Modulation of the Sympatho-Vagal Balance during Sleep: Frequency Domain Study of Heart Rate Variability and Respiration

Sleep and cardiac autonomic modulation in older adults: Insights from an at-home study with auditory deep sleep stimulation

The autonomic nervous system regulates cardiovascular activity during sleep, likely impacting cardiovascular health. Aging, a primary cardiovascular risk factor, is associated with cardiac autonomic disbalance and diminished sleep slow waves. Therefore, slow waves may be linked to aging, autonomic activity and cardiovascular health. However, it is unclear how sleep and slow waves are linked to cardiac autonomic profiles across multiple nights in older adults. We conducted a randomized, crossover trial involving healthy adults aged 62-78 years. Across 2 weeks, we applied auditory stimulation to enhance slow waves and compared it with a SHAM period. We measured sleep parameters using polysomnography and derived heart rate, heart rate variability approximating parasympathetic activity, and blood pulse wave approximating sympathetic activity from a wearable. Here, we report the results of 14 out of 33 enrolled participants, and show that heart rate, heart rate variability and blood pulse wave within sleep stages differ between the first and second half of sleep. Furthermore, baseline slow-wave activity was related to cardiac autonomic activity profiles during sleep. Moreover, we found auditory stimulation to reduce heart rate variability, while heart rate and blood pulse wave remained unchanged. Lastly, within subjects, higher heart rate coincided with increased slow-wave activity, indicating enhanced autonomic activation when slow waves are pronounced. Our study shows the potential of cardiac autonomic markers to offer insights into participants' baseline slow-wave activity when recorded over multiple nights. Furthermore, we highlight that averaging cardiac autonomic parameters across a night may potentially mask dynamic effects of auditory stimulation, potentially playing a role in maintaining a healthy cardiovascular system.

Analysis of changes in heart rate variability after prolonged ultra-high plateau residence in young healthy population: a cross-sectional study

Objective

This study aims to investigate changes in the autonomic nervous system (ANS) by analyzing the characteristics of heart rate variability (HRV).

Methods

A portable 3-lead dynamic electrocardiogram monitoring device was used to collect HRV data from the participants. Based on the inclusion and exclusion criteria, a total of 52 volunteers from the Xinjiang Hetian area (ultra-high plateau group, approximately 5300 m altitude) and 56 volunteers from the Sichuan Chengdu area (plain group, approximately 500 m altitude) were enrolled for the 24-hour long-term HRV data collection. A cross-sectional comparison was made between the groups in terms of various HRV time-domain, frequency-domain, and nonlinear indices. The diurnal and nocturnal variations in HRV and ANS after prolonged residence in the ultra-high plateau were further explored by dividing the day into daytime and nighttime periods and calculating the ΔHRV values. Additionally, the participants' heart rate and sleep conditions were analyzed.

Results

Compared to the plain group, the ultra-high plateau group showed a significant reduction in overall HRV, with decreased indices of vagal activity (RMSSD, NN50, pNN50, HF, HF norm, and SD1) and increased indices of sympathetic activity (LF norm). The ANS balance indices were increased (LF/HF) and decreased (SD1/SD2), respectively. More importantly, although the diurnal and nocturnal trends of various HRV indices in the ultra-high plateau group were consistent with the plain group, the △HRV value analysis indicated that the ultra-high plateau group had increased △LF (95% CI: 10.20 to 271.60, P = 0.031) and △LF/HF (95% CI: -2.23 to -0.49, P < 0.001), and decreased △HF (95% CI: -383.10 to -35.50, P = 0.012) and △S (95% CI: -12149.47 to -2759.29, P = 0.001). Additionally, in the ultra-high plateau group, both the mean and minimum heart rates were elevated compared to the plain group (84.67 ± 1.37 vs. 73.2 ± 0.93 beats/min and 52.9 ± 1.37 vs. 47.57 ± 0.73 beats/min, respectively, P < 0.001), while the maximum heart rate was reduced (135.21 ± 1.63 vs. 144.43 ± 3.22 beats/min, P = 0.012). Furthermore, the ultra-high plateau group had a significant increase in the number of awakenings (18.27 ± 1.14 vs. 15.34 ± 1.43, P = 0.046) and the Apnea-Hypopnea Index (AHI) (20.14 ± 2.47 vs. 11.36 ± 0.76, P < 0.001).

Conclusion

Prolonged residence in the ultra-high plateau reduces HRV, cardiac reserve capacity, and sleep quality in healthy young adults, diminishes the diurnal recovery capacity of the vagal nerve, and leads to a shift in ANS balance towards reduced vagal activity and enhanced sympathetic activity.

Sleep links hippocampal propensity for epileptiform activity to its viscerosensory inputs

The development of a seizure relies on two factors. One is the existence of an overexcitable neuronal network and the other is a trigger that switches normal activity of that network into a paroxysmal state. While mechanisms of local overexcitation have been the focus of many studies, the process of triggering remains poorly understood. We suggest that, apart from the known exteroceptive sources of reflex epilepsy such as visual, auditory or olfactory signals, there is a range of interoceptive triggers, which are relevant for seizure development in Temporal Lobe Epilepsy (TLE). The hypothesis proposed here aims to explain the prevalence of epileptic activity in sleep and in drowsiness states and to provide a detailed mechanism of seizures triggered by interoceptive signals.

Deep Learning for Pediatric Sleep Staging From Photoplethysmography: A Transfer Learning Approach From Adults to Children

BACKGROUND

Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults. However, for children, algorithm development is hindered by the limited availability of datasets, with the Childhood Adenotonsillectomy Trial (CHAT) being the only substantial source, comprising recordings from children aged 5-10. This limitation constrains the evaluation of algorithmic generalization performance.

METHODS

We employed a deep learning model for sleep staging from PPG, initially trained using a large dataset of adult sleep recordings, and fine-tuned it on 80% of the CHAT dataset (CHAT-train) for the task of three-class sleep staging (wake, REM, non-REM). The resulting algorithm performance was compared to the same model architecture but trained from scratch on CHAT-train (benchmark). The algorithms are evaluated on the local test set, denoted CHAT-test, as well as on a newly introduced independent dataset.

RESULTS

Our deep learning algorithm achieved a Cohen's Kappa of 0.88 on CHAT-test (versus 0.65), and demonstrated generalization capabilities with a Kappa of 0.72 on the external Ichilov dataset for children above 5 years old (versus 0.64) and 0.64 for those below 5 (versus 0.53).

SIGNIFICANCE

This research establishes a new state-of-the-art performance for the task of sleep staging in children using raw PPG. The findings underscore the value of transfer learning from the adults to children domain. However, the reduced performance in children under 5 suggests the need for further research and additional datasets covering a broader pediatric age range to fully address generalization limitations.

