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

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.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

Social interaction anxiety and sleep quality in youth: Individual difference in childhood adversity and cardiac vagal control

BACKGROUND

Social interaction anxiety and sleep problems are prevalent during adolescence. Social interaction anxiety undermines sleep quality, however, little is known whether the association between social interaction anxiety and sleep quality is moderated by environmental factors such as childhood adversity and individual factors such as cardiac vagal control. This study sought to investigate the moderating effects of childhood adversity and cardiac vagal control on the link between social interaction anxiety and sleep quality.

METHOD

The Social Interaction Anxiety Scale, the Pittsburgh Sleep Quality Index and the Childhood Trauma Questionnaire were administered to 274 adolescents, who received 3-min resting ECG recording to assess respiratory sinus arrhythmia (RSA) as an index of cardiac vagal control.

RESULTS

Social interaction anxiety was negatively associated with sleep quality, and this association was moderated by childhood adversity and cardiac vagal control. In specific, social interaction anxiety was negatively associated with sleep quality among adolescents with low childhood adversity regardless of cardiac vagal control. Sleep quality was generally disrupted when adolescents exposed to high childhood adversity, but the negative association between social interaction anxiety and sleep quality among adolescents with high childhood adversity could be amortized by high cardiac vagal control.

LIMITATIONS

Cross-sectional design precluded establishing causality among variables.

CONCLUSION

These findings suggest that high cardiac vagal control reflecting better self-regulation might buffer the negative effect of social interaction anxiety on sleep quality particularly among adolescents exposed to early life stress.

Concentration-effect relationships of plasma caffeine on EEG delta power and cardiac autonomic activity during human sleep

Acute caffeine intake affects brain and cardiovascular physiology, yet the concentration-effect relationships on the electroencephalogram and cardiac autonomic activity during sleep are poorly understood. To tackle this question, we simultaneously quantified the plasma caffeine concentration with ultra-high-performance liquid chromatography, as well as the electroencephalogram, heart rate and high-frequency (0.15-0.4 Hz) spectral power in heart rate variability, representing parasympathetic activity, with standard polysomnography during undisturbed human sleep. Twenty-one healthy young men in randomized, double-blind, crossover fashion, ingested 160 mg caffeine or placebo in a delayed, pulsatile-release caffeine formula at their habitual bedtime, and initiated a 4-hr sleep opportunity 4.5 hr later. The mean caffeine levels during sleep exhibited high individual variability between 0.2 and 18.4 μmol L-1 . Across the first two non-rapid-eye-movement (NREM)-rapid-eye-movement sleep cycles, electroencephalogram delta (0.75-2.5 Hz) activity and heart rate were reliably modulated by waking and sleep states. Caffeine dose-dependently reduced delta activity and heart rate, and increased high-frequency heart rate variability in NREM sleep when compared with placebo. The average reduction in heart rate equalled 3.24 ± 0.77 beats per minute. Non-linear statistical models suggest that caffeine levels above ~7.4 μmol L-1 decreased electroencephalogram delta activity, whereas concentrations above ~4.3 μmol L-1 and ~ 4.9 μmol L-1 , respectively, reduced heart rate and increased high-frequency heart rate variability. These findings provide quantitative concentration-effect relationships of caffeine, electroencephalogram delta power and cardiac autonomic activity, and suggest increased parasympathetic activity during sleep after intake of caffeine.

Heart rate variability during sleep in synucleinopathies: a review

Synucleinopathies are a group of neurodegenerative diseases characterized by abnormal accumulations of insoluble alpha-synuclein in neurons or glial cells. These consist of Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Moreover, idiopathic REM sleep behavior disorder (iRBD) is often the first manifestation of synucleinopathies, demonstrating a pathophysiological continuum. While these disorders vary in prevalence, symptom patterns, and severity, they can all include autonomic nervous system (ANS) dysfunction, which significantly reduces quality of life and worsens prognosis. Consequently, identifying abnormalities of the ANS can provide opportunities for improving quality of life through symptomatic treatments that are tailored to the individual's symptoms. An exciting development is using heart rate variability (HRV) as a non-invasive research tool for analyzing how the ANS regulates physiological processes. HRV during sleep, however, may provide a more accurate and reliable measure of ANS activity than during wakefulness, as during awake time, ANS activity is influenced by a variety of factors, including physical activity, stress, and emotions, which may mask or confound the underlying patterns of ANS activity. This review aims to provide an overview of the current knowledge regarding sleep-related HRV in synucleinopathies and to discuss contributing mechanisms. Evidence suggests that iRBD, PD, and MSA are associated with nocturnal ANS dysfunction. Further, comparative studies indicate that the presence of RBD could exacerbate this abnormality. In contrast, no studies have been conducted in patients with DLB. Overall, this review provides new insight into the complex interplay between the ANS and synucleinopathies and underscores the need for further research in this area to develop effective therapies to improve sleep and overall quality of life in patients with synucleinopathies.

Effect of Propofol on Heart Rate and Its Coupling to Cortical Slow Waves in Humans

BACKGROUND

Propofol causes significant cardiovascular depression and a slowing of neurophysiological activity. However, literature on its effect on the heart rate remains mixed, and it is not known whether cortical slow waves are related to cardiac activity in propofol anesthesia.

METHODS

The authors performed a secondary analysis of electrocardiographic and electroencephalographic data collected as part of a previously published study where n = 16 healthy volunteers underwent a slow infusion of propofol up to an estimated effect-site concentration of 4 µg/ml. Heart rate, heart rate variability, and individual slow electroencephalographic waves were extracted for each subject. Timing between slow-wave start and the preceding R-wave was tested against a uniform random surrogate. Heart rate data were further examined as a post hoc analysis in n = 96 members of an American Society of Anesthesiologists Physical Status II/III older clinical population collected as part of the AlphaMax trial.

