Correlation between electroencephalography and heart rate variability during sleep

Noninvasive neuromodulation of subregions of the human insula differentially affect pain processing and heart-rate variability: a within-subjects pseudo-randomized trial

ABSTRACT

The insula is an intriguing target for pain modulation. Unfortunately, it lies deep to the cortex making spatially specific noninvasive access difficult. Here, we leverage the high spatial resolution and deep penetration depth of low-intensity focused ultrasound (LIFU) to nonsurgically modulate the anterior insula (AI) or posterior insula (PI) in humans for effect on subjective pain ratings, electroencephalographic (EEG) contact heat-evoked potentials, as well as autonomic measures including heart-rate variability (HRV). In a within-subjects, repeated-measures, pseudo-randomized trial design, 23 healthy volunteers received brief noxious heat pain stimuli to the dorsum of their right hand during continuous heart-rate, electrodermal, electrocardiography and EEG recording. Low-intensity focused ultrasound was delivered to the AI (anterior short gyrus), PI (posterior longus gyrus), or under an inert Sham condition. The primary outcome measure was pain rating. Low-intensity focused ultrasound to both AI and PI similarly reduced pain ratings but had differential effects on EEG activity. Low-intensity focused ultrasound to PI affected earlier EEG amplitudes, whereas LIFU to AI affected later EEG amplitudes. Only LIFU to the AI affected HRV as indexed by an increase in SD of N-N intervals and mean HRV low-frequency power. Taken together, LIFU is an effective noninvasive method to individually target subregions of the insula in humans for site-specific effects on brain biomarkers of pain processing and autonomic reactivity that translates to reduced perceived pain to a transient heat stimulus.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Non-invasive neuromodulation of sub-regions of the human insula differentially affect pain processing and heart-rate variability

The insula is a portion of the cerebral cortex folded deep within the lateral sulcus covered by the overlying opercula of the inferior frontal lobe and superior portion of the temporal lobe. The insula has been parsed into sub-regions based upon cytoarchitectonics and structural and functional connectivity with multiple lines of evidence supporting specific roles for each of these sub-regions in pain processing and interoception. In the past, causal interrogation of the insula was only possible in patients with surgically implanted electrodes. Here, we leverage the high spatial resolution combined with the deep penetration depth of low-intensity focused ultrasound (LIFU) to non-surgically modulate either the anterior insula (AI) or posterior insula (PI) in humans for effect on subjective pain ratings, electroencephalographic (EEG) contact head evoked potentials (CHEPs) and time-frequency power as well as autonomic measures including heart-rate variability (HRV) and electrodermal response (EDR). N = 23 healthy volunteers received brief noxious heat pain stimuli to the dorsum of their right hand during continuous heart-rate, EDR and EEG recording. LIFU was delivered to either the AI (anterior short gyrus), PI (posterior longus gyrus) or under an inert sham condition time-locked to the heat stimulus. Results demonstrate that single-element 500 kHz LIFU is capable of individually targeting specific gyri of the insula. LIFU to both AI and PI similarly reduced perceived pain ratings but had differential effects on EEG activity. LIFU to PI affected earlier EEG amplitudes around 300 milliseconds whereas LIFU to AI affected EEG amplitudes around 500 milliseconds. In addition, only LIFU to the AI affected HRV as indexed by an increase in standard deviation of N-N intervals (SDNN) and mean HRV low frequency power. There was no effect of LIFU to either AI or PI on EDR or blood pressure. Taken together, LIFU looks to be an effective method to individually target sub-regions of the insula in humans for site-specific effects on brain biomarkers of pain processing and autonomic reactivity that translates to reduced perceived pain to a transient heat stimulus. These data have implications for the treatment of chronic pain and several neuropsychological diseases like anxiety, depression and addiction that all demonstrate abnormal activity in the insula concomitant with dysregulated autonomic function.

An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors

The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.

Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal-subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.