REM refines and rescues memory representations: a new theory

Despite extensive evidence on the roles of nonrapid eye movement (NREM) and REM sleep in memory processing, a comprehensive model that integrates their complementary functions remains elusive due to a lack of mechanistic understanding of REM's role in offline memory processing. We present the REM Refining and Rescuing (RnR) Hypothesis, which posits that the principal function of REM sleep is to increase the signal-to-noise ratio within and across memory representations. As such, REM sleep selectively enhances essential nodes within a memory representation while inhibiting the majority (Refine). Additionally, REM sleep modulates weak and strong memory representations so they fall within a similar range of recallability (Rescue). Across multiple NREM-REM cycles, tuning functions of individual memory traces get sharpened, allowing for integration of shared features across representations. We hypothesize that REM sleep's unique cellular, neuromodulatory, and electrophysiological milieu, marked by greater inhibition and a mixed autonomic state of both sympathetic and parasympathetic activity, underpins these processes. The RnR Hypothesis offers a unified framework that explains diverse behavioral and neural outcomes associated with REM sleep, paving the way for future research and a more comprehensive model of sleep-dependent cognitive functions.

A prospective, self-controlled study of sub-plateau heart rate variability in healthy adults

Background

The low-pressure, hypoxic environment characteristic of high-altitude regions significantly affects the cardiovascular and autonomic nervous system functions of individuals, consequently impairing their sleep quality. Heart rate variability, a non-invasive indicator of autonomic nervous system activity and balance within the cardiovascular system, has not been thoroughly investigated in terms of its patterns during acclimatization and de-acclimatization phases for individuals traveling to and residing in high-altitude areas and its relationship with sleep stability.

Methods

Data was collected from 22 medical staff members who traveled from Chengdu to Yecheng, with measurements taken before leaving Chengdu, 1 week in Yecheng, 3 months in Yecheng, and 1 week after returning to Chengdu. The study analyzed changes in heart rate variability during acclimatization and de-acclimatization at 1,400 m above sea level. It also examined arrhythmia and sleep disorders based on circadian groups.

Results

1. Following 1 week of acclimatization to the sub-plateau environment of Yecheng, significant decreases were observed in SDANN, SDNN and SD2 indices compared to departure from Chengdu (P < 0.05). After 3 months of sub-plateau acclimatization, these indices significantly increased (P < 0.05). Upon returning to Chengdu and undergoing de-acclimatization for 1 week, these indices further significantly increased (P < 0.05). 2. During the period of sub-plateau acclimatization and de-acclimatization, significant changes were noted in average heart rate and minimum heart rate (P < 0.05), with the average heart rate showing a continuous decrease and the minimum heart rate exhibiting an initial increase followed by a decrease. No significant changes were observed in maximum heart rate or the incidence of arrhythmias (P > 0.05). 3. When stratified by day and night, the trends for SDANN, RMSSD, and TP were consistent with the overall trend at night (P < 0.05), but opposite during the day (P < 0.05). 4. During the sub-plateau acclimatization period, stable sleep duration was significantly reduced, and increased markedly after de-acclimatization, although it did not return to pre-acclimatization levels (P < 0.05).

Conclusion

Acclimatization to the sub-plateau environment of Yecheng affects the autonomic nervous system, heart rate, and sleep in healthy adults. De-acclimatization can ameliorate these effects. Furthermore, the impact of sub-plateau acclimatization on the autonomic nervous system exhibits a distinct circadian rhythmicity.

Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.

Heart rate variability during sleep onset in patients with insomnia with or without comorbid sleep apnea

OBJECTIVES

Pre-sleep stress or hyperarousal is a known key etiological component in insomnia disorder. Despite this, physiological alterations during the sleep onset are not well-understood. In particular, insomnia and obstructive sleep apnea (OSA) are highly prevalent co-morbid conditions, where autonomic regulation may be altered. We aimed to characterize heart rate variability (HRV) during sleep onset as a potential measure of pre-sleep hyperarousal.

METHODS

We described the profile of pre-sleep HRV measures and explore autonomic differences in participants with self-reported insomnia disorder (with no OSA, n = 69; with mild OSA, n = 70; with moderate or severe OSA, n = 66), compared to normal sleep controls (n = 123). Heart rate data during the sleep onset process were extracted for HRV analyses.

RESULTS

During the sleep onset process, compared to normal sleep controls, participants with insomnia had altered HRV, indicated by higher heart rate (p = 0.004), lower SDNN (p = 0.003), reduced pNN20 (p < 0.001) and pNN50 (p = 0.010) and lower powers (p < 0.001). Participants with insomnia and moderate/severe OSA may have further deteriorated HRV outcomes compared to no/mild OSA patients with insomnia but differences were not significant. Insomnia itself was associated with significantly higher heart rate, lower pNN20, and lower high frequency power even after adjustment for age, gender, BMI and OSA severity.

CONCLUSIONS

Participants with insomnia had lower vagal activity during the sleep onset period, which may be compounded by OSA, reflected in higher heart rates and lower HRV. These altered heart rate dynamics may serve as a physiological biomarker for insomnia during bedtime wakefulness, or as a potential tool to evaluate the efficacy of behavioral interventions which target bedtime stress.

Associations of Subjective Sleep Quality with Wearable Device-Derived Resting Heart Rate During REM Sleep and Non-REM Sleep in a Cohort of Japanese Office Workers

Background

Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa).

Methods

This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary's Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models.

Results

The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (±7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS -0.18; 95% CI -0.61, 0.24) and HR NREMS (HR NREMS -0.23; 95% CI -0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS -0.21 95% CI -0.27, -0.15) and HR NREMS (HR NREMS -0.21 95% CI -0.27, -0.14) after final adjustments for covariates.

Conclusion

The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.

The optimal exercise intervention for sleep quality in adults: A systematic review and network meta-analysis

BACKGROUND

The impact of various exercise modalities on the improvement of sleep quality in adults remains controversial.

OBJECTIVE

This study aimed to perform a network meta-analysis to analyze the effects of different exercise interventions on sleep quality in adults.

METHODS

The PubMed, Cochrane, Embase, Web of Science, and EBSCO databases were searched for studies published from March 18, 1993, to March 18, 2023. The Cochrane risk of bias tool was used to assess the quality of the included studies. Then, a random-effects network meta-analysis was conducted within a frequentist framework.

RESULTS

A total of 2142 participants from 27 randomized controlled trials were included in the analysis. Exercise modalities such as Pilates, yoga, and traditional Chinese exercises were found to significantly improve sleep quality when compared to a no-exercise control group, with Pilates exhibiting the most potent effect at a 95.3% improvement level.

CONCLUSION

This study demonstrates that exercise interventions are effective in enhancing sleep quality in adults. Adapting exercise to individual preferences and needs may maximize the sleep-related benefits of the activity.

REGISTRATION

The review was registered with PROSPERO, registration number CRD42023434565.

InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography

BACKGROUND

This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monitoring with wearable devices. Furthermore, our aim was to develop a more efficient sleep monitoring method by considering both the interpretability and uncertainty of the model's prediction results, with the goal of providing support to medical professionals in their decision-making process.

METHOD

The developed 4-class sleep staging model based on continuous PPG data incorporates several key components: a local attention module, an InceptionTime module, a time-distributed dense layer, a temporal convolutional network (TCN), and a 1D convolutional network (CNN). This model prioritizes both interpretability and uncertainty estimation in its prediction results. The local attention module is introduced to provide insights into the impact of each epoch within the continuous PPG data. It achieves this by leveraging the TCN structure. To quantify the uncertainty of prediction results and facilitate selective predictions, an energy score estimation is employed. By enhancing both the performance and interpretability of the model and taking into consideration the reliability of its predictions, we developed the InsightSleepNet for accurate sleep staging.

