The link between cardiac autonomic activity and sleep delta power is altered in men with sleep apnea-hypopnea syndrome

Sleep stage, sleep fragmentation and heart rate variability during the initial 3-h sleep period in patients with obstructive sleep apnea syndrome

To investigate differences in polysomnography (PSG) parameters and heart rate variability (HRV) during the initial 3-h sleep period in patients with mild, moderate, and severe obstructive sleep apnea syndrome (OSA). According to the apnea-hypopnea index, patients were divided into 3 groups: mild, moderate, and severe (n = 23, 59, and 94, respectively). PSG was performed, and HRV (frequency domain analysis), sleep stage (S1, S2, S3, REM, and waking), and sleep fragmentation index (SFI) were measured during the initial 3-h sleep periods. The total S1 time was significantly longer in the severe group than in the mild and moderate groups (p < 0.001). The severe group had significantly shorter total S2 and S3 times than the mild (p = 0.014, p < 0.001) and moderate (p = 0.034, p = 0.029) groups did. The SFI was significantly greater in the severe group than in the mild and moderate groups (p < 0.001, p = 0.008). The high-frequency component (HF) of the HRV showed no significant differences except that it was significantly smaller during S3 in the moderate group than in the mild group (p = 0.026). Compared with those with mild/moderate status, patients with severe OSA have shallower sleep and a higher SFI, suggesting poorer sleep quality. Although HF during S3 was significantly smaller in the moderate group, it did not significantly differ between the mild and severe groups, suggesting that the parasympathetic nervous system might compensate for humoral and hypoxemic abnormalities in patients with severe OSA.

Cardiac autonomic function in REM-related obstructive sleep apnoea: insights from nocturnal heart rate variability profiles

PURPOSE

In light of the reported association between REM-related obstructive sleep apnoea (OSA) and heightened cardiovascular risk, this study aims to compare cardiac autonomic function in patients with REM-OSA and OSA independent of sleep stage. We hypothesized that REM-OSA patients would exhibit higher sympathetic cardiac modulation based on heart rate variability (HRV) profiles.

METHODS

HRV was compared between the OSA group (AHI ≥ 5 events/h, n = 252) and the REM-OSA group (AHI ≥ 5 events/h, AHIREM:AHINREM ≥ 2, n = 137). Time- and frequency-domain measures of HRV were analysed during N2 and REM sleep.

RESULTS

Clinical characteristics between the two test groups differed significantly, 45% of REM-OSA patients were female, with mild OSA (median, interquartile range (IQR)) AHI of 10 (7) events/h. Only 26% of the OSA cohort were female with moderate OSA (AHI = 17 (20) events/h, p < 0.001). Compared with the OSA group, the low frequency to high frequency ratio (LF:HF) and LF power were lower and HF power was higher in the REM-OSA group during N2 (LF:HF, p = 0.012; LF; p = 0.013; HF, p = 0.007) and in REM sleep (LF:HF, p = 0.002; LF, p = 0.004; HF, p < 0.001). Patient sex and OSA severity had a significant combined effect on average N to N interval, LF power, and LF:HF ratio during N2 and REM sleep (all p < 0.001).

CONCLUSION

Contrary to our hypothesis, REM-OSA patients demonstrated consistently higher cardiac vagal modulation, reflecting better cardiac autonomic adaptation. These results were attributed to differences in OSA severity and sex in these two groups, both independently affecting HRV. This study emphasises the need for future research into the underlying pathophysiology of REM-OSA and the potential implications of sex and OSA severity on cardiovascular risk.

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.

Heart rate and heart rate variability following sleep deprivation in retired night shift workers and retired day workers