RESULTS

The slow propofol infusion increased the heart rate in a dose-dependent manner (mean ± SD, increase of +4.2 ± 1.5 beats/min/[μg ml-1]; P < 0.001). The effect was smaller but still significant in the older clinical population. In healthy volunteers, propofol decreased the electrocardiogram R-wave amplitude (median [25th to 75th percentile], decrease of -83 [-245 to -28] μV; P < 0.001). Heart rate variability showed a loss of high-frequency parasympathetic activity. Individual cortical slow waves were coupled to the heartbeat. Heartbeat incidence peaked about 450 ms before slow-wave onset, and mean slow-wave frequency correlated with mean heart rate.

CONCLUSIONS

The authors observed a robust increase in heart rate with increasing propofol concentrations in healthy volunteers and patients. This was likely due to decreased parasympathetic cardioinhibition. Similar to non-rapid eye movement sleep, cortical slow waves are coupled to the cardiac rhythm, perhaps due to a common brainstem generator.

EDITOR’S PERSPECTIVE

Heart Rate Variability during Online Video Game Playing in Habitual Gamers: Effects of Internet Addiction Scale, Ranking Score and Gaming Performance

Previous studies have demonstrated that individuals with internet gaming disorder (IGD) display abnormal autonomic activities at rest and during gameplay. Here, we examined whether and how in-game autonomic activity is modulated by human characteristics and behavioral performance of the player. We measured heart rate variability (HRV) in 42 male university student habitual gamers (HGs) when they played a round of League of Legends game online. Short-term HRV indices measured in early, middle and late phases of the game were compared between the players at high risk of developing IGD and those at low risk, as assessed by the revised Chen Internet addiction scale (CIAS-R). Multiple linear regression (MLR) was used to identify significant predictors of HRV measured over the whole gameplay period (WG), among CIAS-R, ranking score, hours of weekly playing and selected in-game performance parameters. The high-risk players showed a significantly higher low-frequency power/high-frequency power ratio (LF/HF) relative to the low-risk players, regardless of game phase. MLR analysis revealed that LF/HF measured in WG was predicted by, and only by, CIAS-R. The HRV indicators of sympathetic activity were found to be predicted only by the number of slain in WG (NSlain), and the indicators of parasympathetic activity were predicted by both CIAS-R and NSlain. Collectively, the results demonstrated that risk of developing IGD is associated with dysregulated autonomic balance during gameplay, and in-game autonomic activities are modulated by complex interactions among personal attributes and in-game behavioral performance of the player, as well as situational factors embedded in game mechanics.

The association between sleep duration and muscle sympathetic nerve activity

PURPOSE

Sleep duration is associated with risk of hypertension and cardiovascular diseases. It is thought that shorter sleep increases sympathetic activity. However, most studies are based on acute experimental sleep deprivation that have produced conflicting results. Furthermore, there are limited data available on habitual sleep duration and gold-standard measures of sympathetic activation. Hence, this study aimed to assess the association between habitual sleep duration and muscle sympathetic nerve activity.

METHODS

Twenty-four participants aged ≥ 18 years were included in the study. Sleep was assessed using at-home 7-day/night actigraphy (ActiGraph™ GT3X-BT) and sleep questionnaires (Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale). Microelectrode recordings of muscle sympathetic nerve activity were obtained from the common peroneal nerve. Participants were categorised into shorter or longer sleep duration groups using a median split of self-report and actigraphy sleep measures.

RESULTS

Compared to longer sleepers, shorter sleepers averaged 99 ± 40 min and 82 ± 40 min less sleep per night as assessed by self-report and objective measures, respectively. There were no differences in age (38 ± 18 vs 39 ± 21 years), sex (5 male, 7 female vs 6 male, 6 female), or body mass index (23 ± 3 vs 22 ± 3 kg/m2) between shorter and longer sleepers. Expressed as burst frequency, muscle sympathetic nerve activity was higher in shorter versus longer sleepers for both self-report (39.4 ± 12.9 vs 28.4 ± 8.5 bursts/min, p = 0.019) and objective (37.9 ± 12.4 vs 28.1 ± 8.8 bursts/min, p = 0.036) sleep duration.

CONCLUSIONS

Shorter sleep duration assessed in a home setting was associated with higher muscle sympathetic nerve activity. Sympathetic overactivity may underlie the association between short sleep and hypertension.

A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination

Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and intervention of sleep disorders and related diseases. Although previous attempts to classify sleep stages have achieved high classification performance, little attention has been paid to integrating the rich information in brain and heart dynamics during sleep for sleep staging. In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning classifiers. Macro-F1 score, macro-geometric mean, and Cohen kappa value are adopted to evaluate the classification performance of each class in an imbalanced dataset. Experimental results show that EEG features contribute more to wake stage classification while ECG features contribute more to deep sleep stages. The proposed combination achieves the highest accuracy of 84.3% and the highest kappa value of 0.794 on the support vector machine in the ISRUC-S3 dataset, suggesting the proposed multimodal features combination is promising in accuracy and efficiency compared to other state-of-the-art methods.

Morning perception of sleep, stress, and mood, and its relationship with overnight physiological sleep: findings from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study

This cross-sectional study investigated objective-subjective sleep discrepancies and the physiological basis for morning perceptions of sleep, mood, and readiness, in adolescents. Data collected during a single in-laboratory polysomnographic assessment from 137 healthy adolescents (61 girls; age range: 12-21 years) in the United States National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study were analysed. Upon awakening, participants completed questionnaires assessing sleep quality, mood, and readiness. We evaluated the relationship between overnight polysomnographic, electroencephalographic, sleep autonomic nervous system functioning measures, and next morning self-reported indices. Results showed that older adolescents reported more awakenings, yet they perceived their sleep to be deeper and less restless than younger adolescents. Prediction models including sleep physiology measures (polysomnographic, electroencephalographic, and sleep autonomic nervous system) explained between 3% and 29% of morning sleep perception, mood, and readiness indices. The subjective experience of sleep is a complex phenomenon with multiple components. Distinct physiological sleep processes contribute to the morning perception of sleep and related measures of mood and readiness. More than 70% of the variance (based on a single observation per person) in the perception of sleep, mood, and morning readiness is not explained by overnight sleep-related physiological measures, suggesting that other factors are important for the subjective sleep experience.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

GOAL AND AIMS

Compare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording. Focus technology: Fitbit Charge 3 and actigraphy. Reference technology: Polysomnography.