EEG brain oscillations are modulated by interoception in response to a synchronized motor vs. cognitive task

So far, little is known about how conscious attention to internal body signals, that is, interoception, affects the synchronization with another person, a necessary or required social process that promotes affiliations and cooperation during daily joint social interactions. The effect of explicit interoceptive attentiveness (IA) modulation, conceived as the focus on the breath for a given time interval, on electrophysiological (EEG) correlates during an interpersonal motor task compared with a cognitive synchronization task was investigated in this study. A total of 28 healthy participants performed a motor and a cognitive synchronization task during the focus and no-focus breath conditions. During the tasks, frequency bands (delta, theta, alpha, and beta bands) from the frontal, temporo-central, and parieto-occipital regions of interest (ROIs) were acquired. According to the results, significantly higher delta and theta power were found in the focus condition in the frontal ROI during the execution of the motor than the cognitive synchronization task. Moreover, in the same experimental condition, delta and beta band power increased in the temporo-central ROI. The current study suggested two main patterns of frequency band modulation during the execution of a motor compared with the cognitive synchronization task while a person is focusing the attention on one's breath. This study can be considered as the first attempt to classify the different effects of interoceptive manipulation on motor and cognitive synchronization tasks using neurophysiological measures.

An exploratory study of drivers' EEG response during emergent collision avoidance

INTRODUCTION

EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers' mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety-critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process.

METHOD

A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants' EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage.

RESULTS

The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages.

CONCLUSIONS

Enhanced activities of different EEG bands could be concurrent at emergent and safety-critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process.

PRACTICAL APPLICATIONS

The study generates more understanding of drivers' neurophysiological response in an emergent and safety-critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Temporal relations between peripheral and central arousals in good and poor sleepers

Good sleepers and patients with insomnia symptoms (poor sleepers) were tracked with two measures of arousal; conventional polysomnography (PSG) for electroencephalogram (EEG) assessed cortical arousals, and a peripheral arterial tonometry device was used for the detection of peripheral nervous system (PNS) arousals associated with vasoconstrictions. The relationship between central (cortical) and peripheral (autonomic) arousals was examined by evaluating their close temporal dynamics. Cortical arousals almost invariably were preceded and followed by peripheral activations, while large peripheral autonomic arousals were followed by cortical arousals only half of the time. The temporal contiguity of these two types of arousals was altered in poor sleepers, and poor sleepers displayed a higher number of cortical and peripheral arousals compared with good sleepers. Given the difference in the number of peripheral autonomic arousals between good and poor sleepers, an evaluation of such arousals could become a means of physiologically distinguishing poor sleepers.

Autonomic Central Coupling during Daytime Sleep Differs between Older and Younger People

Decreased functioning in the elderly is mirrored by independent changes in central and autonomic nervous systems. Additionally, recent work suggests that the coupling of these systems may also serve an important role. In young adults, Autonomic and Central Events (ACEs), measured in the temporal coincidence of heart rate bursts (HRBs) and increased slow-wave-activity (SWA, 0.5-1Hz) and sigma activity (12-15Hz), followed by parasympathetic surge (RR_HF) during non-rapid eye movement (NREM) sleep, predicted cognitive improvements. However, ACEs have not been examined in the elderly. Thus, the current study compared ACEs during wake and daytime sleep in older and younger adults and examined associations with working memory improvement before and after a nap. Compared to youngers, older adults showed lower amplitude of ACEs during NREM sleep, but not during wake. Furthermore, while younger adults demonstrated a parasympathetic surge after HRBs, older adults showed an earlier rise and longer maintenance of the RRHF. Taken together, our results demonstrate that autonomic-central coupling declines with age. Pathological aging implicates independent roles for decreased autonomic and central nervous system functioning, the current findings suggest that the coupling of these systems may also deserve attention.

Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy

In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying manifold (neuro-)physiological impacts induced by different motor learning approaches. The various influences on brain or cardiac function are usually studied separately and modeled linearly. Limitations of these models have recently led to a rapidly growing application of nonlinear models. This study aimed to investigate the acute effects of various sequences of rope skipping on irregularity of the electrocardiography (ECG) and electroencephalography (EEG) signals as well as their interaction and whether these depend on different levels of active movement noise, within the framework of differential learning theory. Thirty-two males were randomly and equally distributed to one of four rope skipping conditions with similar cardiovascular but varying coordinative demand. ECG and EEG were measured simultaneously at rest before and immediately after rope skipping for 25 mins. Signal irregularity of ECG and EEG was calculated via the multiscale fuzzy measure entropy (MSFME). Statistically significant ECG and EEG brain area specific changes in MSFME were found with different pace of occurrence depending on the level of active movement noise of the particular rope skipping condition. Interaction analysis of ECG and EEG MSFME specifically revealed an involvement of the frontal, central, and parietal lobe in the interplay with the heart. In addition, the number of interaction effects indicated an inverted U-shaped trend presenting the interaction level of ECG and EEG MSFME dependent on the level of active movement noise. In summary, conducting rope skipping with varying degrees of movement variation appears to affect the irregularity of cardiac and brain signals and their interaction during the recovery phase differently. These findings provide enough incentives to foster further constructive nonlinear research in exercise-recovery relationship and to reconsider the philosophy of classical endurance training.