Shift workers experience poor sleep and dysregulated cardiac autonomic function during sleep. However, it is unknown if this dysregulation persists into retirement, potentially accelerating the age-associated risk for adverse cardiovascular outcomes. Using sleep deprivation as a physiological challenge to cardiovascular autonomic function, we compared heart rate (HR) and high-frequency heart rate variability (HF-HRV) during baseline and recovery sleep following sleep deprivation between retired night shift and day workers. Participants were retired night shift (N = 33) and day workers (N = 37) equated on age (mean [standard deviation] = 68.0 [5.6] years), sex (47% female), race/ethnicity (86% White), and body mass index. Participants completed a 60-h lab protocol including one night of baseline polysomnography-monitored sleep, followed by 36 h of sleep deprivation and one night of recovery sleep. Continuously recorded HR was used to calculate HF-HRV. Linear mixed models compared HR and HF-HRV during non-rapid eye movement (NREM) and REM sleep between groups during baseline and recovery nights. Groups did not differ on HR or HF-HRV during NREM or REM sleep (ps > .05) and did not show differential responses to sleep deprivation. In the full sample, HR increased and HF-HRV decreased from baseline to recovery during NREM (ps < .05) and REM (ps < .01). Both groups exhibited cardiovascular autonomic changes during recovery sleep following 36 h of sleep deprivation. Sleep deprivation appears to induce cardiovascular autonomic changes that persist into recovery sleep in older adults, regardless of shift work history.

The First-Night Effect on the Instability of Stage N2: Evidence from the Activity of the Central and Autonomic Nervous Systems

A series of studies have suggested that stage N2 is vulnerable and strongly affected by the first-night effect (FNE). However, the neurophysiological mechanism underlying the vulnerability of stage N2 of the FNE has not been well examined. A total of 17 healthy adults (11 women and 6 men, mean age: 21.59 ± 2.12) underwent two nights of polysomnogram recordings in the sleep laboratory. We analyzed sleep structure and central and autonomic nervous system activity during stage N2 and applied the electroencephalographic (EEG) activation index (beta/delta power ratio) and heart rate variability to reflect changes in central and autonomic nervous system activity caused by the FNE. Correlation analyses were performed between EEG activation and heart rate variability. The results showed that EEG activation and high-frequency heart rate variability increased on the adaptation night (Night 1). Importantly, EEG activation was significantly associated with the percentage of stage N1, and the correlation between EEG activation and high-frequency heart rate variability decreased due to the FNE. These findings indicate that the FNE affects the instability of stage N2 by increasing central nervous system activity and uncoupling the activity between the central and autonomic nervous systems.

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.

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.

Changes in physiological network connectivity of body system in narcolepsy during REM sleep

BACKGROUND

Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks.

METHOD

Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine.

RESULT

In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively.

CONCLUSION

Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Association between autonomic function and obstructive sleep apnea: A systematic review

Obstructive sleep apnea (OSA) is an independent risk factor for hypertension and cardiovascular disease. Effects of OSA on the autonomic nervous system may mediate this association. We performed a systematic literature review to determine the profile of autonomic function associated with OSA. Three electronic databases were searched for studies of OSA patients aged ≥18 years in which autonomic function was assessed. Studies comparing patients with and without OSA, or examining the association of OSA severity with changes in autonomic function were included. Seventy-one studies met the inclusion criteria and autonomic function has been assessed using a range of techniques. The profile of autonomic function found in OSA include increased sympathetic activity, reduced parasympathetic activity and less consistently found low heart rate variability. Altered autonomic function in OSA may explain the pathophysiology of increased cardiovascular risk. Evidence from intervention studies is required to determine if treatment improves autonomic function associated with OSA.

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

Obstructive sleep apnea (OSA) is a highly prevalent condition, resulting in recurrent hypoxic events, sleep arousal, and daytime sleepiness. Patients with OSA are at an increased risk of cardiovascular morbidity and mortality. The mechanisms underlying the development of cardiovascular disease in OSA are multifactorial and cause a cascade of events. The primary contributing factor is sympathetic overactivity. Heart rate variability (HRV) can be used to evaluate shifts in the autonomic nervous system, during sleep and in response to treatment in patients with OSA. Newer technologies are aimed at improving HRV analysis to accelerate processing time, improve the diagnosis of OSA, and detection of cardiovascular risk. The present review will present contemporary understandings and uses for HRV, specifically in the realms of physiology, technology, and clinical management.

