The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Heart rate variability analysis in obstructive sleep apnea patients with daytime sleepiness

STUDY OBJECTIVES

Recent studies suggest that sleepy patients with obstructive sleep apnea (OSA) are at higher risk for incident cardiovascular disease. This study assessed cardiac autonomic function in sleepy versus non-sleepy patients with OSA using heart rate variability (HRV) analysis. We hypothesized that HRV profiles of sleepy patients would indicate higher cardiovascular risk.

METHODS

Electrocardiograms (ECG) derived from polysomnograms (PSG) collected by the Sydney Sleep Biobank were used to study HRV in groups of sleepy (ESS ≥ 10) and non-sleepy OSA patients (ESS < 10). HRV parameters were averaged across available ECG signals during N2 sleep.

RESULTS

A total of 421 patients were evaluated, with a mean age of 54 (14) years, body mass index of 33 (9) kg/m2, apnea-hypopnea index of 21 (28) events/h, and 66% male. The sleepy group consisted of 119 patients and the non-sleepy group 302 patients. Sleepy patients exhibited lower HRV values for: root mean square successive difference (RMSSD, p = 0.028), total power (TP, p = 0.031), absolute low frequency (LF, p = 0.045), and high-frequency (HF, p = 0.010) power compared to non-sleepy patients. Sleepy patients with moderate-to-severe OSA exhibited lower HRV values for: (RMSSD, p = 0.045; TP, p = 0.052), absolute LF (p = 0.051), and HF power (p = 0.025). There were no differences in other time and frequency domain HRV markers.

CONCLUSIONS

This study shows a trend toward parasympathetic withdrawal in sleepy OSA patients, particularly in moderate-to-severe cases, lending mechanistic support to the link between the sleepy phenotype and CVD risk in 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.

Comprehensive viewpoints on heart rate variability at high altitude

OBJECTIVES

Hypoxia is a physiological state characterized by reduced oxygen levels in organs and tissues. It is a common clinicopathological process and a major cause of health problems in highland areas.  Heart rate variability (HRV) is a measure of the balance in autonomic innervation to the heart. It provides valuable information on the regulation of the cardiovascular system by neurohumoral factors, and changes in HRV reflect the complex interactions between multiple systems. In this review, we provide a comprehensive overview of the relationship between high-altitude hypoxia and HRV. We summarize the different mechanisms of diseases caused by hypoxia and explore the changes in HRV across various systems. Additionally, we discuss relevant pharmaceutical interventions. Overall, this review aims to provide research ideas and assistance for in-depth studies on HRV. By understanding the intricate relationship between high-altitude hypoxia and HRV, we can gain insights into the underlying mechanisms and potential therapeutic approaches to mitigate the effects of hypoxia on cardiovascular and other systems.

METHODS

The relevant literature was collected systematically from scientific database, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Baidu Scholar, as well as other literature sources, such as classic books of hypoxia.

RESULTS

There is a close relationship between heart rate variability and high-altitude hypoxia. Heart rate variability is an indicator that evaluates the impact of hypoxia on the cardiovascular system and other related systems. By improving the observation of HRV, we can estimate the progress of cardiovascular diseases and predict the impact on other systems related to cardiovascular health. At the same time, changes in heart rate variability can be used to observe the efficacy of preventive drugs for altitude related diseases.

CONCLUSIONS

HRV can be used to assess autonomic nervous function under various systemic conditions, and can be used to predict and monitor diseases caused by hypoxia at high altitude. Investigating the correlation between high altitude hypoxia and heart rate variability can help make HRV more rapid, accurate, and effective for the diagnosis of plateau-related diseases.

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.

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.

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.

Does workplace telepressure get under the skin? Protocol for an ambulatory assessment study on wellbeing and health-related physiological, experiential, and behavioral concomitants of workplace telepressure

BACKGROUND

The daily working life of many employees requires the use of modern information and communication technology (ICT) devices such as computers, tablets, and smartphones. The double-edged nature of digital work environments has been increasingly highlighted. Benefits such as increased flexibility come at a personal cost. One of the potential downsides is workplace telepressure, i.e., the experience of urge and preoccupation to quickly reply to work-related messages and demands using ICT. There is initial - mainly survey-based-evidence that workplace telepressure may have negative effects on a variety of wellbeing and health outcomes.

AIMS AND HYPOTHESES

Adopting the Effort-Recovery Model and the concept of allostatic load as theoretical frameworks, the present study aims to investigate the hypothesis that workplace telepressure is significantly associated with increased "wear and tear", in the form of more psychosomatic complaints, worse sleep quality (self-reported and actigraphy-based), worse mood, and biological alterations (lower cardiac vagal tone, lower anabolic balance defined as the ratio of salivary dehydroepiandrosterone to salivary cortisol, and higher salivary alpha-amylase). Additionally, the study aims to investigate the hypothesis that connection to work defined as work-related workload and work-related perseverative cognition plays a significant role in the mediation of these relationships.