The Effect of Dynamic Lighting for Working Shift People on Clinical Heart Rate Variability and Human Slow Wave Sleep

The quality of sleep during lunch breaks will affect the work efficiency, concentration, and mood of workers, and then increase the performance of workers. In this study, Dynamic CCT lighting is proposed as a method to control sleep quality, and a novel hypnotic lighting system is developed according to the experimental design. Pulse width modulation(PWM) is used for controlling sleep lights and adjusting and controlling the spectrum intensity of polychromatic LED to realize the color mixing, which conforms to CCT, illumination, uniformity, and other parameters in the experimental design. The control group of this study is in a dark room, and the experimental group is given dim light and Dynamic CCT lighting. Through volunteer psychological questionnaire evaluation, objective Sleep Wrist Actigraphy and Heart Rate Variability (HRV) are used analyzing sleep quality and Autonomic Nervous System (ANS). The result is found that the sleep environment with Dynamic CCT lighting is better than that with Dim light in three kinds of sleep lighting environments, while the sleep environment of Dynamic CCT lighting is very similar to that of a dark room. In terms of work efficiency after sleep, Dynamic CCT used in the sleep environment of lighting and Dim light is significantly better than that in a dark room.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia

Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.

Sleep in 21-Day Dry Immersion. Are Cardiovascular Adjustments Rapid Eye Movement Sleep-Dependent?

Introduction: A decrease in sleep quality and duration during space missions has repeatedly been reported. However, the exact causes that underlie this effect remain unclear. In space, sleep might be impacted by weightlessness and its influence on cardiovascular function. In this study, we aimed at exploring the changes of night sleep architecture during prolonged, 21-day Dry Immersion (DI) as one of the ground-based models for microgravity studies and comparing them with adaptive changes in the cardiovascular system. Methods: Ten healthy young men were exposed to DI for 21 days. The day before (baseline, B-1), on the 3rd (DI3), 10th (DI10), and 19th (DI19) day of DI, as well as in the recovery period, 1 day after the end of DI (R + 1), they were subjected to overnight polysomnography (PSG) and ambulatory blood pressure monitoring. Results: On DI3, when the most severe back pain occurred due to the effects of DI on the spine and back muscles, the PSG data showed dramatically disorganized sleep architecture. Sleep latency, the number of awakenings, and the duration of wake after sleep onset (WASO) were significantly increased compared with the B-1. Furthermore, the sleep efficiency, duration of rapid eye movement sleep (REM), and duration of non-rapid eye movement stage 2 decreased. On DI10, subjective pain ratings declined to 0 and sleep architecture returned to the baseline values. On DI19, the REM duration increased and continued to rise on R + 1. An increase in REM was accompanied by rising in a nighttime heart rate (HR), which also shows the most significant changes after the end of DI. On DI19 and R + 1, the REM duration showed opposite correlations with the BP parameters: on DI19 it was negatively associated with the systolic BP (SBP), and on R + 1 it was positively correlated with the diastolic BP (DBP). Conclusion: An increase in REM at the end of DI and in recovery might be associated with regulatory changes in the cardiovascular system, in particular, with the reorganization of the peripheral and central blood flow in response to environmental changes.

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study

BACKGROUND

Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation.

OBJECTIVE

This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work.

METHODS

A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement.

RESULTS

Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm.

CONCLUSIONS

We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

Applying Heart Rate Variability to Monitor Health and Performance in Tactical Personnel: A Narrative Review

Human performance optimization of tactical personnel requires accurate, meticulous, and effective monitoring of biological adaptations and systemic recovery. Due to an increased understanding of its importance and the commercial availability of assessment tools, the use of heart rate variability (HRV) to address this need is becoming more common in the tactical community. Measuring HRV is a non-invasive, practical method for objectively assessing a performer's readiness, workload, and recovery status; when combined with additional data sources and practitioner input, it provides an affordable and scalable solution for gaining actionable information to support the facilitation and maintenance of operational performance. This narrative review discusses the non-clinical use of HRV for assessing, monitoring, and interpreting autonomic nervous system resource availability, modulation, effectiveness, and efficiency in tactical populations. Broadly, HRV metrics represent a complex series of interactions resulting from internal and external stimuli; therefore, a general overview of HRV applications in tactical personnel is discussed, including the influence of occupational specific demands, interactions between cognitive and physical domains, and recommendations on implementing HRV for training and recovery insights into critical health and performance outcomes.

