61) (p < 0.001). Both control and sedative-free GAD groups with SDB had worse global autonomic function than the control group without SDB, as evidenced by the HRV results (p < 0.05 for all).

CONCLUSION

Average age 55 years and mean BMI 23 kg/m² patients with GAD and matched controls had an undiagnosed SDB prevalence of approximately 50%. SDB correlated with worsening anxiety severity and reduced cardiac autonomic function. Moreover, age and BMI were considered major risk factors for predicting undiagnosed SDB.

ECG and Heart Rate Variability in Sleep-Related Breathing Disorders

Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.

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).

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

OBJECTIVE

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients.

DATA SOURCES

PubMed.

REVIEW METHODS

A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review.

CONCLUSIONS

In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care.

IMPLICATIONS FOR PRACTICE

Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.

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.

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

SDNN24 Estimation from Semi-Continuous HR Measures

The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users' heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.