Multicomponent Analysis of Sleep Using Electrocortical, Respiratory, Autonomic and Hemodynamic Signals Reveals Distinct Features of Stable and Unstable NREM and REM Sleep

A new concept of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep is proposed, that of multi-component integrative states that define stable and unstable sleep, respectively, NREM_S, NREM_US REM_S, and REM_US. Three complementary data sets are used: obstructive sleep apnea (20), healthy subjects (11), and high loop gain sleep apnea (50). We use polysomnography (PSG) with beat-to-beat blood pressure monitoring, and electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC) analysis to demonstrate a bimodal, rather than graded, characteristic of NREM sleep. Stable NREM (NREM_S) is characterized by high probability of occurrence of the <1 Hz slow oscillation, high delta power, stable breathing, blood pressure dipping, strong sinus arrhythmia and vagal dominance, and high frequency CPC. Conversely, unstable NREM (NREM_US) has the opposite features: a fragmented and discontinuous <1 Hz slow oscillation, non-dipping of blood pressure, unstable respiration, cyclic variation in heart rate, and low frequency CPC. The dimension of NREM stability raises the possibility of a comprehensive integrated multicomponent network model of NREM sleep which captures sleep onset (e.g., ventrolateral preoptic area-based sleep switch) processes, synaptic homeostatic delta power kinetics, and the interaction of global and local sleep processes as reflected in the spatiotemporal evolution of cortical “UP” and “DOWN” states, while incorporating the complex dynamics of autonomic-respiratory-hemodynamic systems during sleep. Bimodality of REM sleep is harder to discern in health. However, individuals with combined obstructive and central sleep apnea allows ready recognition of REM_S and REM_US (stable and unstable REM sleep, respectively), especially when there is a discordance of respiratory patterns in relation to conventional stage of sleep.

Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state-space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications.

A Bradycardia-Based Stress Calculator for the Neonatal Intensive Care Unit: A Multisystem Approach

Early life stress in the neonatal intensive care unit (NICU) can predispose premature infants to adverse health outcomes and neurodevelopment delays. Hands-on-care and procedural pain might induce apneas, hypoxic events, and sleep-wake disturbances, which can ultimately impact maturation, but a data-driven method based on physiological fingerprints to quantify early-life stress does not exist. This study aims to provide an automatic stress detector by investigating the relationship between bradycardias, hypoxic events and perinatal stress in NICU patients. EEG, ECG, and SpO 2 were recorded from 136 patients for at least 3 h in three different monitoring groups. In these subjects, the stress burden was assessed using the Leuven Pain Scale. Different subspace linear discriminant analysis models were designed to detect the presence or the absence of stress based on information in each bradycardic spell. The classification shows an area under the curve in the range [0.80-0.96] and a kappa score in the range [0.41-0.80]. The results suggest that stress seems to increase SpO 2 desaturations and EEG regularity as well as the interaction between the cardiovascular and neurological system. It might be possible that stress load enhances the reaction to respiratory abnormalities, which could ultimately impact the neurological and behavioral development.

Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality

Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability.

Age and autonomic control, but not cerebral oxygenation, are significant determinants of EEG spectral power in children

Study Objectives

Sleep disordered breathing (SDB) in children has significant effects on daytime functioning and cardiovascular control; attributed to sleep fragmentation and repetitive hypoxia. Associations between electroencephalograph (EEG) spectral power, autonomic cardiovascular control and cerebral oxygenation have been identified in adults with SDB. To date, there have been no studies in children. We aimed to assess associations between EEG spectral power and heart rate variability as a measure of autonomic control, with cerebral oxygenation in children with SDB.

Methods

One hundred sixteen children (3–12 years) with SDB and 42 controls underwent overnight polysomnography including measurement of cerebral oxygenation. Power spectral analysis of the EEG derived from C4-M1 and F4-M1, quantified delta, theta, alpha, and beta waveforms during sleep. Multiple regression tested whether age, SDB severity, heart rate (HR), HR variability (HRV), and cerebral oxygenation were determinants of EEG spectral power.

Results

There were no differences in EEG spectral power derived from either central or frontal regions for any frequency between children with different severities of SDB so these were combined. Age, HR, and HRV low frequency power were significant determinants of EEG spectral power depending on brain region and sleep stage.

Conclusion

The significant findings of this study were that age and autonomic control, rather than cerebral oxygenation and SDB severity, were predictive of EEG spectral power in children. Further research is needed to elucidate how the physiology that underlies the relationship between autonomic control and EEG impacts on the cardiovascular sequelae in children with SDB.