METHODS

To test our hypotheses, we will conduct an ambulatory assessment study with a convenience sample of 120 healthy workers regularly using ICTs for job communication. For one week, participants will be asked to complete electronic diaries assessing their level of workplace telepressure, psychosomatic complaints, sleep quality, mood, work-related workload, and work-related perseverative cognition. They will also continuously wear the Bittium Faros 180L ECG monitor, the wrist-worn actigraph MotionWatch 8, and perform saliva sampling five times per day.

DISCUSSION

This study will be the most comprehensive ambulatory investigation of workplace telepressure and its psychophysiological concomitants to date and constitutes an important step towards understanding how high levels of workplace telepressure may lead in the long term to secondary alterations (e.g., hypertension, chronic inflammation) and disease (e.g., heart disease). The findings of this study are also anticipated to contribute to guiding the development and implementation of interventions, programs, and policies relevant to employees' digital wellbeing.

A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine

Digital health and telemonitoring have resulted in a wealth of information to be collected to monitor, manage, and improve human health. The multi-source mixed-frequency health data overwhelm the modeling capacity of existing statistical and machine learning models, due to many challenging properties. Although predictive analytics for big health data plays an important role in telemonitoring, there is a lack of rigorous prediction model that can automatically predicts patients' health conditions, e.g., Disease Severity Indicators (DSIs), from multi-source mixed-frequency data. Sleep disorder is a prevalent cardiac syndrome that is characterized by abnormal respiratory patterns during sleep. Although wearable devices are available to administrate sleep studies at home, the manual scoring process to generate the DSI remains a bottleneck in automated monitoring and diagnosis of sleep disorder. To address the multi-fold challenges for precise prediction of the DSI from high-dimensional multi-source mixed-frequency data in sleep disorder, we propose a sparse linear mixed model that combines the modified Cholesky decomposition with group lasso penalties to enable joint group selection of fixed effects and random effects. A novel Expectation Maximization (EM) algorithm integrated with an efficient Majorization Maximization (MM) algorithm is developed for model estimation of the proposed sparse linear mixed model with group variable selection. The proposed method was applied to the SHHS data for telemonitoring and diagnosis of sleep disorder and found that a few significant feature groups that are consistent with prior medical studies on sleep disorder. The proposed method also outperformed a few benchmark methods with the highest prediction accuracy.

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.

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability

Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1-5 e/s; 5-10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.

Screening of obstructive sleep apnea syndrome by the deep breathing technique

STUDY OBJECTIVES

Obstructive sleep apnea syndrome (OSAS) is associated with alterations in heart rate variability (HRV) in relation to chronic autonomic dysfunction. We tested the ability of the deep breathing technique-a simple way to evaluate HRV-to identify patients with OSAS.

METHODS

Consecutive patients referred for suspected OSAS (without obesity, diabetes, and heart diseases) were included. They underwent a measure of HRV at rest and of heart rate oscillations during expiration vs inspiration (DeltaHRDB) when breathing deeply at the resonant frequency of 6 cycles per minute (deep breathing technique) while sitting awake, followed by a nighttime polysomnography. We measured DeltaHRDB and performed temporal and spectral HRV analysis.

RESULTS

Of 31 included participants (77% male), 14 had mild to moderate OSAS (apnea-hypopnea index median [IQR]: 18 [12]) and 17 had no OSAS. The conventional HRV analysis did not reveal any difference between the groups with vs without OSAS. However, the DeltaHRDB was lower in those with than without OSAS. Lower DeltaHRDB correlated with higher apnea-hypopnea index, arousal index, and desaturation degree. A DeltaHRDB below 11 beats per minute (bpm) predicted OSAS with a sensitivity of 100% and specificity of 86%.

CONCLUSIONS

The deep breathing technique accurately identifies a reduction in cardiac changes in patients with mild to moderate OSAS. It could be used as a simple screening tool to select patients for polysomnography.

CITATION

Onanga M, Joanny S, Rivals I, et al. Screening of obstructive sleep apnea syndrome by the deep breathing technique. J Clin Sleep Med. 2023;19(2):293-302.

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.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

The effect of obstructive sleep apnea therapy on cardiovascular autonomic function: a systematic review and meta-analysis

STUDY OBJECTIVES

Autonomic function is impaired in obstructive sleep apnea (OSA) and may mediate the association between OSA and cardiovascular risk. We investigated the effect of OSA therapy on autonomic function through a systematic review and meta-analysis of intervention studies.