Automatic sleep staging by cardiorespiratory signals: a systematic review

BACKGROUND

Because of problems with the recording and analysis of the EEG signal, automatic sleep staging using cardiorespiratory signals has been employed as an alternative. This study reports on certain critical points which hold considerable promise for the improvement of the results of the automatic sleep staging using cardiorespiratory signals.

METHODS

A systematic review.

RESULTS

The review and analysis of the literature in this area revealed four outstanding points: (1) the feature extraction epoch length, denoting that the standard 30-s segments of cardiorespiratory signals do not carry enough information for automatic sleep staging and that a 4.5-min length segment centering on each 30-s segment is proper for staging, (2) the time delay between the EEG signal extracted from the central nervous system activity and the cardiorespiratory signals extracted from the autonomic nervous system activity should be considered in the automatic sleep staging using cardiorespiratory signals, (3) the information in the morphology of ECG signals can contribute to the improvement of sleep staging, and (4) applying convolutional neural network (CNN) and long short-term memory network (LSTM) deep structures simultaneously to a large PSG recording database can lead to more reliable automatic sleep staging results.

CONCLUSIONS

Considering the above-mentioned points simultaneously can improve automatic sleep staging by cardiorespiratory signals. It is hoped that by considering the points, staging sleep automatically using cardiorespiratory signals, which does not have problems with the recording and analysis of EEG signals, yields results acceptably close to the results of automatic sleep staging by EEG signals.

Human-in-the-Loop Predictive Analytics Using Statistical Learning

The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human's input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human's intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

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

Age-related losses in cardiac autonomic activity during a daytime nap

In healthy, young individuals, a reduction in cardiovascular output and a shift from sympathetic to parasympathetic (vagal) dominance is observed from wake into stages of nocturnal and daytime sleep. This cardiac autonomic profile, measured by heart rate variability (HRV), has been associated with significant benefits for cardiovascular health. Aging is associated with decreased nighttime sleep quality and lower parasympathetic activity during both sleep and resting. However, it is not known whether age-related dampening of HRV extends to daytime sleep, diminishing the cardiovascular benefits of naps in the elderly. Here, we investigated this question by comparing the autonomic activity profile between young and older healthy adults during a daytime nap and a similar period of wakefulness (quiet wake; QW). For each condition, from the electrocardiogram (ECG), we obtained beat-to-beat HRV intervals (RR), root mean square of successive differences between adjacent heart-beat-intervals (RMSSD), high-frequency (HF), low-frequency (LF) power, and total power (TP), HF normalized units (HFnu ), and the LF/HF ratio. As previously reported, young subjects showed a parasympathetic dominance during NREM, compared with REM, prenap rest, and WASO. Moreover, older, compared to younger, adults showed significantly lower vagally mediated HRV (measured by RMSSD, HF, HFnu ) during NREM. Interestingly, however, no age-related differences were detected during prenap rest or QW. Altogether, our findings suggest a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

Circadian Rhythms, Sleep, and Aging

Abstract. Circadian mechanisms and the sleep-wakefulness rhythms guarantee survival, adaptation, efficient action in everyday life or in emergencies and well-being. Disordered circadian processes at central and/or cellular levels, sleep disorders, and unhealthy wakefulness/sleep rhythms can impair the physiological circadian organization and result in subjective, professional, or behavioral changes ranging from functional inadequacy to higher risks at work or on the road to medical relevance. Circadian rhythms and the sleep organization change ontogenetically; major changes result from normal aging and from the multiple diseases that are often associated. There are circular functional interactions involving sleep/sleep disorders, the autonomic and immune systems, and the functional changes in the circadian system due to aging that deserve attention but have been overlooked thus far.

Sleep-Dependent Directional Interactions of the Central Nervous System-Cardiorespiratory Network

OBJECTIVE

We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep.

METHODS

Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied.

RESULTS

Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions.

CONCLUSION

These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms.

SIGNIFICANCE

Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.