Association Between Phase Coupling of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity During Sleep

Phase coupling of respiratory sinus arrhythmia (RSA) has been proposed to be an alternative measure for evaluating autonomic nervous system (ANS) activity. The aim of this study was to analyze how phase coupling of RSA is altered during sleep, in order to explore whether this measure is a predictor of slow wave sleep (SWS). Overnight electroencephalograms (EEG), electrocardiograms (ECG), and breathing using inductance plethysmography were recorded from 30 healthy volunteers (six females, age range 21–64, 31.6 ± 14.7 years). Slow wave activity was evaluated by the envelope of the amplitude of the EEG δ-wave (0.5–4 Hz). The RSA was extracted from the change in the R-R interval (RRI) by band-pass filter, where pass band frequencies were determined from the profile of the power spectral density for respiration. The analytic signals of RSA and respiration were obtained by Hilbert transform, after which the amplitude of RSA (A_RSA) and the degree of phase coupling (λ) were quantified. Additionally, the normalized high-frequency component (HF_n) of the frequency-domain heart rate variability (HRV) was calculated. Using auto- and cross-correlation analyses, we found that overnight profiles of λ and δ-wave were correlated, with significant cross-correlation coefficients (0.461 ± 0.107). The δ-wave and HF_n were also correlated (0.426 ± 0.115). These correlations were higher than that for the relationship between δ-wave and A_RSA (0.212 ± 0.161). The variation of λ precedes the onset of the δ-wave by ~3 min, suggesting a vagal enhancement prior to the onset of SWS. Auto correlation analysis revealed that the periodicity of λ was quite similar to that of the δ-wave (88.3 ± 15.7 min vs. 88.6 ± 16.3 min, λ-cycle = 0.938 × δ-cycle + 5.77 min, r = 0.902). These results suggest that phase coupling analysis of RSA appears to be a marker for predicting SWS intervals, thereby complementing other noninvasive tools and diagnostic efforts.

Dynamic coupling between the central and autonomic nervous systems during sleep: A review

Sleep is characterized by coordinated cortical and cardiac oscillations reflecting communication between the central (CNS) and autonomic (ANS) nervous systems. Here, we review fluctuations in ANS activity in association with CNS-defined sleep stages and cycles, and with phasic cortical events during sleep (e.g., arousals, K-complexes). Recent novel analytic methods reveal a dynamic organization of integrated physiological networks during sleep and indicate how multiple factors (e.g., sleep structure, age, sleep disorders) affect "CNS-ANS coupling". However, these data are mostly correlational and there is a lack of clarity of the underlying physiology, making it challenging to interpret causality and direction of coupling. Experimental manipulations (e.g., evoking K-complexes or arousals) provide information on the precise temporal sequence of cortical-cardiac activity, and are useful for investigating physiological pathways underlying CNS-ANS coupling. With the emergence of new analytical approaches and a renewed interest in ANS and CNS communication during sleep, future work may reveal novel insights into sleep and cardiovascular interactions during health and disease, in which coupling could be adversely impacted.

Altered nocturnal blood pressure profiles in women with insomnia disorder in the menopausal transition

OBJECTIVE

Insomnia disorder is a risk factor for cardiovascular (CV) pathology. It is unknown whether insomnia that develops in the context of the menopausal transition (MT) impacts the CV system. We assessed nocturnal blood pressure (BP) and heart rate (HR) profiles in women with insomnia disorder in the MT.

METHODS

Twelve women meeting DSM-IV criteria for insomnia in the MT (age, mean ± SD: 50.5 ± 3.6 y) and 11 controls (age, mean ± SD: 49.0 ± 3.0 y) had polysomnographic recordings on one or two nights during which beat-to-beat BP and HR were assessed and analyzed hourly from lights-out across the first 6 hours of the night and according to sleep stage. Physiological hot flashes were identified from fluctuations in sternal skin conductance.

RESULTS

Women with insomnia and controls had similar distributions of sleep stages and awakenings/arousals across hours of the night, although insomnia participants tended to have more wakefulness overall. More women in the insomnia group (7 of 12) than in the control group (2 of 11) had at least one physiological hot flash at night (P < 0.05). Both groups showed a drop in BP in the first part of the night; however, systolic and diastolic BP patterns diverged later, remaining low in controls but increasing in insomnia participants 4 to 6 hours after lights-out (P < 0.05). Both groups showed a similar pattern of decline in HR across the night.

CONCLUSIONS

Our findings suggest altered regulatory control of BP during sleep in the MT insomnia. The causes and long-term consequences of this altered nocturnal BP profile remain to be determined.