RESULT

InsightSleepNet was evaluated using three distinct datasets: MESA, CFS, and CAP. Initially, we assessed the model's classification performance both before and after applying an energy score threshold. We observed a significant improvement in the model's performance with the implementation of the energy score threshold. On the MESA dataset, prior to applying the energy score threshold, the accuracy was 84.2% with a Cohen's kappa of 0.742 and weighted F1 score of 0.842. After implementing the energy score threshold, the accuracy increased to a range of 84.8-86.1%, Cohen's kappa values ranged from 0.75 to 0.78 and weighted F1 scores ranged from 0.848 to 0.861. In the case of the CFS dataset, we also noted enhanced performance. Before the application of the energy score threshold, the accuracy stood at 80.6% with a Cohen's kappa of 0.72 and weighted F1 score of 0.808. After thresholding, the accuracy improved to a range of 81.9-85.6%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.821 to 0.857. Similarly, on the CAP dataset, the initial accuracy was 80.6%, accompanied by a Cohen's kappa of 0.73 and weighted F1 score was 0.805. Following the application of the threshold, the accuracy increased to a range of 81.4-84.3%, Cohen's kappa values ranged from 0.74 to 0.79 and weighted F1 scores ranged from 0.813 to 0.842. Additionally, by interpreting the model's predictions, we obtained results indicating a correlation between the peak of the PPG signal and sleep stage classification.

CONCLUSION

InsightSleepNet is a 4-class sleep staging model that utilizes continuous PPG data, serves the purpose of continuous sleep monitoring with wearable devices. Beyond its primary function, it might facilitate in-depth sleep analysis by medical professionals and empower them with interpretability for intervention-based predictions. This capability can also support well-informed clinical decision-making, providing valuable insights and serving as a reliable second opinion in medical settings.

Acute Effect of Positive Airway Pressure on Heart Rate Variability in Obstructive Sleep Apnea

Autonomic dysregulation is associated with cardiovascular consequences in obstructive sleep apnea (OSA). This study aimed to investigate the effect of acute continuous positive airway pressure (CPAP) treatment on autonomic activity and to identify factors contributing to heart rate variability (HRV) changes in OSA. Frequency domain HRV parameters were calculated and compared between the baseline polysomnography and during the CPAP titration in 402 patients with moderate to severe OSA. There were significant reductions in total power, very low-frequency band power, low-frequency band power, and high-frequency band power during the CPAP titration as compared to the baseline polysomnography. This tendency was pronounced in male patients with severe OSA. Multivariate analysis found that changes in the apnea-hypopnea index and oxygen saturation were significantly associated with changes in sympathetic and parasympathetic activity, respectively. This study demonstrated that HRV parameters significantly changed during the CPAP titration, indicating a beneficial effect of CPAP in the restoration of sympathetic and parasympathetic hyperactivity in OSA. Prospective longitudinal studies should determine whether long-term CPAP treatment aids in maintaining the long-lasting improvement of the autonomic functions, thereby contributing to the prevention of cardiovascular and cerebrovascular diseases in patients with OSA.

Effect of sleep deprivation and NREM sleep stage on physiological brain pulsations

Introduction

Sleep increases brain fluid transport and the power of pulsations driving the fluids. We investigated how sleep deprivation or electrophysiologically different stages of non-rapid-eye-movement (NREM) sleep affect the human brain pulsations.

Methods

Fast functional magnetic resonance imaging (fMRI) was performed in healthy subjects (n = 23) with synchronous electroencephalography (EEG), that was used to verify arousal states (awake, N1 and N2 sleep). Cardiorespiratory rates were verified with physiological monitoring. Spectral power analysis assessed the strength, and spectral entropy assessed the stability of the pulsations.

Results

In N1 sleep, the power of vasomotor (VLF < 0.1 Hz), but not cardiorespiratory pulsations, intensified after sleep deprived vs. non-sleep deprived subjects. The power of all three pulsations increased as a function of arousal state (N2 > N1 > awake) encompassing brain tissue in both sleep stages, but extra-axial CSF spaces only in N2 sleep. Spectral entropy of full band and respiratory pulsations decreased most in N2 sleep stage, while cardiac spectral entropy increased in ventricles.

Discussion

In summary, the sleep deprivation and sleep depth, both increase the power and harmonize the spectral content of human brain pulsations.

Exploring the Gut-Brain Connection in Gastroparesis With Autonomic and Gastric Myoelectric Monitoring

OBJECTIVE

The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology.

METHODS

We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively. From these, we constructed quantitative indices which differentiated the distinct groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores.

RESULTS

We identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes.

CONCLUSION

The differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while at-home with non-invasive sensors.

SIGNIFICANCE

Autonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

Insomnia and Cardiovascular Health: Exploring the Link Between Sleep Disorders and Cardiac Arrhythmias

Cardiovascular diseases (CVDs), driven by modern lifestyles, have increased, with atrial fibrillation (AF) being a major concern linked to heart failure and stroke. Insomnia affects a large population, especially younger individuals, males, and menopausal women, decreasing the quality of life and potentially causing autonomic disturbances and cardiac arrhythmias. PURPOSE OF REVIEW: This review explores the link between insomnia and cardiac arrhythmias, particularly AF, and its impact on cardiovascular health and emphasizes the need to address insomnia in individuals with cardiac arrhythmias by tailored strategies for sleep management to improve their overall well-being. RECENT FINDINGS: Recent findings emphasize maintaining a regular sleep schedule to lower AF and bradyarrhythmia risks. Better sleep scores correlate with reduced AF and bradyarrhythmia risks, while insomnia increases AF risk, particularly in those under 40 years of age. Studies underscore the potential impact of sleep management in reducing cardiovascular risks and highlight the importance of addressing sleep issues to improve cardiovascular health outcomes. Our review presents compelling evidence connecting insomnia and AF. Improving sleep patterns and addressing sleep issues can reduce AF risk, benefiting cardiovascular health. A comprehensive approach for managing at-risk individuals with cardiac arrhythmias, considering co-existing conditions, can decrease long-term disease burden and expenses. Incorporating sleep assessments and interventions into cardiovascular risk management, especially for those with insomnia, is recommended. Further research is needed to fully comprehend the complex relationship between insomnia and cardiac arrhythmias.

Exploring the Potential of Pulse Transit Time as a Biomarker for Sleep Efficiency through a Comparison Analysis with Heart Rate and Heart Rate Variability

The relationship between sleep dynamics and blood pressure (BP) changes is well established. Moreover, sleep efficiency and wakefulness during sleep (WASO) events have a significant impact on BP dipping. Despite this knowledge, there is limited research on the measurement of sleep dynamics and continuous blood pressure (CBP). This study aims to explore the relationship between sleep efficiency and cardiovascular function indicators such as pulse transit time (PTT), as a biomarker of CBP, and heart rate variability (HRV), measured using wearable sensors. The results of the study conducted on 20 participants at the UConn Health Sleep Disorders Center suggest a strong linear relationship between sleep efficiency and changes in PTT (r² = 0.8515) and HRV during sleep (r² = 5886). The findings of this study contribute to our understanding of the relationship between sleep dynamics, CBP, and cardiovascular health.