SAMPLE

Twenty-one university students (10 females).

DESIGN

Simultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants' homes.

CORE ANALYTICS

Total sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Variability of specificity and negative predictive value across subjects and across nights.

CORE OUTCOMES

Fitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively).

IMPORTANT ADDITIONAL OUTCOMES

Fitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights.

CORE CONCLUSION

This study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

Integrated Biosignal Analysis to Provide Biomarkers for Recognizing Time Perception Difficulties

Background

Time perception refers to the capability to recognize the passage of time. The cerebellum is located at the back of the brain, underlying the occipital and temporal lobes. Dyschronometria is a cerebellar dysfunction, in which a person cannot precisely estimate the amount of time that has passed. Cardiac indicators such as heart rate (HR) variability have been associated with mental function in healthy individuals. Moreover, time perception has been previously studied concerning cardiac signs. Human time perception is influenced by various factors such as attention and drowsiness. An electroencephalogram (EEG) is a suitable modality for evaluating cortical reactions due to its affordability and usefulness. Because EEG has a high sequential outcome, it offers valuable data to explore variability in psychological situations. An electrocardiogram (ECG) records electrical signals from the heart to examine various heart conditions. The electromyography (EMG) technique detects electrical impulses produced by muscles.

Methods

EEG, ECG, and EMG are integrated during time perception. This study evaluated the human body's time perception through the neurological, cardiovascular, and muscular systems using a simple neurofeedback exercise after time perception tasks. The three biosignals which are EEG, ECG, and EMG were investigated to use them as biomarkers for recognizing time perception difficulty as the main goal of the study. Five healthy college students with no health issues participated, and their EEG, ECG, and EMG were recorded while relaxing and performing a time wall estimation task and neurofeedback training. Previous research has shown the relationship between EEG frequency bands and the frontal center during time perception. Investigating the connection between ECG, EEG, and EMG under time perception conditions is significant.

Results

The results show that ECG (HR), EEG (Delta wave), and EMG (root mean square) are critical features in time perception difficulties.

Conclusion

The ability and outcomes of multiple biomarkers might allow for improved diagnosis and monitoring of the progress of any treatment applications such as biofeedback training. Furthermore, those biomarkers could be used as useful for evaluating and treating dyschronometria.

Home-Based Measurements of Nocturnal Cardiac Parasympathetic Activity in Athletes during Return to Sport after Sport-Related Concussion

Sport-related concussions (SRC) are characterized by impaired autonomic control. Heart rate variability (HRV) offers easily obtainable diagnostic approaches to SRC-associated dysautonomia, but studies investigating HRV during sleep, a crucial time for post-traumatic cerebral regeneration, are relatively sparse. The aim of this study was to assess nocturnal HRV in athletes during their return to sports (RTS) after SRC in their home environment using wireless wrist sensors (E4, Empatica, Milan, Italy) and to explore possible relations with clinical concussion-associated sleep symptoms. Eighteen SRC athletes wore a wrist sensor obtaining photoplethysmographic data at night during RTS as well as one night after full clinical recovery post RTS (>3 weeks). Nocturnal heart rate and parasympathetic activity of HRV (RMSSD) were calculated and compared using the Mann-Whitney U Test to values of eighteen; matched by sex, age, sport, and expertise, control athletes underwent the identical protocol. During RTS, nocturnal RMSSD of SRC athletes (Mdn = 77.74 ms) showed a trend compared to controls (Mdn = 95.68 ms, p = 0.021, r = -0.382, p adjusted using false discovery rate = 0.126) and positively correlated to "drowsiness" (r = 0.523, p = 0.023, p adjusted = 0.046). Post RTS, no differences in RMSSD between groups were detected. The presented findings in nocturnal cardiac parasympathetic activity during nights of RTS in SRC athletes might be a result of concussion, although its relation to recovery still needs to be elucidated. Utilization of wireless sensors and wearable technologies in home-based settings offer a possibility to obtain helpful objective data in the management of SRC.

Effects of sleep fragmentation and partial sleep restriction on heart rate variability during night

We developed a cross-over study design with two interventions in randomized order to compare the effects of sleep fragmentation and partial sleep restriction on cardiac autonomic tone. Twenty male subjects (40.6 ± 7.5 years old) underwent overnight polysomnography during 2 weeks, each week containing one undisturbed baseline night, one intervention night (either sleep restriction with 5 h of sleep or sleep fragmentation with awakening every hour) and two undisturbed recovery nights. Parameters of heart rate variability (HRV) were used to assess cardiac autonomic modulation during the nights. Sleep restriction showed significant higher heart rate (p = 0.018) and lower HRV-pNN50 (p = 0.012) during sleep stage N1 and lower HRV-SDNN (p = 0.009) during wakefulness compared to the respective baseline. For HR and SDNN there were recovery effects. There was no significant difference comparing fragmentation night and its baseline. Comparing both intervention nights, sleep restriction had lower HRV high frequency (HF) components in stage N1 (p = 0.018) and stage N2 (p = 0.012), lower HRV low frequency (LF) (p = 0.007) regarding the entire night and lower SDNN (p = 0.033) during WASO during sleep. Sleep restriction increases sympathetic tone and decreases vagal tone during night causing increased autonomic stress, while fragmented sleep does not affect cardiac autonomic parameters in our sample.

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.

The past, present, and future of sleep quality assessment and monitoring

Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.

Cardiorespiratory changes associated with micro-arousals during naps

The autonomic nervous system (ANS) and the central nervous system (CNS) interplay during sleep, particularly during phasic events such as micro-arousals, has been the subject of several studies. The underlying mechanisms of such relationship which remain unclear, specifically during daytime sleep, were partly investigated in this study. Napping polysomnography was performed on two occasions at least one week apart in 15 healthy subjects. The following cardiorespiratory variables were extracted from the recordings: tachogram, pulse transit time (PTT), pulse wave amplitude, respiratory cycle amplitude, and frequency. Two experts first detected micro-arousal events, then, cardiorespiratory variables were averaged by 30-s epochs over 2 min centered on the onset of the micro-arousals. We found that in the 30 s preceding the arousal events as detected on the electroencephalogram (EEG) recordings, there was a decrease in tachogram, pulse wave amplitude, and PTT values while the respiratory amplitude increased. These changes were more prominent in stage N2 and N3 sleep than in stage N1. The present findings provide new insights into the autonomic changes during the pre-arousal period in daytime naps, as all the variables investigated suggest a sympathetic physiological origin for the changes.