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac processη) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to theη,δ,θ,α,σ,βtime series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP forδandθbut not forη The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Neuroimaging and Neuromonitoring Effects of Electro and Manual Acupuncture on the Central Nervous System: A Literature Review and Analysis

The aim of this review is to provide an overview of the different effects of manual and electroacupuncture on the central nervous system in studies with different neuroimaging interventions. The Database PubMed was searched from 1/1/2000 to 1/6/2014 with restriction to human studies in English language. Data collection for functional magnetic resonance (fMRI) studies was restricted to the period from 1/1/2010 to 1/6/2014 due to a recently published review which included all published randomized and nonrandomized controlled clinical studies as well as observational studies with control groups, no blinding required. Only studies comparing manual or electroacupuncture with sham acupuncture were eligible. All participants were healthy adult men and women. A majority of 25 studies compared manual versus sham, a minority of 7 trials compared electro versus sham and only 1 study compared electro versus manual acupuncture. In 29 out of 33 studies verum acupuncture results were found to present either more or different modulation effects on neurological components measured by neuroimaging and neuromonitoring methods than sham acupuncture. Only four studies reported no effects of verum in comparison to sham acupuncture. Evaluation of the very heterogeneous results shows evidence that verum acupuncture elicits more modulation effects on neurological components than sham acupuncture.

Time-varying correlations between delta EEG power and heart rate variability in midlife women: the SWAN Sleep Study

No studies have evaluated the dynamic, time-varying relationship between delta electroencephalographic (EEG) sleep and high frequency heart rate variability (HF-HRV) in women. Delta EEG and HF-HRV were measured during sleep in 197 midlife women (M(age)  = 52.1, SD = 2.2). Delta EEG-HF-HRV correlations in nonrapid eye movement (NREM) sleep were modeled as whole-night averages and as continuous functions of time. The whole-night delta EEG-HF-HRV correlation was positive. The strongest correlations were observed during the first NREM sleep period preceding and following peak delta power. Time-varying correlations between delta EEG-HF-HRV were stronger in participants with sleep-disordered breathing and self-reported insomnia compared to healthy controls. The dynamic interplay between sleep and autonomic activity can be modeled across the night to examine within- and between-participant differences including individuals with and without sleep disorders.

Real-time prediction of disordered breathing events in people with obstructive sleep apnea

PURPOSE

Conventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predicting the onset of disordered breathing events in OSA patients may lead to improved strategies for treating OSA and inform our understanding of underlying disease mechanisms. In this work, we describe a deployable system capable of performing real-time predictions of sleep disordered breathing events in patients diagnosed with OSA, providing a novel approach for gaining insight into OSA pathophysiology, discovering population subgroups, and improving therapies.

METHODS

LArge Memory STorage and Retrieval artificial neural networks with 864 different configurations were applied to polysomnogram records from 64 patients. Wavelet transforms, measures of entropy, and other statistics were applied to six physiological signals to provide network inputs. Approximate statistical tests were used to determine the best performing network for each patient. The most important predictors of disordered breathing events in OSA patients were determined by analyzing internal network parameters.

RESULTS

The average optimized individual prediction sensitivity and specificity were 0.81 and 0.77, respectively. Predictions were better than random guessing for all OSA patients. Analysis of internal network parameters revealed a high degree of heterogeneity among disordered breathing event predictors and may reveal patient subgroups.

CONCLUSIONS

We report the first practical system to predict individual disordered breathing events in a heterogeneous group of patients diagnosed with OSA. The pattern of disordered breathing predictors suggests variable underlying pathophysiological mechanisms and highlights the need for an individualized approach to OSA diagnosis, therapy, and management.

Long-term CPAP treatment partially improves the link between cardiac vagal influence and delta sleep

BACKGROUND

Continuous positive airway pressure (CPAP) treatment improves the risk of cardiovascular events in patients suffering from severe sleep apnea-hypopnea syndrome (SAHS) but its effect on the link between delta power band that is related to deep sleep and the relative cardiac vagal component of heart rate variability, HF(nu) of HRV, is unknown. Therefore, we tested the hypothesis that CPAP restores the link between cardiac autonomic activity and delta sleep across the night.

METHODS

Eight patients suffering from severe SAHS before and after 4 ± 3 years of nasal CPAP treatment were matched with fourteen healthy controls. Sleep EEG and ECG were analysed to obtain spectral sleep and HRV components. Coherence analysis was applied between HF(nu) and delta power bands across the first three sleep cycles.