Mutual Information Analysis of Brain-Body Interactions during different Levels of Mental stress

In this work, we analyze brain-heart interactions during different mental states computing mutual information (MI) between the dynamic activity of different physiological systems. In 18 healthy subjects monitored in a relaxed resting state and during a mental arithmetic and a serious game task, multichannel EEG, one lead ECG, respiration and blood volume pulse were collected via wireless non-invasive biosensors. From these signals, synchronous 300-second time series were extracted measuring brain activity via the δ, θ, α, and β EEG power, and activity of the body district via the ECG R-R interval η, the respiratory amplitude ρ and the pulse arrival time π. MI was computed using a linear estimator: (i) between {η,ρ,π} and {δ,θ,α,β}, to measure overall brain-body interactions; (ii) between each time series and the others of the same district, to measure information shared within a district; and (iii) between each time series of a district and all series of the other district, to evaluate individual contributions to the information shared between brain and body. Results document the existence of statistically significant brain-body interactions, with high MI values involving mainly the η body dynamics and the δ and β brain dynamics. State-dependent variations were mostly relevant to the MI of the brain system involving δ, θ, α during mental arithmetic, and α and β during serious game. Thus, MI can be useful to detect correlated activity within and between brain and body systems monitored simultaneously during different mental states.

Autonomic/central coupling benefits working memory in healthy young adults

Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is not known. Studies have shown greater WM gains after a period of sleep than a similar period of wake, and correlations between WM improvement and slow wave activity (SWA; 0.5-1 Hz) during slow wave sleep (SWS). A different body of literature has suggested an important role for autonomic activity during wake for WM. A recent study from our group reported that the temporal coupling of Autonomic/CentralEvents (ACEs) during sleep was associated with memory consolidation. We found that heart rate bursts (HR bursts) during non-rapid eye movement (NREM) sleep are accompanied by increases in SWA and sigma (12-15 Hz) power, as well as increases in the high-frequency (HF) component of the RR interval, reflecting vagal rebound. In addition, ACEs predict long-term, episodic memory improvement. Building on these previous results, we examined whether ACEs also contribute to gains in WM. We tested 104 young adults in an operation span task (OSPAN) in the morning and evening, with either a nap (n = 53; with electroencephalography (EEG) and electrocardiography (ECG)) or wake (n = 51) between testing sessions. We identified HR bursts in the ECG and replicated the increases in SWA and sigma prior to peak of the HR burst, as well as vagal rebound after the peak. Furthermore, we showed sleep-dependent WM improvement, which was predicted by ACE activity. Using regression analyses, we discovered that significantly more variance in WM improvement could be explained with ACE variables than with overall sleep activity not time-locked with ECG. These results provide the first evidence that coordinated autonomic and central events play a significant role in sleep-related WM improvement and implicate the potential of autonomic interventions during sleep for cognitive enhancement.

High-Frequency Cerebral Activation and Interhemispheric Synchronization Following Sudarshan Kriya Yoga as Global Brain Rhythms: The State Effects

Context:

Respiration is known to modulate neuronal oscillations in the brain and is measured by electroencephalogram (EEG). Sudarshan Kriya Yoga (SKY) is a popular breathing process and is established for its significant effects on the various aspects of physiology and psychology.

Aims:

This study aimed to observe neuronal oscillations in multifrequency bands and interhemispheric synchronization following SKY.

Settings and Design:

This study employed before- and after-study design.

Subjects and Methods:

Forty healthy volunteers (average age 25.45 ± 5.75, 23 males and 17 females) participated in the study. Nineteen-channel EEG was recorded and analyzed for 5 min each: before and after SKY. Spectral power for delta, theta, alpha, beta, and gamma frequency band was calculated using Multi-taper Fast Fourier Transform (Chronux toolbox). The Asymmetry Index was calculated by subtracting the natural log of powers of left (L) hemisphere from the right^® to show interhemispheric synchronization.

Statistical Analysis:

Paired t-test was used for statistical analysis.

Results:

Spectral power increased significantly in all frequency bands bilaterally in frontal, central, parietal, temporal, and occipital regions of the brain after long SKY. Electrical activity shifted from lower to higher frequency range with a significant rise in the gamma and beta powers following SKY. Asymmetry Index values tended toward 0 following SKY.

Conclusions:

A single session of SKY generates global brain rhythm dominantly with high-frequency cerebral activation and initiates appropriate interhemispheric synchronization in brain rhythms as state effects. This suggests that SKY leads to better attention, memory, and emotional and autonomic control along with enhanced cognitive functions, which finally improves physical and mental well-being.