The Putative Role of Neuroinflammation in the Interaction between Traumatic Brain Injuries, Sleep, Pain and Other Neuropsychiatric Outcomes: A State-of-the-Art Review

Sleep disturbances are widely prevalent following a traumatic brain injury (TBI) and have the potential to contribute to numerous post-traumatic physiological, psychological, and cognitive difficulties developing chronically, including chronic pain. An important pathophysiological mechanism involved in the recovery of TBI is neuroinflammation, which leads to many downstream consequences. While neuroinflammation is a process that can be both beneficial and detrimental to individuals' recovery after sustaining a TBI, recent evidence suggests that neuroinflammation may worsen outcomes in traumatically injured patients, as well as exacerbate the deleterious consequences of sleep disturbances. Additionally, a bidirectional relationship between neuroinflammation and sleep has been described, where neuroinflammation plays a role in sleep regulation and, in turn, poor sleep promotes neuroinflammation. Given the complexity of this interplay, this review aims to clarify the role of neuroinflammation in the relationship between sleep and TBI, with an emphasis on long-term outcomes such as pain, mood disorders, cognitive dysfunctions, and elevated risk of Alzheimer's disease and dementia. In addition, some management strategies and novel treatment targeting sleep and neuroinflammation will be discussed in order to establish an effective approach to mitigate long-term outcomes after TBI.

Tenuifolin ameliorates the sleep deprivation-induced cognitive deficits

Tenuifolin (TEN), a natural neuroprotective compound obtained from the Polygala tenuifolia Willd plant, has improved cognitive symptoms. However, the impact of TEN on memory impairments caused by sleep deprivation (SD) is unclear. Accordingly, the objective of this study was to investigate the mechanisms behind the preventative benefits of TEN on cognitive impairment caused by SD. TEN (10 and 20 mg/kg) and Huperzine A (0.1 mg/kg) were given to mice through oral gavage for 28 days during the SD process. The results indicate that TEN administrations improve short- and long-term memory impairments caused by SD in the Y-maze, object identification, and step-through tests. Moreover, TEN stimulated the generation of anti-inflammatory cytokines (interleukin-10), lowered the production of pro-inflammatory cytokines (interleukin-1β, interleukin-6, and interleukin-18), and activated microglia, improving antioxidant status in the hippocampus. TEN treatments significantly boosted the expression of nuclear factor erythroid 2-related factor 2 and heme oxygenase-1 while considerably decreasing the expression of NOD-like receptor thermal protein domain associated protein 3 and caspase-1 p20. Additionally, TEN restored the downregulation of the brain-derived neurotrophic factor signaling cascade and the impaired hippocampal neurogenesis induced by SD. When considered collectively, our data suggest that TEN is a potentially effective neuroprotective agent for cognition dysfunction.

The complexities of the sleep-pain relationship in adolescents: A critical review

Chronic pain is a common and disabling condition in adolescents. Disturbed sleep is associated with many detrimental effects in adolescents with acute and chronic pain. While sleep and pain are known to share a reciprocal relationship, the sleep-pain relationship in adolescence warrants further contextualization within normally occurring maturation of several biopsychological processes. Since sleep and pain disorders begin to emerge in early adolescence and are often comorbid, there is a need for a comprehensive picture of their interrelation especially related to temporal relationships and mechanistic drivers. While existing reviews provide a solid foundation for the interaction between disturbed sleep and pain in youth, we will extend this review by highlighting current methodological challenges for both sleep and pain assessments, exploring the recent evidence for directionality in the sleep-pain relationship, reviewing potential mechanisms and factors underlying the relationship, and providing direction for future investigations. We will also highlight the potential role of digital technologies in advancing the understanding of the sleep and pain relationship. Ultimately, we anticipate this information will facilitate further research and inform the management of pain and poor sleep, which will ultimately improve the quality of life in adolescents and reduce the risk of pain persisting into adulthood.

Peripheral blood flow estimated by laser doppler flowmetry provides additional information about sleep state beyond that provided by pulse rate variability

Pulse rate variability (PRV), derived from Laser Doppler flowmetry (LDF) or photoplethysmography, has recently become widely used for sleep state assessment, although it cannot identify all the sleep stages. Peripheral blood flow (BF), also estimated by LDF, may be modulated by sleep stages; however, few studies have explored its potential for assessing sleep state. Thus, we aimed to investigate whether peripheral BF could provide information about sleep stages, and thus improve sleep state assessment. We performed electrocardiography and simultaneously recorded BF signals by LDF from the right-index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of polysomnographic recording. Time- and frequency-domain parameters of peripheral BF, and time-domain, frequency-domain, and non-linear indices of PRV and heart rate variability (HRV) were calculated. Finger-BF parameters in the time and frequency domains provided information about different sleep stages, some of which (such as the difference between N1 and rapid eye movement sleep) were not revealed by finger-PRV. In addition, finger-PRV patterns and HRV patterns were similar for most parameters. Further, both finger- and ear-BF results showed 0.2-0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation of respiration within this frequency band. These results showed that peripheral BF could provide information for different sleep stages, some of which was complementary to the information provided by PRV. Furthermore, the combination of peripheral BF and PRV may be more advantageous than HRV alone in assessing sleep states and related autonomic nervous activity.