Functional roles of REM sleep

Rapid eye movement (REM) sleep is an enigmatic and intriguing sleep state. REM sleep differs from non-REM sleep by its characteristic brain activity and from wakefulness by a reduced anti-gravity muscle tone. In addition to these key traits, diverse physiological phenomena appear across the whole body during REM sleep. However, it remains unclear whether these phenomena are the causes or the consequences of REM sleep. Experimental approaches using humans and animal models have gradually revealed the functional roles of REM sleep. Extensive efforts have been made to interpret the characteristic brain activity in the context of memory functions. Numerous physical and psychological functions of REM sleep have also been proposed. Moreover, REM sleep has been implicated in aspects of brain development. Here, we review the variety of functional roles of REM sleep, mainly as revealed by animal models. In addition, we discuss controversies regarding the functional roles of REM sleep.

Experimental study of the negative effects of raised bedroom temperature and reduced ventilation on the sleep quality of elderly subjects

This study investigated the effects of air temperature and ventilation on the sleep quality of elderly subjects and elucidated the mechanisms involved. Sixteen subjects aged over 65 years old were exposed to four conditions in a 2 × 2 design: air temperatures of 27°C and 30°C (with a ceiling fan in operation at 30°C) and two ventilation conditions (with and without mechanical ventilation) in experimental bedrooms. Their electroencephalogram, electrooculogram, chin electromyogram, electrocardiogram, respiration, oxygen saturation, and wrist skin temperature were measured continuously during sleep. Saliva samples were collected, and blood pressure was measured both before and after sleep. The results showed that at the temperature of 30°C, the total sleep time, sleep efficiency, and duration of REM sleep of the elderly decreased by 26.3 min, 5.5%, and 5.3 min, respectively, and time awake increased by 27.0 min, in comparison with 27°C, indicating that the sleep quality of the elderly is very vulnerable to heat exposure. Even a small heat load led to an overactive sympathetic nervous system and increased wrist skin temperature, which reduced sleep quality. Improving the ventilation increased the duration of deep sleep and REM sleep by 10.3 min and 3.7 min, respectively. Higher pollutant concentrations affected the respiration and autonomous nervous systems to reduce sleep quality. The benefits of improved thermal environment and ventilation on sleep quality were found to be additive. Good ventilation and the avoidance of raised temperatures in the bedroom are thus both important for the sleep quality of the elderly.

The multibranched nerve: vagal function beyond heart rate variability

This paper reviews the many functions of the vagus nerve, to understand how they interact in daily life and what might be accomplished by therapeutical electrical stimulation. A short historical introduction on the discovery and name-giving of the cranial nerves numbers 9-12 is followed by an overview of the functions that are under lower brain stem control: heart (rate, contractility), intestine (swallowing, peristalsis and glands secretions, feeling of satiety), lungs (bronchoconstriction, lung-irritant and stretch receptor signaling), blood pressure (by vascular wall stress sensing) and blood gases by specialized receptors. Key in the review is the physiology behind beat-by-beat heart rate variations, how everyday life is reflected in its variability, from exciting moments to quiet sleep, with the 'common faint' or vasovagal collapse as extreme example. Next, the recently proposed role of the vagus nerve in limiting inflammation is discussed. This has led to adoption of an earlier developed technique for epilepsy treatment, i.e., electrical stimulation of one vagus nerve bundle in the neck, but now for immune diseases like rheumatoid arthritis and the scope is even widening to depression and cluster headache. However, the problem in application of whole vagus nerve stimulation is the lack of specificity: there is no way to titrate the stimulation to an observable effect variable. All nerves in the bundle, incoming and outgoing, can be 'hit', leading to side-effects which limit the intended application.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

Somatovisceral Convergence in Sleep-Wake Cycle: Transmitting Different Types of Information via the Same Pathway

Convergence of somatic and visceral inputs occurs at the levels of nervous system ranging from spinal cord to cerebral cortex. This anatomical organization gave explanation to a referred pain phenomenon. However, it also presents a problem: How does the brain know what information is coming for processing-somatic or visceral - if both are transferred by the same spinal cord fibers by means of the standard neuronal spikes? Recent studies provided evidence for cortical processing of interoceptive information largely occurring in sleep, when somatosensation is suppressed, and for the corresponding functional brain networks rearrangement. We suggest that convergent units of the spinal cord would be able to collectively provide mainly somatosensory information in wakefulness and mainly visceral in sleep, solving the puzzle of somatovisceral convergence. We recorded spiking activity from the spinal cord lemniscus pathway during multiple sleep-wake cycles in freely behaving rabbits. In wakefulness high increased spiking corresponded to movements. When animals stopped moving this activity ceased, the fibers remained silent during passive wakefulness. However, upon transition to sleep fibers began firing again. Analysis of spiking patterns of individual fibers revealed that in the majority of them spiking rates recovered in slow wave sleep. Thus, despite cessation of motion and a corresponding decrease of somatic component of the convergent signal, considerable ascending signaling occurs during sleep, that is likely to be visceral. We also recorded evoked responses of the lemniscus pathway to innocuous electrostimulation of the abdominal viscera, and uncovered the existence of two groups of responses depending upon the state of vigilance. Response from an individual fiber could be detected either during wakefulness or in sleep, but not in both states. Wakefulness-responsive group had lower spiking rates in wakefulness and almost stopped spiking in sleep. Sleep-responsive retained substantial spiking during sleep. These groups also differed in spike amplitudes, indicative of fiber diameter differences; however, both had somatic responses during wakefulness. We suggest a mechanism that utilizes differences in somatic and visceral activities to extract both types of information by varying transmission thresholds, and discuss the implications of this mechanism on functional networks under normal and pathological conditions.

Activation of brain-heart axis during REM sleep: a trigger for dreaming

Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. While a dreaming experience implies bodily changes at many organ-, system-, and timescale-levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during REM sleep at night, including high-density EEG and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and non-recalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and non-recalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.

Cortical Responses to Vagus Nerve Stimulation Are Modulated by Brain State in Nonhuman Primates

Vagus nerve stimulation (VNS) has been tested as therapy for several brain disorders and as a means to modulate cortical excitability and brain plasticity. Cortical effects of VNS, manifesting as vagal-evoked potentials (VEPs), are thought to arise from activation of ascending cholinergic and noradrenergic systems. However, it is unknown whether those effects are modulated by brain state at the time of stimulation. In 2 freely behaving macaque monkeys, we delivered short trains of 5 pulses to the left cervical vagus nerve at different frequencies (5-300 Hz) while recording local field potentials (LFPs) from sites in contralateral prefrontal, sensorimotor and parietal cortical areas. Brain states were inferred from spectral components of LFPs and the presence of overt movement: active awake, resting awake, REM sleep and NREM sleep. VNS elicited VEPs in all sampled cortical areas. VEPs comprised early (<70 ms), intermediate (70-250 ms) and late (>250 ms) components. The magnitude of the intermediate and late components was largest during NREM sleep and smallest during wakefulness, whereas that of the early component was not modulated by brain state. VEPs during NREM were larger for stimuli delivered at the depolarized phase of ongoing delta oscillations. Higher pulsing frequencies generated larger VEPs. These short VNS trains did not affect brain state transitions during wakefulness or sleep. Our findings suggest that ongoing brain state modulates the evoked effects of VNS on cortical activity. This has implications for the role of ongoing cortical activity and brain state in shaping cortical responses to peripheral stimuli, for the modulation of vagal interoceptive signaling by cortical activity, and for the dose calibration of VNS therapies.

Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Hemodynamics Changes in the Phase Before, During, and After Sleep Based on Patients’ Sleep Quality in High Care Unit

BACKGROUND: Sleep is a human physiological need that must be fulfilled. Sleep disturbance is generally experienced by hospitalized patients and measured by sleep quality. Sleep disturbance can adversely affect hemodynamic parameters, physiological, and psychological outcome that contribute to the healing of patients. However, few literatures discussing the hemodynamic changes based on the patients’ sleep quality. AIM: The study aimed to describe the hemodynamic changes before, during, and after sleeping phases METHODS: This is an observational analytic quantitative study conducted between February and March 2019 and involved 45 patients. The samples were the conscious patients, aged between 18 and 60 years old (adult) and had been hospitalized for more than 2 days. The Richards-Campbell Sleep Questionnaire was utilized to measure the patients sleep quality, while hemodynamic values were observed by patients’ bedside monitor before, during and after sleep. Data analysis used the Friedman test to determine hemodynamic changes. RESULTS: The results showed that most respondents were female (75.6%), used oxygen (46.7%), sleep in supine position (55.6%), and average age of 35.47 (standard deviation [SD] = 9.581) years old. Patients’ sleep quality score was 44.27 (SD = 22.809), with the average days of treatment were 2.47 days (SD = 694). The average score of Hemodynamic Mean Arterial Pressure (MAP), Heart Rate (HR), and Oxygen saturation (SpO2) before sleeping was 97.64, 94.04, and 94.09, during sleeping was 89.87, 85.00, and 91.22 while after sleeping was 98.27, 97.56, and 97.89, respectively. There was a significant change in HR with p = 0.019, and there was no significant change in the MAP (p = 0.152) and SpO2 (p = 0.149) CONCLUSION: There were variations in hemodynamic score changes before, during, and after sleep, changes in MAP, HR, and SpO2 score within normal ranges. The high hemodynamic changes in the early phase, decrease during sleep, and rise again after sleep. HR is a hemodynamic parameter that significantly changes in those three phases. Monitoring of hemodynamic values in patients could be carried out in the before, during, and after sleep phases to determine the patients’ physiological and psychological condition so as to contribute the healing process.

Cardiac Autonomic Nervous System Activity during Slow Breathing in Supine Position

The purpose of this study is to clarify cardiac autonomic nervous system activity during slow breathing exercises in a supine position. Eighteen healthy young males were participated. Heart rate variability was measured for 5 minutes at rest, 5 minutes at slow breathing, and then 5 minutes at rest. As a result, the LF/HF ratio increased with slow breathing, but HF value did not change. We suggest that the increased LF/HF ratio may be due to increased airway resistance. Cardiac autonomic nervous system activity during slow breathing in the supine position was revealed.

Social Context Influences Resting Physiology in Dogs

Domestication has affected the social life of dogs. They seem to be less dependent on their pack members than wolves, potentially causing dogs to be more alert towards their environment, especially when resting. Such a response has been found in dogs resting alone compared to wolves in the same situation. However, as this may be influenced by social context, we compared alertness (i.e., degree of activation along the sleep-wake continuum-measured via cardiac parameters) of pack-living and enclosure-kept dogs in two conditions: (1) alone, and (2) with pack members, and in two states of activation: (1) inactive wakefulness, and (2) resting. We found that when dogs were resting alone, alertness was higher than when resting in the pack; individual alertness was potentially influenced by social rank. However, alertness was similar in the two conditions during inactive wakefulness. Thus, depending on social context, familiar conspecifics may still provide support in dogs; i.e., domestication has probably only partly shifted the social orientation of dogs from conspecifics to humans. We suggest that cardiac responses of dogs may be more flexible than those of wolves because of their adaptation to the more variable presence of humans and conspecifics in their environment.

The Association of Sleep Duration and Quality with Heart Rate Variability and Blood Pressure

BACKGROUND

The current study was conducted to evaluate the relation of sleep duration and quality with blood pressure (BP) and heart rate variability (HRV).

MATERIALS AND METHODS

This cross-sectional study was carried out in 2017 among 260 staff of a university hospital in Isfahan, Iran. They were selected by multi-stage random method from different wards. Time domain spectral analysis was used to measure a number of HRV parameters. The long-term components of the HRV were estimated using the standard deviation of the normal-to-normal interval (SDNN). The square root of the mean squared differences of successive NN intervals (RMSSD) was calculated by statistical time domain measurements; SNN50, and PNN50 were measured. Pittsburg sleep quality index (PSQI) questionnaire was used to assess sleep quality.

RESULTS

Higher PSQI score correlated with lower SDANN rise (OR=0.92). Fairly bad to very good subjective sleep quality had association with lower SDANN (OR=0.43). Very high sleep latency to very low sleep latency ratio had association with lower SDANN (OR=0.39) and lower PNN50 (OR= 0.44). Sleep duration and HRV parameters had no significant association. Fairly bad sleep efficiency to very good sleep efficiency ratio was correlated with lower SDANN (OR= 0.29). Very high daytime dysfunction to very low daytime dysfunction ratio had correlation with lower SDANN (OR=0.35). Very bad compared to very good subjective sleep quality had significant correlation with higher Heart rate (HR) (B=0.03). Very high sleep latency compared to no sleep latency was associated with higher HR (B=4.74). Very high compared to very low amount of sleep disturbances correlated with higher SBP levels (B=15.2). Using sleep medication less than once a week compared with no history of taking such drugs was associated with higher HR (B=16.4).

CONCLUSION

Our findings showed that poor sleep quality are adversely associated with HRV, HR and BP. This finding should be considered in clinical and preventive recommendations.

Nocturnal heart rate variability moderates the association between sleep-wake regularity and mood in young adults

STUDY OBJECTIVES

Sleep-wake regularity (SWR) is often disrupted in college students and mood disorders are rife at this age. Disrupted SWR can cause repetitive and long-term misalignment between environmental and behavioral cycles and the circadian system which may then have psychological and physical health consequences. We tested whether SWR was independently associated with mood and autonomic function in a healthy adult cohort.

METHODS

We studied 42 college students over a 3 week period using daily sleep-wake diaries and continuous electrocardiogram recordings. Weekly SWR was quantified by the interdaily stability of sleep-wake times (ISSW) and mood was assessed weekly using the Beck Depression Inventory-II. To assess autonomic function, we quantified the high-frequency (HF) power of heart rate variability (HRV). Linear mixed effects models were used to assess the relationship between repeated weekly measures of mood, SWR, and HF.

RESULTS

Low weekly ISSW predicted subsequent poor mood and worsening mood independently of age, sex, race, sleep duration, and physical activity. Although no association was found between ISSW and HF, the ISSW-mood association was significantly moderated by nocturnal HF, i.e. reported mood was lowest after a week with low ISSW and high HF. Prior week mood scores did not significantly predict the subsequent week's ISSW.