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

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

Deep learning of spontaneous arousal fluctuations detects early cholinergic defects across neurodevelopmental mouse models and patients

Neurodevelopmental spectrum disorders like autism (ASD) are diagnosed, on average, beyond age 4 y, after multiple critical periods of brain development close and behavioral intervention becomes less effective. This raises the urgent need for quantitative, noninvasive, and translational biomarkers for their early detection and tracking. We found that both idiopathic (BTBR) and genetic (CDKL5- and MeCP2-deficient) mouse models of ASD display an early, impaired cholinergic neuromodulation as reflected in altered spontaneous pupil fluctuations. Abnormalities were already present before the onset of symptoms and were rescued by the selective expression of MeCP2 in cholinergic circuits. Hence, we trained a neural network (ConvNetACh) to recognize, with 97% accuracy, patterns of these arousal fluctuations in mice with enhanced cholinergic sensitivity (LYNX1-deficient). ConvNetACh then successfully detected impairments in all ASD mouse models tested except in MeCP2-rescued mice. By retraining only the last layers of ConvNetACh with heart rate variation data (a similar proxy of arousal) directly from Rett syndrome patients, we generated ConvNetPatients, a neural network capable of distinguishing them from typically developing subjects. Even with small cohorts of rare patients, our approach exhibited significant accuracy before (80% in the first and second year of life) and into regression (88% in stage III patients). Thus, transfer learning across species and modalities establishes spontaneous arousal fluctuations combined with deep learning as a robust noninvasive, quantitative, and sensitive translational biomarker for the rapid and early detection of neurodevelopmental disorders before major symptom onset.

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

Study Objectives

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

Methods

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

Results

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

Conclusion

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

The effects of a 120-minute nap on sleepiness, fatigue, and performance during 16-hour night shifts: A pilot study

Objective

To investigate sleepiness, fatigue, and performance following a 120‐minute nap during simulated 16‐hour night shifts based on subjective and objective assessments.

Methods

Fourteen females participated in this crossover comparative study. Three experimental nap conditions were used: naps from 22:00 to 00:00 (22‐NAP), 00:00 to 02:00 (00‐NAP), and 02:00 to 04:00 (02‐NAP), respectively. Measurement items were sleep parameters, sublingual temperature, a Visual Analog Scale for sleepiness and fatigue, a single‐digit mental arithmetic task (for 10 minutes), and heart rate variability. Participants wore an ActiGraph to estimate their sleep state.

Results

There was no difference in the sleep parameters at the time of naps among the three conditions. Immediately following a 120‐minute nap, sleepiness and fatigue increased, and the number of calculations performed in the single‐digit mental arithmetic task decreased in any of the conditions. In particular, immediately after the 02‐NAP, fatigue and high‐frequency power (HF) were higher than after the 22‐NAP. In the early morning (from 05:00 to 09:00), in the 22‐NAP, sleepiness and fatigue increased, and performance and sublingual temperature decreased more than in the 00‐NAP and 02‐NAP. Furthermore, the ratio of errors was significantly lower in the 00‐NAP than in the 22‐NAP in the early morning.

Conclusions

A 120‐minute nap taken from 22:00 to 02:00 may cause temporary sleepiness after waking, increase fatigue and reduce performance. Greater attention should be given to naps taken at a later time (ie, 02‐NAP). In addition, taking a nap starting at 00:00 might decrease the risks of errors in the morning.

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ, θ, α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ, θ, α and β. The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain–peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states.

Entropic Approach to the Detection of Crucial Events

In this paper, we establish a clear distinction between two processes yielding anomalous diffusion and 1/f noise. The first process is called Stationary Fractional Brownian Motion (SFBM) and is characterized by the use of stationary correlation functions. The second process rests on the action of crucial events generating ergodicity breakdown and aging effects. We refer to the latter as Aging Fractional Brownian Motion (AFBM). To settle the confusion between these different forms of Fractional Brownian Motion (FBM) we use an entropic approach properly updated to incorporate the recent advances of biology and psychology sciences on cognition. We show that although the joint action of crucial and non-crucial events may have the effect of making the crucial events virtually invisible, the entropic approach allows us to detect their action. The results of this paper lead us to the conclusion that the communication between the heart and the brain is accomplished by AFBM processes.