Application of Piezoelectric PLLA Braided Cord as Wearable Sensor to Realize Monitoring System for Indoor Dogs with Less Physical or Mental Stress

We attempted to realize a prototype system that monitors the living condition of indoor dogs without physical or mental burden by using a piezoelectric poly-l-lactic acid (PLLA) braided cord as a wearable sensor. First, to achieve flexibility and durability of the piezoelectric PLLA braided cord used as a sensor for indoor dogs, the process of manufacturing the piezoelectric PLLA fiber for the piezoelectric braided cord was studied in detail and improved to achieve the required performance. Piezoelectric PLLA braided cords were fabricated from the developed PLLA fibers, and the finite element method was used to realize an e-textile that can effectively function as a monitoring sensor. As a result, we realized an e-textile that feels similar to a high-grade textile and senses the complex movements of indoor dogs without the use of a complex computer system. Finally, a prototype system was constructed and applied to an actual indoor dog to demonstrate the usefulness of the e-textile as a sensor for indoor dog monitoring.

Changes in the cortical network during sleep stage transitions

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Introduction: Autonomic nervous system (ANS) plays an important role in the exchange of metabolic information between organs and regulation on peripheral metabolism with obvious circadian rhythm in a healthy state. Sleep, a vital brain phenomenon, significantly affects both ANS and metabolic function. Objectives: This study investigated the relationships among sleep, ANS and metabolic function in type 2 diabetes mellitus (T2DM), to support the evaluation of ANS function through heart rate variability (HRV) metrics, and the determination of the correlated underlying autonomic pathways, and help optimize the early prevention, post-diagnosis and management of T2DM and its complications. Materials and methods: A total of 64 volunteered inpatients with T2DM took part in this study. 24-h electrocardiogram (ECG), clinical indicators of metabolic function, sleep quality and sleep staging results of T2DM patients were monitored. Results: The associations between sleep quality, 24-h/awake/sleep/sleep staging HRV and clinical indicators of metabolic function were analyzed. Significant correlations were found between sleep quality and metabolic function (|r| = 0.386 ± 0.062, p < 0.05); HRV derived ANS function showed strengthened correlations with metabolic function during sleep period (|r| = 0.474 ± 0.100, p < 0.05); HRV metrics during sleep stages coupled more tightly with clinical indicators of metabolic function [in unstable sleep: |r| = 0.453 ± 0.095, p < 0.05; in stable sleep: |r| = 0.463 ± 0.100, p < 0.05; in rapid eye movement (REM) sleep: |r| = 0.453 ± 0.082, p < 0.05], and showed significant associations with glycemic control in non-linear analysis [fasting blood glucose within 24 h of admission (admission FBG), |r| = 0.420 ± 0.064, p < 0.05; glycated hemoglobin (HbA1c), |r| = 0.417 ± 0.016, p < 0.05]. Conclusions: HRV metrics during sleep period play more distinct role than during awake period in investigating ANS dysfunction and metabolism in T2DM patients, and sleep rhythm based HRV analysis should perform better in ANS and metabolic function assessment, especially for glycemic control in non-linear analysis among T2DM patients.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

Objective

Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence.

Methods

Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance.

Results

InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence.

Conclusions

The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Exploratory Validation of Sleep-Tracking Devices in Patients with Psychiatric Disorders

Purpose

Sleep-tracking devices have performed well in recent studies that evaluated their use in healthy adults by comparing them with the gold standard sleep assessment technique, polysomnography (PSG). These devices have not been validated for use in patients with psychiatric disorders. Therefore, we tested the performance of three sleep-tracking devices against PSG in patients with psychiatric disorders.

Patients and methods

In total, 52 patients (32 women; 48.1 ± 17.2 years, mean ± SD; 18 patients diagnosed with schizophrenia, 19 with depressive disorder, 3 with bipolar disorder, and 12 with sleep disorder cases) were tested in a sleep laboratory with PSG, along with portable electroencephalography (EEG) device (Sleepgraph), actigraphy (MTN-220/221) and consumer sleep-tracking device (Fitbit Sense).

Results

Epoch-by-epoch sensitivity (for sleep) and specificity (for wake), respectively, were as follows: Sleepgraph (0.95, 0.76), Fitbit Sense (0.95, 0.45) and MTN-220/221 (0.93, 0.40). Portable EEG (Sleepgraph) had the best sleep stage-tracking performance. Sleep-wake summary metrics demonstrated lower performance on poor sleep (ice, shorter total sleep time, lower sleep efficiency, longer sleep latency, longer wake after sleep onset).

Conclusion

Devices demonstrated similar sleep-wake detecting performance as compared with previous studies that evaluated sleep in healthy adults. Consumer sleep device may exhibit poor sleep stage-tracking performance in patients with psychiatric disorders due to factors that affect the sleep determination algorithm, such as changes in autonomic nervous system activity. However, Sleepgraph, a portable EEG device, demonstrated higher performance in mental disorders than the Fitbit Sense and actigraphy.

Effects of age and sex on vasomotor activity and baroreflex sensitivity during the sleep-wake cycle

Cardiovascular function is related to age, sex, and state of consciousness. We hypothesized that cardiovagal baroreflex sensitivity (BRS) demonstrates different patterns in both sexes before and after 50 years of age and that these patterns are associated with patterned changes during the sleep-wake cycle. We recruited 67 healthy participants (aged 20-79 years; 41 women) and divided them into four age groups: 20-29, 30-49, 50-69, and 70-79 years. All the participants underwent polysomnography and blood pressure measurements. For each participant, we used the average of the arterial pressure variability, heart rate variability (HRV), and BRS parameters during the sleep-wake stages. BRS and HRV parameters were significantly negatively correlated with age. The BRS indexes were significantly lower in the participants aged ≥ 50 years than in those aged < 50 years, and these age-related declines were more apparent during non-rapid eye movement sleep than during wakefulness. Only BRS demonstrated a significantly negative correlation with age in participants ≥ 50 years old. Women exhibited a stronger association than men between BRS and age and an earlier decline in BRS. Changes in BRS varied with age, sex, and consciousness state, each demonstrating a specific pattern. The age of 50 years appeared to be a crucial turning point for sexual dimorphism in BRS. Baroreflex modulation of the cardiovascular system during sleep sensitively delineated the age- and sex-dependent BRS patterns, highlighting the clinical importance of our results. Our findings may aid in screening for neurocardiac abnormalities in apparently healthy individuals.