METHODS

A systematic search using three databases (Medline, Embase, and Scopus) was performed up to December 9, 2020. Studies of OSA patients ≥ 18 years with autonomic function assessed before and after treatment with positive airway pressure, oral appliance, positional therapy, weight loss, or surgical intervention were included for review. Random effects meta-analysis was carried out for five groups of autonomic function indices. Risk of bias was assessed using the Cochrane Collaboration tool.

RESULTS

Forty-three eligible studies were reviewed with 39 included in the meta-analysis. OSA treatment led to large decreases in muscle sympathetic nerve activity (Hedges' g = -1.08; 95% CI -1.50, -0.65, n = 8) and moderate decreases in catecholamines (-0.60; -0.94, -0.27, n = 3) and radio nucleotide imaging (-0.61; -0.99, -0.24, n = 2). OSA therapy had no significant effect on baroreflex function (Hedges' g = 0.15; 95% CI -0.09, 0.39, n = 6) or heart rate variability (0.02; -0.32, 0.36, n = 14). There was a significant risk of bias due to studies being primarily non-randomized trials.

CONCLUSIONS

OSA therapy selectively improves autonomic function measures. The strongest evidence for the effect of OSA therapy on autonomic function was seen in reduced sympathetic activity as assessed by microneurography, but without increased improvement in parasympathetic function. OSA therapy may reduce the risk of cardiovascular disease in OSA through reduced sympathetic activity.

Sleep dysregulation in sympathetic-mediated diseases: implications for disease progression

The autonomic nervous system (ANS) plays an important role in the coordination of several physiological functions including sleep/wake process. Significant changes in ANS activity occur during wake-to-sleep transition maintaining the adequate cardiorespiratory regulation and brain activity. Since sleep is a complex homeostatic function, partly regulated by the ANS, it is not surprising that sleep disruption trigger and/or evidence symptoms of ANS impairment. Indeed, several studies suggest a bidirectional relationship between impaired ANS function (i.e. enhanced sympathetic drive), and the emergence/development of sleep disorders. Furthermore, several epidemiological studies described a strong association between sympathetic-mediated diseases and the development and maintenance of sleep disorders resulting in a vicious cycle with adverse outcomes and increased mortality risk. However, which and how the sleep/wake control and ANS circuitry becomes affected during the progression of ANS-related diseases remains poorly understood. Thus, understanding the physiological mechanisms underpinning sleep/wake-dependent sympathetic modulation could provide insights into diseases involving autonomic dysfunction. The purpose of this review is to explore potential neural mechanisms involved in both the onset/maintenance of sympathetic-mediated diseases (Rett syndrome, congenital central hypoventilation syndrome, obstructive sleep apnoea, type 2 diabetes, obesity, heart failure, hypertension, and neurodegenerative diseases) and their plausible contribution to the generation of sleep disorders in order to review evidence that may serve to establish a causal link between sleep disorders and heightened sympathetic activity.

Heart Rate Variability from Wearable Photoplethysmography Systems: Implications in Sleep Studies at High Altitude

The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R–R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis.

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).

Influencing Cardiovascular Outcomes through Heart Rate Variability Modulation: A Systematic Review

Psychological stress is a well-established risk factor for cardiovascular disease (CVD). Heart rate variability (HRV)-biofeedback could significantly reduce stress levels and improve autonomic nervous system function and cardiovascular endpoints. We aimed to systematically review the literature to investigate the impact of HRV modulation through HRV-biofeedback on clinical outcomes in patients with CVD. A literature search was performed in the following databases: MEDLINE (PubMed), Embase, and Cochrane from the inception until 1 October 2021. Patients in the HRV-biofeedback group had significantly lower rates of all-cause readmissions than patients who received psychological education (respectively, p = 0.028 and p = 0.001). Heart failure following HRV-biofeedback displayed an inverse association with stress and depression (respectively, p = 0.022 and p = 0.033). When stratified according to left ventricular ejection fraction (LVEF), patients with LVEF ≥ 31% showed improved values of the 6 min walk test after HRV-biofeedback interventions (p = 0.05). A reduction in systolic and diastolic blood pressure associated with HRV-biofeedback was observed (p < 0.01) in pre-hypertensive patients. HRV-biofeedback had beneficial effects on different cardiovascular diseases documented in clinical trials, such as arterial hypertension, heart failure, and coronary artery disease. A standard breathing protocol should be applied in future studies to obtain equivalent results and outcomes. However, data regarding mortality in patients with coronary artery disease are scarce and need further research.