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

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

High rebound mattress toppers facilitate core body temperature drop and enhance deep sleep in the initial phase of nocturnal sleep

Recently, several new materials for mattresses have been introduced. Although some of these, such as low rebound (pressure-absorbing/memory foam) and high rebound mattresses have fairly different characteristics, effects of these mattresses on sleep have never been scientifically evaluated. In the current study, we have evaluated effects of a high rebound mattress topper [HR] on sleep and its associated physiology, and the effects were compared to those of a low rebound mattress toppers (LR) in healthy young (n = 10) and old (n = 20) adult males with a randomized, single-blind, cross over design. We found that sleeping with HR compared to LR induced a larger decline in core body temperature (CBT) in the initial phase of nocturnal sleep both in young (minimum CBT: 36.05 vs 36.35°C) and old (minimum CBT: 36.47 vs. 36.55°C) subjects, and declines in the CBT were associated with increases in deep sleep/delta power (+27.8% in young and +24.7% in old subjects between 11:00-01:00). We also found significantly smaller muscle activities during roll over motions with HR (-53.0 to -66.1%, depending on the muscle) during a separate daytime testing. These results suggest that sleeping with HR in comparison to with LR, may facilitate restorative sleep at the initial phase of sleep.

First-night effect on cardiac autonomic function in different female reproductive states

Decreases in heart rate variability, a marker of autonomic nervous system function, are associated with increased cardiovascular mortality. Heart rate variability increases in non-rapid eye movement sleep, peaking in slow-wave sleep. Therefore, decreasing the amount of deep sleep, for example, by introducing patients to a sleep laboratory environment, could decrease heart rate variability, increasing cardiovascular risk. We studied four groups of women with no previous sleep laboratory experience: young [n = 11, 23.1 (0.5) years]; perimenopausal [n = 15, 48.0 (0.4) years]; postmenopausal without hormone therapy [n = 22, 63.4 (0.8) years]; and postmenopausal on hormone therapy [n = 16, 63.1 (0.9) years], using a cross-sectional design. Polysomnography including electrocardiogram was performed over two consecutive nights. Heart rate variability was assessed overnight, and the first-night effect on heart rate variability was analysed. Furthermore, correlations between heart rate variability and sleep variables were analysed. Using combined groups, only minor changes were observed in non-linear heart rate variability, indicating increased parasympathetic tone from the first to the second night. No group differences in first-night effect were seen. Heart rate variability and sleep variables were not significantly correlated. Heart rate variability decreased with increasing age, and it was lowest in the postmenopausal women on hormone therapy. We conclude that a first night in a sleep laboratory elicits only minimal changes in overnight vagally mediated non-linear heart rate variability in women irrespective of reproductive state. This finding warrants further analyses in different sleep stages, but suggests that changes in sleep architecture per se do not predict the autonomic strain of a poor night.

An Integrated Model of Emotional Problems, Beta Power of Electroencephalography, and Low Frequency of Heart Rate Variability after Childhood Trauma in a Non-Clinical Sample: A Path Analysis Study

Childhood trauma is known to be related to emotional problems, quantitative electroencephalography (EEG) indices, and heart rate variability (HRV) indices in adulthood, whereas directions among these factors have not been reported yet. This study aimed to evaluate pathway models in young and healthy adults: (1) one with physiological factors first and emotional problems later in adulthood as results of childhood trauma and (2) one with emotional problems first and physiological factors later. A total of 103 non-clinical volunteers were included. Self-reported psychological scales, including the Childhood Trauma Questionnaire (CTQ), State-Trait Anxiety Inventory, Beck Depression Inventory, and Affective Lability Scale were administered. For physiological evaluation, EEG record was performed during resting eyes closed condition in addition to the resting-state HRV, and the quantitative power analyses of eight EEG bands and three HRV components were calculated in the frequency domain. After a normality test, Pearson's correlation analysis to make path models and path analyses to examine them were conducted. The CTQ score was significantly correlated with depression, state and trait anxiety, affective lability, and HRV low-frequency (LF) power. LF power was associated with beta2 (18-22 Hz) power that was related to affective lability. Affective lability was associated with state anxiety, trait anxiety, and depression. Based on the correlation and the hypothesis, two models were composed: a model with pathways from CTQ score to affective lability, and a model with pathways from CTQ score to LF power. The second model showed significantly better fit than the first model (AICmodel1 = 63.403 > AICmodel2 = 46.003), which revealed that child trauma could affect emotion, and then physiology. The specific directions of relationships among emotions, the EEG, and HRV in adulthood after childhood trauma was discussed.

Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults

Study Objectives

To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy.

Methods

Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-by-epoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set.

Results

The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%.

Conclusions

The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.