Monitoring Effects of Sleep Extension and Restriction on Endurance Performance Using Heart Rate Indices

ABSTRACT

Roberts, SSH, Aisbett, B, Teo, W-P, and Warmington, S. Monitoring effects of sleep extension and restriction on endurance performance using heart rate indices. J Strength Cond Res 36(12): 3381-3389, 2022-Heart rate (HR) indices are useful for monitoring athlete fatigue or "readiness to perform." This study examined whether HR indices are sensitive to changes in readiness following sleep restriction (SR) and sleep extension (SE). Nine athletes completed a crossover study with 3 conditions: SR, normal sleep (NS), and SE. Each condition required completion of an endurance time trial (TT) on 4 consecutive days (D1-D4). Athletes slept habitually before D1; however, time in bed was reduced by 30% (SR), remained normal (NS), or extended by 30% (SE), on subsequent nights (D1-D3). Daily resting HR and HR variability were recorded. The maximal rate of HR increase and HR recovery was determined from a constant-load test before TTs. Exercise intensity ratios incorporating mean HR, mean power (W), and perceived exertion (RPE) were recorded at steady state during constant-load tests (W:HR SS ) and during TTs (W:HR TT , RPE:HR TT ). Compared with D4 of NS, RPE:HR TT was lower on D4 of SE ( p = 0.008)-when TT performances were faster. Compared with D1 of SR, RPE:HR TT was higher on D3 and D4 of SR ( p < 0.02). Moderate correlations were found between percentage changes in W:HR TT and changes in TT finishing time in SR ( r = -0.67, p = 0.049) and SE ( r = -0.69, p = 0.038) conditions. Intensity ratios incorporating mean HR seem sensitive to effects of sleep duration on athlete readiness to perform. When interpreting intensity ratios, practitioners should consider potential effects of prior sleep duration to determine whether sleep-promoting interventions are required (e.g., SE).

Effects of Ultrasound-Guided Stellate Ganglion Block on Postoperative Quality of Recovery in Patients Undergoing Breast Cancer Surgery: A Randomized Controlled Clinical Trial

Surgery has been the primary treatment for breast cancer. However, instant postoperative complications, such as sleep disorder and pain, dramatically impair early postoperative quality of recovery, resulting in more extended hospital stays and higher costs. Recent clinical trials indicated that stellate ganglion block (SGB) could prolong sleep time and improve sleep quality in breast cancer survivors. Moreover, during the perioperative period, SGB enhanced the recovery of gastrointestinal functions in patients with laparoscopic colorectal cancer surgery and thoracolumbar spinal surgery. Furthermore, perioperative SGB decreased intraoperative requirements for anesthetics and analgesics in patients with complex regional pain syndrome. However, information is scarce regarding the effects of SGB on postoperative quality recovery in patients with breast cancer surgery. Therefore, we investigated the effects of SGB on the postoperative quality of recovery of patients undergoing breast cancer surgery. Sixty patients who underwent an elective unilateral modified radical mastectomy were randomized into two 30-patient groups that received either an ultrasound-guided right-sided SGB with 6 ml 0.25% ropivacaine (SGB group) or no block (control group). The primary outcome was the quality of postoperative recovery 24 hours after surgery, assessed with a Chinese version of the 40-item Quality of Recovery (QoR-40) questionnaire. Secondary outcomes were intraoperative requirements of propofol and opioids, rest pain at two, four, eight, and 24 hours after surgery, patient satisfaction score, and the incidence of postoperative abdominal distension. At 24 hours after surgery, global QoR-40 scores were higher in the SGB group than in the control group. Besides, in the SGB group, patients needed less propofol, had a lower incidence of postoperative abdominal bloating, and had higher satisfaction scores. Ultrasound-guided SGB could improve the quality of postoperative recovery in patients undergoing breast cancer surgery by less intraoperatively need for propofol and better postoperative recovery of sleep and gastrointestinal function.

Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.

Full-Body Photobiomodulation Therapy Is Associated with Reduced Sleep Durations and Augmented Cardiorespiratory Indicators of Recovery

Research is emerging on the use of Photobiomodulation therapy (PBMT) and its potential for augmenting human performance, however, relatively little research exists utilizing full-body administration methods. As such, further research supporting the efficacy of whole-body applications of PBMT for behavioral and physiological modifications in applicable, real-world settings are warranted. The purpose of this analysis was to observe cardiorespiratory and sleep patterns surrounding the use of full-body PBMT in an elite cohort of female soccer players. Members of a women’s soccer team in a “Power 5 conference” of the National Collegiate Athletic Association (NCAA) were observed across one competitive season while wearing an OURA Ring nightly and a global positioning system (GPS) sensor during training. Within-subject comparisons of cardiorespiratory physiology, sleep duration, and sleep composition were evaluated the night before and after PBMT sessions completed as a standard of care for team recovery. Compared to pre-intervention, mean heart rate (HR) was significantly lower the night after a PBMT session (p = 0.0055). Sleep durations were also reduced following PBMT, with total sleep time (TST) averaging 40 min less the night after a session (p = 0.0006), as well as significant reductions in light sleep (p = 0.0307) and rapid eye movement (REM) sleep durations (p = 0.0019). Sleep durations were still lower following PBMT, even when controlling for daily and accumulated training loads. Enhanced cardiorespiratory indicators of recovery following PBMT, despite significant reductions in sleep duration, suggest that it may be an effective modality for maintaining adequate recovery from the high stress loads experienced by elite athletes.

Detecting sleep outside the clinic using wearable heart rate devices

The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04-0.06 and a total sleep time (TST) deviation of [Formula: see text]2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between [Formula: see text]29.07 and [Formula: see text]55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.

Combining cardiac monitoring with actigraphy aids nocturnal arousal detection during ambulatory sleep assessment in insomnia

STUDY OBJECTIVES

The objective assessment of insomnia has remained difficult. Multisensory devices collecting heart rate (HR) and motion are regarded as the future of ambulatory sleep monitoring. Unfortunately, reports on altered average HR or heart rate variability (HRV) during sleep in insomnia are equivocal. Here, we evaluated whether the objective quantification of insomnia improves by assessing state-related changes in cardiac measures.