CONCLUSIONS

Irregular sleep-wake timing appears to precede poor mood in young adults. Further work is needed to understand the implications of high nocturnal HRV in those with low mood and irregular sleep-wake cycles.

Alterations in EEG connectivity in healthy young adults provide an indicator of sleep depth

Current sleep analyses have used electroencephalography (EEG) to establish sleep intensity through linear and nonlinear measures. Slow wave activity (SWA) and entropy are the most commonly used markers of sleep depth. The purpose of this study is to evaluate changes in brain EEG connectivity during sleep in healthy subjects and compare them with SWA and entropy. Four different connectivity metrics: coherence (MSC), synchronization likelihood (SL), cross mutual information function (CMIF), and phase locking value (PLV), were computed focusing on their correlation with sleep depth. These measures provide different information and perspectives about functional connectivity. All connectivity measures revealed to have functional changes between the different sleep stages. The averaged CMIF seemed to be a more robust connectivity metric to measure sleep depth (correlations of 0.78 and 0.84 with SWA and entropy, respectively), translating greater linear and nonlinear interdependences between brain regions especially during slow wave sleep. Potential changes of brain connectivity were also assessed throughout the night. Connectivity measures indicated a reduction of functional connectivity in N2 as sleep progresses. The validation of connectivity indexes is necessary because they can reveal the interaction between different brain regions in physiological and pathological conditions and help understand the different functions of deep sleep in humans.

The Inconsistent Nature of Heart Rate Variability During Sleep in Normal Children and Adolescents

Introduction: Cardiac function is modulated by multiple factors including exogenous (circadian rhythm) and endogenous (ultradian 90–110 min sleep cycle) factors. By evaluating heart rate variability (HRV) during sleep, we will better understand their influence on cardiac activity. The aim of this study was to evaluate HRV in the dark phase of the circadian rhythm during sleep in healthy children and adolescents.

Methods: One 3 min segment of pre-sleep electrocardiography (EEG) and 3, 6 min segments of electrocardiography recorded during polysomnography from 75 healthy children and adolescents were sampled during progressive cycles of slow wave sleep (SWS1, SWS2, SWS3). Three, 3 min segments of rapid eye movement sleep (REM) were also assessed, with REM1 marked at the last REM period before awakening. Studies that recorded REM3 prior to SWS3 were used for assessment. HRV variables include the following time domain values: mean NN (average RR intervals over given time), SDNN (Standard Deviation of RR intervals), and RMSSD (root Mean Square of beat-to-beat Differences). Frequency domain values include: low frequency (LF), high frequency (HF), and LF:HF.

Results: Mixed linear effects model analysis revealed a significant difference in time and frequency domain values between sleep cycles and stages. Mean NN was lowest (highest heart rate) during pre—sleep then significantly increased across SWS1-3. Mean NN in SWS1 was similar to all REM periods which was significantly lower than both SWS2 and SWS3. SDNN remained at pre-sleep levels until SWS3, and then significantly increased in REM1&2. There was a large drop in LF from pre-sleep to SWS1. As cycles progressed through the night, LF remains lower than awake but increases to awake like levels by REM2. RMSSD and HF were lowest in pre-sleep and increased significantly by SWS1 and remain high and stable across stages and cycles except during the REM3 period where RMSSD decreased.

Conclusion: Our results demonstrate that there are considerable changes in the spectral analysis of cardiac function occurring during different sleep stages and between sleep cycles across the night. Hence, time of night and sleep stage need to be considered when reporting any HRV differences.

The Relationship between Sleep Bruxism Intensity and Renalase Concentration-An Enzyme Involved in Hypertension Development

Background and objectives: Renalase, a novel amine oxidase, is involved in the development of hypertension. Sleep bruxism (SB) is a sleep-related behavior characterized by rhythmic or non-rhythmic activity of the masticatory muscles, which leads to the mechanical wear of teeth, pain in the masticatory muscles, and disturbed sleep. Recent studies indicate that SB plays a role in increased blood pressure. Therefore, this study aimed to determine the relationship between sleep bruxism intensity and renalase concentration, which may help in the future to elucidate the pathogenesis of hypertension and other cardiovascular disorders. Material and methods: SB was evaluated in 87 adult patients using single-night diagnostic polysomnography with video and audio recordings, and the episodes of bruxism were scored according to the standards of the American Academy of Sleep Medicine. The levels of serum renalase were measured in the patients using enzyme-linked immunosorbent assay kits. Results: SB (Bruxism Episode Index (BEI) ≥2) was diagnosed in 54% (n = 47) of the studied population, and the mean concentration of renalase was found to be decreased in the hypertensive group compared with the normotensive group (133.33 ± 160.71 vs 219.23 ± 220.58, p = 0.047). In addition, a linear negative correlation was observed between the renalase concentration and the body mass index (BMI) in the SB group (r = 0.38, p < 0.05) but not in controls. Thus, higher BEI and higher BMI were identified as factors independently associated with the lower concentration of renalase, but only in the group of patients which had a blood renalase concentration of >212.5 ng/mL. Conclusion: There exists an association between renalase concentration and SB intensity, and further studies are needed to clarify the role of renalase in the pathogenesis of hypertension and other cardiovascular disorders.

Wake-up stroke: From pathophysiology to management

Wake-up strokes (WUS) are strokes with unknown exact time of onset as they are noted on awakening by the patients. They represent 20% of all ischemic strokes. The chronobiological pattern of ischemic stroke onset, with higher frequency in the first morning hours, is likely to be associated with circadian fluctuations in blood pressure, heart rate, hemostatic processes, and the occurrence of atrial fibrillation episodes. The modulation of stroke onset time also involves the sleep-wake cycle as there is an increased risk associated with rapid-eye-movement sleep. Furthermore, sleep may have an impact on the expression and perception of stroke symptoms by patients, but also on brain tissue ischemia processes via a neuroprotective effect. Obstructive sleep apnea syndrome is particularly prevalent in WUS patients. Until recently, WUS was considered as a contra-indication to reperfusion therapy because of the unknown onset time and the potential cerebral bleeding risk associated with thrombolytic treatment. A renewed interest in WUS has been observed over the past few years related to an improved radiological evaluation of WUS patients and the recent demonstration of the clinical efficacy of reperfusion in selected patients when the presence of salvageable brain tissue on advanced cerebral imaging is demonstrated.

Does socio-ecology drive differences in alertness between wolves and dogs when resting?

Variation in resting behaviour across animals may be driven by adaptations towards their environment. Wolves and dogs seem promising models to examine this idea as they share a common ancestor, but occupy different socio-ecological niches. While wolves generally avoid humans, hunt, defend their territory, and raise offspring cooperatively, most dogs live in human-shaped environments. Hence, we hypothesized wolves to be more alert towards their environment than dogs, i.e. the degree of activation along the sleep-wake continuum (alertness) should be greater in wolves than in dogs. We estimated alertness via cardiac output. We tested similarly raised and kept pack-living wolves and dogs in two different behavioural conditions: (1) inactive wakefulness: animal is lying, head in an upward position with eyes opened, (2) resting: animal is lying, head in downward position with eyes mainly closed. In contrast to our expectations, we found that in both conditions wolves had a lower heart rate and higher heart rate variability than dogs, i.e. wolves might be less alert/more relaxed than dogs. Although our results are preliminary, we suggest that the higher alertness of dogs compared to wolves is potentially driven by differences in their socio-ecology (i.e. domestication) causing greater attention of dogs to human behaviour.

Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection

This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness.

Remembering to Forget: A Dual Role for Sleep Oscillations in Memory Consolidation and Forgetting

It has been known since the time of patient H. M. and Karl Lashley's equipotentiality studies that the hippocampus and cortex serve mnestic functions. Current memory models maintain that these two brain structures accomplish unique, but interactive, memory functions. Specifically, most modeling suggests that memories are rapidly acquired during waking experience by the hippocampus, before being later consolidated into the cortex for long-term storage. Sleep has been shown to be critical for the transfer and consolidation of memories in the cortex. Like memory consolidation, a role for sleep in adaptive forgetting has both historical precedent, as Francis Crick suggested in 1983 that sleep was for "reverse-learning," and recent empirical support. In this article I review the evidence indicating that the same brain activity involved in sleep replay associated memory consolidation is responsible for sleep-dependent forgetting. In reviewing the literature, it became clear that both a cellular mechanism for systems consolidation and an agreed upon general, as well as cellular, mechanism for sleep-dependent forgetting is seldom discussed or is lacking. I advocate here for a candidate cellular systems consolidation mechanism wherein changes in calcium kinetics and the activation of consolidative signaling cascades arise from the triple phase locking of non-rapid eye movement sleep (NREMS) slow oscillation, sleep spindle and sharp-wave ripple rhythms. I go on to speculatively consider several sleep stage specific forgetting mechanisms and conclude by discussing a notional function of NREM-rapid eye movement sleep (REMS) cycling. The discussed model argues that the cyclical organization of sleep functions to first lay down and edit and then stabilize and integrate engrams. All things considered, it is increasingly clear that hallmark sleep stage rhythms, including several NREMS oscillations and the REMS hippocampal theta rhythm, serve the dual function of enabling simultaneous memory consolidation and adaptive forgetting. Specifically, the same sleep rhythms that consolidate new memories, in the cortex and hippocampus, simultaneously organize the adaptive forgetting of older memories in these brain regions.

Shift Work and Heart Rate Variability Coherence: Pilot Study Among Nurses

This study used ambient heart rate monitoring among health care workers to determine whether a novel measure of heart rate variability (HRV), as well as sleep disturbances, fatigue, or cognitive performance differed among non-rotating night shift nurses relative to those working permanent day shifts. Continuous ambulatory HRV monitoring was performed among night nurses (n = 11), and a comparison group of permanent day nurses (n = 7), over a 36-h period coinciding with the last two 12-h shifts of each participant's work week. Symptoms and psychomotor vigilance were assessed at the end of the ambient HRV monitoring period, and no differences between shifts were observed. Day nurses exhibited an increase in hourly mean HRV coherence ratios during their sleep period, suggesting a circadian pattern of cardiorespiratory phase coupling, whereas night nurses had no increase in HRV coherence ratios during their sleep period. The HRV coherence patterns were similar to high frequency HRV power among nurses on the same shift. To the authors knowledge, this study was the first to quantify patterns of the HRV coherence ratio among shiftworkers in a non-experimental (work/home) setting. The results suggest a pattern of autonomic dysregulation among night workers during their sleep period relative to those working day shifts. The HRV coherence ratio may serve as a novel indicator of HRV dysregulation among shift workers.

The frequency architecture of brain and brain body oscillations: an analysis

Research on brain oscillations has brought up a picture of coupled oscillators. Some of the most important questions that will be analyzed are, how many frequencies are there, what are the coupling principles, what their functional meaning is, and whether body oscillations follow similar coupling principles. It is argued that physiologically, two basic coupling principles govern brain as well as body oscillations: (i) amplitude (envelope) modulation between any frequencies m and n, where the phase of the slower frequency m modulates the envelope of the faster frequency n, and (ii) phase coupling between m and n, where the frequency of n is a harmonic multiple of m. An analysis of the center frequency of traditional frequency bands and their coupling principles suggest a binary hierarchy of frequencies. This principle leads to the foundation of the binary hierarchy brain body oscillation theory. Its central hypotheses are that the frequencies of body oscillations can be predicted from brain oscillations and that brain and body oscillations are aligned to each other. The empirical evaluation of the predicted frequencies for body oscillations is discussed on the basis of findings for heart rate, heart rate variability, breathing frequencies, fluctuations in the BOLD signal, and other body oscillations. The conclusion is that brain and many body oscillations can be described by a single system, where the cross talk - reflecting communication - within and between brain and body oscillations is governed by m : n phase to envelope and phase to phase coupling.

Optimization of Vagal Stimulation Protocol Based on Spontaneous Breathing Rate

Controlled breathing maneuver is being widely applied for cardiovascular autonomic control evaluation and cardiac vagal activation through reduction of breathing rate (BR). However, this maneuver presented contradictory results depending on the protocol and the chosen BR. These variations may be related to the individual intrinsic profile baseline sympathetic tonus, as described before by others. In this study, we evaluated the effect of controlled breathing maneuver on cardiovascular autonomic control in 26 healthy subjects allocated into two protocols: (1) controlled breathing in three different rates (10, 15, and 20 breaths/min) and (2) controlled breathing in rates normalized by the individual spontaneous breathing rate (SBR) at 100, 80, 70, and 50%. Our results showed autonomic responses favorable to vagal modulation with the lower BR maneuvers. Nevertheless, while this activation was variable using the standard protocol, all participants of the normalized protocol demonstrated an increase of vagal modulation at 80% BR (HFnu 80 = 67.5% vs. 48.2%, p < 0.0001). These results suggest that controlled breathing protocols to induce vagal activation should consider the SBR, being limited to values moderately lower than the baseline.

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.

Autonomic regulation during sleep and wakefulness: a review with implications for defining the pathophysiology of neurological disorders

Cardiovascular and respiratory parameters change during sleep and wakefulness. This observation underscores an important, albeit incompletely understood, role for the central nervous system in the differential regulation of autonomic functions. Understanding sleep/wake-dependent sympathetic modulations provides insights into diseases involving autonomic dysfunction. The purpose of this review was to define the central nervous system nuclei regulating sleep and cardiovascular function and to identify reciprocal networks that may underlie autonomic symptoms of disorders such as insomnia, sleep apnea, restless leg syndrome, rapid eye movement sleep behavior disorder, and narcolepsy/cataplexy. In this review, we examine the functional and anatomical significance of hypothalamic, pontine, and medullary networks on sleep, cardiovascular function, and breathing.