Nocturnal heart rate variability in 1-year-old infants analyzed by using the Least Square Cosine Spectrum Method

BACKGROUND

To evaluate the dynamic nature of nocturnal heart rate variability, RR intervals recorded with a wearable heart rate sensor were analyzed using the Least Square Cosine Spectrum Method.

METHODS

Six 1-year-old infants participated in the study. A wearable heart rate sensor was placed on their chest to measure RR intervals and 3-axis acceleration. Heartbeat time series were analyzed for every 30 s using the Least Square Cosine Spectrum Method, and an original parameter to quantify the regularity of respiratory-related heart rate rhythm was extracted and referred to as "RA (RA-COSPEC: Respiratory Area obtained by COSPEC)." The RA value is higher when a cosine curve is fitted to the original data series.

RESULTS

The time sequential changes of RA showed cyclic changes with significant rhythm during the night. The mean cycle length of RA was 70 ± 15 min, which is shorter than young adult's cycle in our previous study. At the threshold level of RA greater than 3, the HR was significantly decreased compared with the RA value less than 3.

CONCLUSIONS

The regularity of heart rate rhythm showed dynamic changes during the night in 1-year-old infants. Significant decrease of HR at the time of higher RA suggests the increase of parasympathetic activity. We suspect that the higher RA reflects the regular respiratory pattern during the night. This analysis system may be useful for quantitative assessment of regularity and dynamic changes of nocturnal heart rate variability in infants.

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

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

Effects of Five Nights under Normobaric Hypoxia on Sleep Quality

PURPOSE

The purpose of this study was to evaluate the effects of five nights' sleep under normobaric hypoxia on ventilatory acclimatization and sleep quality.

METHODS

Seven men initially slept for six nights under normoxia and then for five nights under normobaric hypoxia equivalent to a 2000-m altitude. Nocturnal polysomnograms (PSGs), arterial blood oxygen saturation (SpO2), and respiratory events were recorded on the first and fifth nights under both conditions.

RESULTS

The hypoxic ventilatory response (HVR), hypercapnic ventilatory response (HCVR), and resting end-tidal CO2 (resting PETCO2) were measured three times during the experimental period. The duration of slow-wave sleep (SWS: stage N3) and the whole-night delta (1-3 Hz) power of nonrapid eye movement (NREM) sleep EEG decreased on the first night under hypoxia. This hypoxia-induced sleep quality deterioration on the first night was accompanied by a lower mean and minimum SpO2, a longer time spent with SpO2 below 90% (<90% SpO2 time), and more episodes of respiratory disturbance. On the fifth night, the SWS duration and whole-night delta power did not differ between the conditions. Although the mean SpO2 under hypoxia was still lower than under normoxia, the minimum SpO2 increased, and the <90% SpO2 time and number of episodes of respiratory disturbance decreased during the five nights under hypoxia. The HVR increased and resting PETCO2 decreased after five nights under hypoxia.

CONCLUSIONS

The results suggest that five nights under hypoxia improves the sleep quality. This may be derived from improvements of respiratory disturbances, the minimum SpO2, and <90% SpO2 time.

Differential changes and interactions of autonomic functioning and sleep architecture before and after 50 years of age

We hypothesize that the time when age-related changes in autonomic functioning and in sleep structure occur are different and that autonomic functioning modulates sleep architecture differently before and after 50 years of age. Sixty-eight healthy subjects (aged 20 to 79 years old, 49 of them women) were enrolled. Correlation analysis revealed that wake after sleep onset, the absolute and relative value of stage 1 (S1; S1%), and relative value of stage 2 (S2) were positively correlated with age; however, sleep efficiency, stage 3 (S3), S3%, and rapid-eye-movement latency (REML) were negatively correlated with age. Significant degenerations of sleep during normal aging were occurred after 50 years of age; however, significant declines of autonomic activity were showed before 50 years of age. Before 50 years of age, vagal function during sleep was negatively correlated with arousal index; however, after 50 years of age, it was positively correlated with S1 and S1%. In addition, sympathetic activity during wake stage was positively related to S2% only after 50 years of age. Our results imply that the age-related changes in autonomic functioning decline promptly as individuals leave the younger part of their adult life span and that age-related changes in sleep slowly develop as individuals enter the older part of their adult life span. Furthermore, while various aspects of sleep architecture are modulated by both the sympathetic and vagal nervous systems during adult life span, the sleep quality is mainly correlated with the sympathetic division after 50 years of age.