METHODS

We recorded electrocardiography, posture, and actigraphy in 33 people without sleep complaints and 158 patients with mild to severe insomnia over 4 d in their home environment. At the microscale, we investigated whether HR changed with proximity to gross (body) and small (wrist) movements at nighttime. At the macroscale, we calculated day-night differences in HR and HRV measures. For both timescales, we tested whether outcome measures were related to insomnia diagnosis and severity.

RESULTS

At the microscale, an increase in HR was often detectable already 60 s prior to as well as following a nocturnal chest, but not wrist, movement. This increase was slightly steeper in insomnia and was associated with insomnia severity, but future EEG recordings are necessary to elucidate whether these changes occur prior to or simultaneously with PSG-indicators of wakefulness. At the macroscale, we found an attenuated cardiac response to sleep in insomnia: patients consistently showed smaller day-night differences in HR and HRV.

CONCLUSIONS

Incorporating state-related changes in cardiac features in the ambulatory monitoring of sleep might provide a more sensitive biomarker of insomnia than the use of cardiac activity averages or actigraphy alone.

When the Locus Coeruleus Speaks Up in Sleep: Recent Insights, Emerging Perspectives

For decades, numerous seminal studies have built our understanding of the locus coeruleus (LC), the vertebrate brain’s principal noradrenergic system. Containing a numerically small but broadly efferent cell population, the LC provides brain-wide noradrenergic modulation that optimizes network function in the context of attentive and flexible interaction with the sensory environment. This review turns attention to the LC’s roles during sleep. We show that these roles go beyond down-scaled versions of the ones in wakefulness. Novel dynamic assessments of noradrenaline signaling and LC activity uncover a rich diversity of activity patterns that establish the LC as an integral portion of sleep regulation and function. The LC could be involved in beneficial functions for the sleeping brain, and even minute alterations in its functionality may prove quintessential in sleep disorders.

Tracking Sleep, Temperature, Heart Rate, and Daily Symptoms Across the Menstrual Cycle with the Oura Ring in Healthy Women

Background and Objective

The ovulatory menstrual cycle is characterized by hormonal fluctuations that influence physiological systems and functioning. Multi-sensor wearable devices can be sensitive tools capturing cycle-related physiological features pertinent to women’s health research. This study used the Oura ring to track changes in sleep and related physiological features, and also tracked self-reported daily functioning and symptoms across the regular, healthy menstrual cycle.

Methods

Twenty-six healthy women (age, mean (SD): 24.4 (1.1 years)) with regular, ovulatory cycles (length, mean (SD): 28.57 (3.8 days)) were monitored across a complete menstrual cycle. Four menstrual cycle phases, reflecting different hormone milieus, were selected for analysis: menses, ovulation, mid-luteal, and late-luteal. Objective measures of sleep, sleep distal skin temperature, heart rate (HR) and vagal-mediated heart rate variability (HRV, rMSSD), derived from the Oura ring, and subjective daily diary measures (eg sleep quality, readiness) were compared across phases.

Results

Wearable-based measures of sleep continuity and sleep stages did not vary across the menstrual cycle. Women reported no menstrual cycle-related changes in perceived sleep quality or readiness and only marginally poorer mood in the midluteal phase. However, they reported moderately more physical symptoms during menses (p < 0.001). Distal skin temperature and HR, measured during sleep, showed a biphasic pattern across the menstrual cycle, with increased HR (p < 0.03) and body temperature (p < 0.001) in the mid- and late-luteal phases relative to menses and ovulation. Correspondingly, rMSSD HRV tended to be lower in the luteal phase. Further, distal skin temperature was lower during ovulation relative to menses (p = 0.05).

Conclusion

The menstrual cycle was not accompanied by significant fluctuations in objective and perceived measures of sleep or in mood, in healthy women with regular, ovulatory menstrual cycles. However, other physiological changes in skin temperature and HR were evident and may be longitudinally tracked with the Oura ring in women over multiple cycles in a natural setting.

Performance Evaluation of a Smart Bed Technology against Polysomnography

The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.

PtNPs/Short MWCNT-PEDOT: PSS-Modified Microelectrode Array to Detect Neuronal Firing Patterns in the Dorsal Raphe Nucleus and Hippocampus of Insomnia Rats

Research on the intracerebral mechanism of insomnia induced by serotonin (5-HT) deficiency is indispensable. In order to explore the effect of 5-HT deficiency-induced insomnia on brain regions related to memory in rats, we designed and fabricated a microelectrode array that simultaneously detects the electrical activity of the dorsal raphe nucleus (DRN) and hippocampus in normal, insomnia and recovery rats in vivo. In the DRN and hippocampus of insomnia rats, our results showed that the spike amplitudes decreased by 40.16 and 57.92%, the spike repolarization slope decreased by 44.64 and 48.59%, and the spiking rate increased by 66.81 and 63.40%. On a mesoscopic scale, the increased firing rates of individual neurons led to an increased δ wave power. In the DRN and hippocampus of insomnia rats, the δ wave power increased by 57.57 and 67.75%. Furthermore, two segments’ δ wave slopes were also increased in two brain regions of the insomnia rats. Our findings suggest that 5-HT deficiency causes the hyperactivity of neurons in the hippocampus and DRN; the DRN’s firing rate and the hippocampal neuronal amplitude reflect insomnia in rats more effectively. Further studies on alleviating neurons affected by 5-HT deficiency and on achieving a highly effective treatment for insomnia by the microelectrode array are needed.

Interbeat interval-based sleep staging: work in progress toward real-time implementation

Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals.Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen's Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep.Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms.Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies.Novelty & Significance(92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.

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.