RESULTS

Sleep characteristics and spectral HRV components were similar between untreated patients, treated patients and controls, with the exception of decreased Rapid Eye Movement duration in untreated patients. Coherence and gain values between HF(nu) and delta EEG variability were decreased in untreated patients while gain values normalized in treated patients. In patients before and during long-term CPAP treatment, phase shift and delay between modifications in HF(nu) and delta EEG variability did not differ from controls but were not different from zero. In healthy men, changes in cardiac vagal activity appeared 9 ± 7 minutes before modifications in delta sleep.

CONCLUSIONS

Long-term nasal CPAP restored, in severe SAHS, the information between cardiovascular and sleep brainstem structures by increasing gain, but did not improve its tightness or time shift.

Sleep and cardiovascular regulation

Normal sleep has a profound effect on the cardiovascular system, reducing cardiovascular activity throughout non-rapid eye movement sleep; changes that are modified and augmented by circadian system influence. There is also evidence that sleep-initiated changes in autonomic balance may in turn modify the development of sleep within a night, particularly the development of slow wave sleep. It is assumed that the cardiovascular changes that accompany sleep reflect a functional aspect of sleep, although the precise functional role has not been agreed upon. Nevertheless, there is good evidence that the cardiovascular changes that occur during normal sleep are beneficial for the cardiovascular system. Arousals from sleep, which are common even in normal sleep, are associated with a surge in activity in cardiorespiratory systems, with marked effects on the sleep-related pattern of cardiovascular activity when they occur frequently. Despite the importance of this aspect of sleep, controversy remains as to both the nature of the activation response and the circumstances under which it is elicited. The concept that sleep-related changes in cardiovascular activity are beneficial leads to the corollary that sleep disturbance would result in adverse cardiovascular consequences. While there is strong empirical evidence for such a relationship, it remains unclear whether this is a direct effect or, as has been suggested recently, the effect of disturbed sleep is mediated via stress-related modification of neuroendocrine systems.

EEG delta oscillations as a correlate of basic homeostatic and motivational processes

Functional significance of delta oscillations is not fully understood. One way to approach this question would be from an evolutionary perspective. Delta oscillations dominate the EEG of waking reptiles. In humans, they are prominent only in early developmental stages and during slow-wave sleep. Increase of delta power has been documented in a wide array of developmental disorders and pathological conditions. Considerable evidence on the association between delta waves and autonomic and metabolic processes hints that they may be involved in integration of cerebral activity with homeostatic processes. Much evidence suggests the involvement of delta oscillations in motivation. They increase during hunger, sexual arousal, and in substance users. They also increase during panic attacks and sustained pain. In cognitive domain, they are implicated in attention, salience detection, and subliminal perception. This evidence shows that delta oscillations are associated with evolutionary old basic processes, which in waking adults are overshadowed by more advanced processes associated with higher frequency oscillations. The former processes rise in activity, however, when the latter are dysfunctional.

Heart rate variability, sleep and sleep disorders

Heart rate (HR) is modulated by the combined effects of the sympathetic and parasympathetic nervous systems. Therefore, measurement of changes in HR over time (heart rate variability or HRV) provides information about autonomic functioning. HRV has been used to identify high risk people, understand the autonomic components of different disorders and to evaluate the effect of different interventions, etc. Since the signal required to measure HRV is already being collected on the electrocardiogram (ECG) channel of the polysomnogram (PSG), collecting data for research on HRV and sleep is straightforward, but applications have been limited. As reviewed here, HRV has been applied to understand autonomic changes during different sleep stages. It has also been applied to understand the effect of sleep-disordered breathing, periodic limb movements and insomnia both during sleep and during the daytime. HRV has been successfully used to screen people for possible referral to a Sleep Lab. It has also been used to monitor the effects of continuous positive airway pressure (CPAP). A novel HRV measure, cardiopulmonary coupling (CPC) has been proposed for sleep quality. Evidence also suggests that HRV collected during a PSG can be used in risk stratification models, at least for older adults. Caveats for accurate interpretation of HRV are also presented.

Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF(nu), HF(nu) and LF/HF) features from the spectral analysis. The statistical analysis in different sleep stages highlighted that in sleep apnoea patients, the EEG delta, sigma and beta bands exhibited a strong correlation with HRV features. Then the correlation between EEG frequency bands and HRV features were examined for sleep apnoea classification using univariate and multivariate Gaussian models (UGs and MGs). The MG outperformed the UG in the classification. When EEG and HRV features were combined and modelled with MG, we achieved 64% correct classification accuracy, which is 2 or 8% improvement with respect to using only EEG or ECG features. When delta and acceleration coefficients of the EEG features were incorporated, then the overall accuracy improved to 71%.

Effects of sildenafil on autonomic nervous function during sleep in obstructive sleep apnea

OBJECTIVE

To evaluate the effects of sildenafil on the autonomic nervous system in patients with severe obstructive sleep apnea.

METHODS

Thirteen male patients with severe obstructive sleep apnea (mean age 43+/-10 years with a mean body mass index of 26.7+/-1.9 kg/m(2)) received a single 50-mg dose of sildenafil or a placebo at bedtime. All-night polysomnography and heart rate variability were recorded. Frequency domain analysis of heart rate variability was performed for the central five-minute sample of the longest uninterrupted interval of slow wave and rapid eye movement sleep, as well as for one-minute samples during apnea and during slow wave and rapid eye movement sleep after resumption of respiration.

RESULTS

Compared to the placebo, sildenafil was associated with an increase in the normalized high-frequency (HF(nu)) components and a decrease in the low/high-frequency components of the heart rate variability ratio (LF/HF) in slow wave sleep (p<0.01 for both). Differences in heart rate variability parameters between one-minute post-apnea and apnea samples (Delta = difference between resumption of respiration and apnea) were assessed. A trend toward a decreasing magnitude of DeltaLF activity was observed during rapid eye movement sleep with sildenafil in comparison to placebo (p=0.046). Additionally, DeltaLF/HF in SWS and rapid eye movement sleep was correlated with mean desaturation (s(R =) -0.72 and -0.51, respectively, p= 0.01 for both), and DeltaHF(nu) in rapid eye movement sleep was correlated with mean desaturation (s(R=) 0.66, p= 0.02) and the desaturation index (s(R=) 0.58, p = 0.047).

CONCLUSIONS

The decrease in arousal response to apnea/hypopnea events along with the increase in HF(nu) components and decrease in LH/HF components of the heart rate variability ratio during slow wave sleep suggest that, in addition to worsening sleep apnea, sildenafil has potentially immediate cardiac effects in patients with severe obstructive sleep apnea.

Altered interaction between cardiac vagal influence and delta sleep EEG suggests an altered neuroplasticity in patients suffering from major depressive disorder

OBJECTIVE

Major depressive disorder (MDD), which is associated with altered neuroplasticity and increased relative cardiac sympathic activity, enhances the risk of cardiovascular pathologies. Interaction between cardiac sympatho-vagal indexes and delta sleep power is probably altered in MDD.

METHOD

Sleep characteristics and cardiac sympatho-vagal indexes of 10 depressive patients were compared to 10 control men across the first three non-rapid eye movement (NREM)-REM cycles. Interaction between normalized high frequency (HF) and delta power bands was studied using coherence analysis.

RESULTS

Patients showed increased sleep latency, stage 1 and wake durations. No differences in heart rate variabilities were observed: Total power, HF and RR-interval decreased from NREM to REM sleep and wakefulness in both groups. Gain value was lower in patients while coherence and phase shift were similar between groups. Modifications in HF appear 8 min before modifications in delta.

CONCLUSION

Major depressive disorder is related to an altered link between cardiac vagal influence and delta sleep, suggesting disorders in cardiovascular controls and an altered neuroplasticity.

Nonlinear dynamical analysis of the interdependence between central and autonomic nervous systems in neonates during sleep

We present in this paper the results of a study of the interdependence between signal characteristic of the central nervous system (electroencephalography) and the autonomic nervous system (heart rate and respiration) in human neonates during sleep. By using methods from nonlinear dynamical systems theory, we show that there exist significant differences in this interdependence with the sleep stage and the electrodes considered. This paves the way for the application of this methodology in clinical practice to study pathologies where this interdependence is altered, such as the sudden infant death syndrome.