Top Mysteries of the Mind: Insights From the Default Space Model of Consciousness

Aside from the nature of consciousness itself, there are still many unsolved problems in the neurosciences. Despite the vast and quickly growing body of work in this field, we still find ourselves perplexed at seemingly simple qualities of our mental being such as why we need to sleep. The neurosciences are at least beginning to take a hold on these mysteries and are working toward solving them. We hold a perspective that metastable consciousness models, specifically the Default Space Model (DSM), provide insights into these mysteries. In this perspective article, we explore some of these curious questions in order to elucidate the interesting points they bring up. The DSM is a dynamic, global theory of consciousness that involves the maintenance of an internal, 3D simulation of the external, physical world which is the foundation and structure of consciousness. This space is created and filled by multiple frequencies of membrane potential oscillations throughout the brain and body which are organized, synchronized and harmonized by the thalamus. The veracity of the DSM is highlighted here in its ability to further understanding of some of the most puzzling problems in neuroscience.

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep).

Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm.

Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS.

Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

Self-Regulation of Breathing as an Adjunctive Treatment of Insomnia

Sleep is a quiescent behavioral state during which complex homeostatic functions essential to health and well-being occur. Insomnia is a very common psychiatric disorder leading to a myriad of detrimental effects including loss of concentration, memory, and performance as well as disease. Current pharmaceutical treatments can be expensive, impairing, unhealthy, and habit-forming. Relaxation techniques, such as meditation target the brain and body in contrast to pharmaceutical interventions which solely target neurotransmitter systems in the brain. In this article we present a viewpoint on the treatment of insomnia that techniques of slow, deep breathing (0.1 Hz) in adjunct to sleep hygiene and relaxation therapies may be highly effective in initiating sleep as well as facilitating falling back asleep. The autonomic nervous system is integral to sleep initiation, maintenance, and disruption. Understanding the relationship between the autonomic nervous system and sleep physiology along with the nature of sleep itself remains a challenge to modern science. We present this perspective in light of a prevailing "dysevolution" theory on the pathology of insomnia that proposes hyper-arousal characterized in part by chronic sympathetic hyperactivation and/or parasympathetic hypoactivation disrupts normal sleep onset latency, sleep quality, and sleep duration. We additionally discuss physiological mechanisms responsible for the effectiveness of the breathing treatment we describe. A better understanding of these mechanisms and autonomic pathologies of insomnia may provide support for the effectiveness of such techniques and provide relief to sufferers of this health epidemic.

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

BACKGROUND

Normal sleep is associated with typical physiological changes in both the central and autonomic nervous systems. In particular, nocturnal blood pressure dipping has emerged as a strong marker of normal sleep physiology, whereas the absence of dipping or reverse dipping has been associated with cardiovascular risk. However, nocturnal blood pressure is not measured commonly in clinical practice. Heart rate (HR) dipping in sleep may be a similar important marker and is measured routinely in at-home and in-laboratory sleep testing.

METHODS

We performed a retrospective cross-sectional analysis of diagnostic polysomnography in a clinically heterogeneous cohort of n=1047 adults without sleep apnea.

RESULTS

We found that almost half of the cohort showed an increased HR in stable nonrapid eye movement sleep (NREM) compared to wake, while only 13.5% showed a reduced NREM HR of at least 10% relative to wake. The strongest correlates of HR dipping were younger age and male sex, whereas the periodic limb movement index (PLMI), sleep quality, and Epworth Sleepiness Scale (ESS) scores were not correlated with HR dipping. PLMI was however significantly correlated with metrics of impaired HR variability (HRV): increased low-frequency power and reduced high-frequency power. HRV metrics were unrelated to sleep quality or the ESS value. Following the work of Vgontzas et al, we also analyzed the sub-cohort with insomnia symptoms and short objective sleep duration. Interestingly, the sleep-wake stage-specific HR values depended upon insomnia symptoms more than sleep duration.

CONCLUSION

While our work demonstrates heterogeneity in cardiac metrics (HR and HRV), the population analysis suggests that pathological signatures of HR (nondipping and elevation) are common even in this cohort selected for the absence of sleep apnea. Future prospective work in clinical populations will further inform risk stratification and set the stage for testing interventions.

Nocturnal Heart Rate Variability Spectrum Characterization in Preschool Children With Asthmatic Symptoms

Asthma is a chronic lung disease that usually develops during childhood. Despite that symptoms can almost be controlled with medication, early diagnosis is desirable in order to reduce permanent airway obstruction risk. It has been suggested that abnormal parasympathetic nervous system (PSNS) activity might be closely related with the pathogenesis of asthma, and that this PSNS activity could be reflected in cardiac vagal control. In this work, an index to characterize the spectral distribution of the high frequency (HF) component of heart rate variability (HRV), named peakness ($\wp$), is proposed. Three different implementations of $\wp$, based on electrocardiogram (ECG) recordings, impedance pneumography (IP) recordings and a combination of both, were employed in the characterization of a group of preschool children classified attending to their risk of developing asthma. Peakier components were observed in the HF band of those children classified as high-risk ( $p < 0.005$), who also presented reduced sympathvoagal balance. Results suggest that high-risk of developing asthma might be related with a lack of adaptability of PSNS.

Indices of heart rate variability as potential early markers of metabolic stress and compromised regulatory capacity in dried-off high-yielding dairy cows

High performing dairy cows experience distinct metabolic stress during periods of negative energy balance. Subclinical disorders of the cow's energy metabolism facilitate failure of adaptational responses resulting in health problems and reduced performance. The autonomic nervous system (ANS) with its sympathetic and parasympathetic branches plays a predominant role in adaption to inadequate energy and/or fuel availability and mediation of the stress response. Therefore, we hypothesize that indices of heart rate variability (HRV) that reflect ANS activity and sympatho-vagal balance could be early markers of metabolic stress, and possibly useful to predict cows with compromised regulatory capacity. In this study we analysed the autonomic regulation and stress level of 10 pregnant dried-off German Holstein cows before, during and after a 10-h fasting period by using a wide range of HRV parameters. In addition heat production (HP), energy balance, feed intake, rumen fermentative activity, physical activity, non-esterified fatty acids, β-hydroxybutyric acid, cortisol and total ghrelin plasma concentrations, and body temperature (BT) were measured. In all cows fasting induced immediate regulatory adjustments including increased lipolysis (84%) and total ghrelin levels (179%), reduction of HP (-16%), standing time (-38%) and heart rate (-15%). However, by analysing frequency domain parameters of HRV (high-frequency (HF) and low-frequency (LF) components, ratio LF/HF) cows could be retrospectively assigned to groups reacting to food removal with increased or decreased activity of the parasympathetic branch of the ANS. Regression analysis reveals that under control conditions (feeding ad libitum) group differences were best predicted by the nonlinear domain HRV component Maxline (L MAX, R 2=0.76, threshold; TS=258). Compared with cows having L MAX values above TS (>L MAX: 348±17), those with L MAX values below TS (<L MAX: 109±26) had higher basal blood cortisol levels, lower concentrations of insulin, and respond to fasting with a shift of their sympatho-vagal balance towards a much stronger dominance of the sympathetic branch of the ANS and development of stress-induced hyperthermia. The data indicate a higher stress level, reduced well-being and restricted regulatory capacity in <L MAX cows. This assumption is in accord with the lower dry matter intake and energy corrected milk yield (16.0±0.7 and 42±2 kg/day) in lactating <L MAX compared with >L MAX cows (18.5±0.4 and 47.3 kg/day). From the present study, it seems conceivable that L MAX can be used as a predictive marker to discover alterations in central autonomic regulation that might precede metabolic disturbances.