Development of the Ontogenetic Self-Regulation Clock

To date, there is no overarching proposition for the ontogenetic-neurobiological basis of self-regulation. This paper suggests that the balanced self-regulatory reaction of the fetus, newborn and infant is based on a complex mechanism starting from early brainstem development and continuing to progressive control of the cortex over the brainstem. It is suggested that this balance occurs through the synchronous reactivity between the sympathetic and parasympathetic systems, both which originate from the brainstem. The paper presents an evidence-based approach in which molecular excitation-inhibition balance, interchanges between excitatory and inhibitory roles of neurotransmitters as well as cardiovascular and white matter development across gestational ages, are shown to create sympathetic-parasympathetic synchrony, including the postnatal development of electroencephalogram waves and vagal tone. These occur in developmental milestones detectable in the same time windows (sensitive periods of development) within a convergent systematic progress. This ontogenetic stepwise process is termed "the self-regulation clock" and suggest that this clock is located in the largest connection between the brainstem and the cortex, the corticospinal tract. This novel evidence-based new theory paves the way towards more accurate hypotheses and complex studies of self-regulation and its biological basis, as well as pointing to time windows for interventions in preterm infants. The paper also describes the developing indirect signaling between the suprachiasmatic nucleus and the corticospinal tract. Finally, the paper proposes novel hypotheses for molecular, structural and functional investigation of the "clock" circuitry, including its associations with other biological clocks. This complex circuitry is suggested to be responsible for the developing self-regulatory functions and their neurobehavioral correlates.

Autonomic arousals contribute to brain fluid pulsations during sleep

During sleep, slow waves of neuro-electrical activity engulf the human brain and aid in the consolidation of memories. Recent research suggests that these slow waves may also promote brain health by facilitating the removal of metabolic waste, possibly by orchestrating the pulsatile flow of cerebro-spinal fluid (CSF) through local neural control over vascular tone. To investigate the role of slow waves in the generation of CSF pulsations, we analyzed functional MRI data obtained across the full sleep-wake cycle and during a respiratory task during wakefulness. This revealed a novel generating mechanism that relies on the autonomic regulation of cerebral vascular tone without requiring slow electrocortical activity or even sleep. Therefore, the role of CSF pulsations in brain waste clearance may, in part, depend on proper autoregulatory control of cerebral blood flow.

Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker

PURPOSE

To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware.

PATIENTS AND METHODS

58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2^nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics.

RESULTS

Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males.

CONCLUSION

These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.

Environmental noise-induced cardiovascular responses during sleep

STUDY OBJECTIVES

This study was designed to test the utility of cardiovascular responses as markers of potentially different environmental noise disruption effects of wind farm compared to traffic noise exposure during sleep.

METHODS

Twenty participants underwent polysomnography. In random order, and at six sound pressure levels from 33 dBA to 48 dBA in 3 dB increments, three types of wind farm and two types of road traffic noise recordings of 20-sec duration were played during established N2 or deeper sleep, each separated by 20 seconds without noise. Each noise sequence also included a no-noise control. Electrocardiogram and finger pulse oximeter recorded pulse wave amplitude changes from the pre-noise onset baseline following each noise exposure and were assessed algorithmically to quantify the magnitude of heart rate and finger vasoconstriction responses to noise exposure.

RESULTS

Higher sound pressure levels were more likely to induce drops in pulse wave amplitude. Sound pressure levels as low as 39 dBA evoked a pulse wave amplitude response (Odds ratio [95% confidence interval]; 1.52 [1.15, 2.02]). Wind farm noise with amplitude modulation was less likely to evoke a pulse wave amplitude response than the other noise types, but warrants cautious interpretation given low numbers of replications within each noise type.

CONCLUSION

These preliminary data support that drops in pulse wave amplitude are a particularly sensitive marker of noise-induced cardiovascular responses during. Larger trials are clearly warranted to further assess relationships between recurrent cardiovascular activation responses to environmental noise and potential long-term health effects.

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical-cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Increased resting heart rate and glucose metabolism in a community population

Objective

Resting heart rate (RHR) independently predicts cardiovascular death. Increased RHR is related to chronic diseases, but community-based studies are rare. We investigated this population and factors related to RHR.

Methods

In total, 374 participants underwent medical examinations from March 2019 to December 2019. Participants were divided into groups with low RHR (LRHR; <65 beats/minute) and high RHR (HRHR; ≥65 beats/minute). RHR was judged using resting electrocardiogram at physical examination. We conducted laboratory examinations, including glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), and blood lipids, among participants with chronic diseases. We used Cox proportional risk regression and multivariate analyses for the following covariates: previous chronic diseases, body mass index (BMI), smoking, blood lipids, and FPG.

Results

The incidence of type 2 diabetes mellitus (T2DM) and HbA1c values were both significantly higher in the HRHR group than in the LRHR group. Spearman correlation analysis showed RHR had a positive correlation with low-density lipoprotein, BMI, FPG, and HbA1c (r = 0.104574, 0.117266, 0.116041, and 0.311761, respectively). Multiple linear regression analysis showed age, hypertension, T2DM, and HbA1c were factors influencing RHR.

Conclusion

RHR showed strong correlation with T2DM and HbA1c in our community population, suggesting that RHR may be a risk factor for cardiovascular disease.

A model characterizing the coupling between slow-wave activity, instantaneous heart rate and heart rate variability during sleep. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:72-75. [PMID: 34891242 DOI: 10.1109/embc46164.2021.9630006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The cyclical and progressively decreasing dynamics of electroencephalogram (EEG) based slow-wave activity (SWA) during sleep reflects the homeostatic component of sleep-wake regulation. The dynamic changes of heart rate (HR) and heart rate variability (HRV) indices during sleep also exhibit quasi-cyclic trends that appear to correlate with SWA. This article proposes a model to characterize the relationship between SWA, HR and HRV in the polar-coordinate (r-θ) domain. Polar coordinates are particularly well-suited to model cyclic shapes with simple (linear) equations in the r-θ plane. Group-level analyses and individual-level ones of the correlations between the polar-coordinate transformations of SWA and HR reveal R² values of 0.99 and 0.95 respectively. Given that, HR and HRV can be estimated in less obtrusive ways compared to EEG. This research offers relevant options to conveniently monitor sleep SWA.Clinical Relevance- Slow wave activity is a marker of sleep restoration that most prominently manifests in the EEG. This research suggests that an electrocardiography (ECG)-based non-linear model can approximate a polar-coordinate version of SWA. Since ECG correlates can be unobtrusively acquired during sleep, these results suggest that practical SWA monitoring can be achieved through cardiac activity measurements.