Analysis of coherence between sleep EEG and ECG signals during and after obstructive sleep apnea events

This study presents the first successful preliminary attempt to directly investigate the interactions of power spectra of sleep EEG and ECG signals of patients with obstructive sleep apnea syndrome (OSAS) by coherence analysis. ECG and EEG signals were collected from 8 OSAS patients and 3 healthy subjects. Coherence between two signals over different frequency bands(0-128 Hz) were calculated for normal breathing events, obstructive sleep apnea (OSA) events and events following OSA terminations (with/without arousals) in non-REM as well as REM sleep. Overall coherence of ECG and EEG in REM sleep is higher than that in non-REM sleep. A significant (p=0.0164) difference of coherence in the range of 10-5 Hz was found among normal, OSA and termination events in REM sleep. The results could be useful in detecting OSA events or OSA related arousals to characterize sleep fragmentation from ECG and EEG signals.

Correlation of sleep EEG frequency bands and Heart Rate Variability

Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). This paper presents a pilot study result of assessing the correlation between EEG frequency bands and ECG Heart Rate Variability (HRV) in normal and sleep apnoea human clinical patients at different sleep stages. In sleep apnoea patients, the results have shown that EEG delta, sigma and beta bands exhibited a strong correlation with cardiac HRV parameters at different sleep stages.

A preliminary study on the influence of obstructive sleep apnea upon cumulative parasympathetic system activity

OBJECTIVE

Although the autonomic nervous system plays a key role in mediating cardiovascular changes during obstructive sleep apnea (OSA), parasympathetic nervous system (PNS) activity during sleep apnea has not yet been sufficiently investigated. This study is to discuss the relationship between PNS activity and OSA.

METHODS

Polysomnography recording was carried out in 76 patients (71 male and 5 female) with OSA. Cumulative PNS activity during sleep for each patient was derived from time series data of electrocardiogram (ECG) and analyzed by coarse graining spectral analysis of heart rate variability. The correlation between cumulative PNS activity and apnea-hypopnea index (AHI) was then discussed.

RESULTS

Cumulative PNS activity and PNS peaks during sleep were lowly but significantly correlated with OSA severity (r=-0.344, p<0.005; and r=-0.266, p<0.05 respectively), and a linear regression equation could be established. Furthermore, significant correlation was also observed in the adult groups and in the moderate and severe groups, but not in the juvenile and the elderly and mild groups.

CONCLUSION

These findings indicated that PNS function was obviously influenced by OSA during sleep. Cumulative PNS activity level might also serve as a useful parameter for the evaluation of OSA.

Specific acupuncture sensation correlates with EEGs and autonomic changes in human subjects

Sympathetic overactivation is suggested to be associated with chronic pain syndrome, and acupuncture is frequently applied in therapy for this syndrome. Furthermore, the forebrain including the various cerebral cortices has been implicated in inhibitory and facilitatory control of pain as well as autonomic functions. We investigated relationships among specific sensations induced by acupuncture manipulation, effects on sympathetic and parasympathetic autonomic functions, and EEG changes. An acupuncture needle was inserted into the right trapezius muscle of the subjects, and acupuncture manipulation was repeated to induce specific acupuncture sensation repeatedly while the needle was left in the muscle. Acupuncture manipulation significantly decreased heart rate (HR), and increased systolic blood pressure (SBP). Spectral analysis indicated that acupuncture manipulation significantly decreased low frequency components (LF) of both HR variability (HRV) and SBP variability (SBPV), and significantly reduced ratio of LF to high frequency component (HF) of HRV (LF/HF, index of sympathetic activity). Furthermore, there was a significant negative correlation between changes in LF/HF ratio of HRV and the number of specific acupuncture sensations reported, and a significant positive correlation between HF of HRV and the number of acupuncture sensations. Analyses of EEG data indicated that acupuncture manipulation non-specifically increased power of all spectral bands except the gamma band. Furthermore, changes in HF (index of parasympathetic activity) and total power (overall activity of the autonomic nervous system) of HRV were positively correlated with changes in theta, alpha, and gamma power, while changes in LF of SBPV and LF/HF of HRV were negatively correlated with changes in power of all spectral bands. These results are consistent with the suggestion that autonomic changes induced by manipulation inducing specific acupuncture sensations might be mediated through the central nervous system, especially through the forebrain as shown in EEG changes, and are beneficial to relieve chronic pain by inhibiting sympathetic nervous activity.