Heart rate variability: a tool to explore the sleeping brain?

Different heart rate variability profile during sleep in mid-later life adults with remitted early-onset versus late-onset depression

BACKGROUND

In mid-later life adults, early-onset and late-onset (i.e., onset ≥50 years) depression appear to be underpinned by different pathophysiology yet have not been examined in relation to autonomic function. Sleep provides an opportunity to examine the autonomic nervous system as the physiology changes across the night. Hence, we aimed to explore if autonomic profile is altered in mid-later life adults with remitted early-onset, late-onset and no history of lifetime depression.

METHODS

Participants aged 50-90 years (n = 188) from a specialised clinic underwent a comprehensive clinical assessment and completed an overnight polysomnography study. General Linear Models were used to examine the heart rate variability differences among the three groups for four distinct sleep stages and the wake after sleep onset. All analyses controlled for potential confounders - age, sex, current depressive symptoms and antidepressant usage.

RESULTS

For the wake after sleep onset, mid-later life adults with remitted early-onset depression had reduced standard deviation of Normal to Normal intervals (SDNN; p = .014, d = -0.64) and Shannon Entropy (p = .004, d = -0.46,) than those with no history of lifetime depression. Further, the late-onset group showed a reduction in high-frequency heart rate variability (HFn.u.) during non-rapid eye movement sleep stage 2 (N2; p = .005, d = -0.53) and non-rapid eye movement sleep stage 3 (N3; p = .009, d = -0.55) when compared to those with no lifetime history.

LIMITATIONS

Causality between heart rate variability and depression cannot be derived in this cross-sectional study. Longitudinal studies are needed to examine the effects remitted depressive episodes on autonomic function.

CONCLUSION

The findings suggest differential autonomic profile for remitted early-onset and late-onset mid-later life adults during sleep stages and wake periods. The differences could potentially serve as peripheral biomarkers in conjunction with more disease-specific markers of depression to improve diagnosis and prognosis.

Vortioxetine's impact on the autonomic nervous system in depressed children and adolescents: analysis of the heart rate variability

Relationship between depressive disorder and autonomic nervous system has been already discussed. Reduced emotional regulation is supposed to be associated with prefrontal hypofunction and subcortical hyperactivity. The aim of this study was to determine the effect of vortioxetine on heart rate variability (HRV), a parameter of cardiac autonomic regulation, in depressed hospitalized paediatric patients and assess the clinical effectiveness of the drug in this population. We performed repeated polysomnography analyses at admission and after a short treatment in hospital (15.2 days on average) and measured various HRV parameters (RRi, pNN50, RMSSD, LF-HRV, HF-HRV) during wakefulness, N3 and REM sleep stages. Out of 27 study subjects, 67% have improved depression symptoms as well as anxiety and subjective sleep quality after short vortioxetine treatment. We have found a significant decrease in parasympathetic parameters pNN50, RMSSD and HF-HRV during N3 sleep phase, though not exclusively among vortioxetine responders. The anticipated increase in cardiovagal regulation after vortioxetine treatment was not demonstrated in this pilot study, possibly due to the drug's multimodal mechanism and impact on the nucleus tractus solitarii, particularly its antagonism on 5HT-3 receptors. Application of selective drugs could further explain the effect of vortioxetine on HRV in depressed patients.

Mapping Early Brain-Body Interactions: Associations of Fetal Heart Rate Variation with Newborn Brainstem, Hypothalamic, and Dorsal Anterior Cingulate Cortex Functional Connectivity

The autonomic nervous system (ANS) regulates the body's physiology, including cardiovascular function. As the ANS develops during the second to third trimester, fetal heart rate variability (HRV) increases while fetal heart rate (HR) decreases. In this way, fetal HR and HRV provide an index of fetal ANS development and future neurobehavioral regulation. Fetal HR and HRV have been associated with child language ability and psychomotor development behavior in toddlerhood. However, their associations with postbirth autonomic brain systems, such as the brainstem, hypothalamus, and dorsal anterior cingulate cortex (dACC), have yet to be investigated even though brain pathways involved in autonomic regulation are well established in older individuals. We assessed whether fetal HR and HRV were associated with the brainstem, hypothalamic, and dACC functional connectivity in newborns. Data were obtained from 60 pregnant individuals (ages 14-42) at 24-27 and 34-37 weeks of gestation using a fetal actocardiograph to generate fetal HR and HRV. During natural sleep, their infants (38 males and 22 females) underwent a fMRI scan between 40 and 46 weeks of postmenstrual age. Our findings relate fetal heart indices to brainstem, hypothalamic, and dACC connectivity and reveal connections with widespread brain regions that may support behavioral and emotional regulation. We demonstrated the basic physiologic association between fetal HR indices and lower- and higher-order brain regions involved in regulatory processes. This work provides the foundation for future behavioral or physiological regulation research in fetuses and infants.

Altered parasympathetic activity during sleep and emotionally arousing wakefulness in frequent nightmare recallers

Nightmare disorder is characterized by dysfunctional emotion regulation and poor subjective sleep quality reflected in pathophysiological features such as abnormal arousal processes and sympathetic influences. Dysfunctional parasympathetic regulation, especially before and during rapid eye movement (REM) phases, is assumed to alter heart rate (HR) and its variability (HRV) of frequent nightmare recallers (NM). We hypothesized that cardiac variability is attenuated in NMs as opposed to healthy controls (CTL) during sleep, pre-sleep wakefulness and under an emotion-evoking picture-rating task. Based on the polysomnographic recordings of 24 NM and 30 CTL participants, we examined HRV during pre-REM, REM, post-REM and slow wave sleep, separately. Additionally, electrocardiographic recordings of resting state before sleep onset and under an emotionally challenging picture-rating task were also analyzed. Applying repeated measures analysis of variance (rmANOVA), a significant difference was found in the HR of NMs and CTLs during nocturnal segments but not during resting wakefulness, suggesting autonomic dysregulation, specifically during sleep in NMs. As opposed to the HR, the HRV values were not significantly different in the rmANOVA in the two groups, implying that the extent of parasympathetic dysregulation on a trait level might depend on the severeness of dysphoric dreaming. Nonetheless, in the group comparisons, the NM group showed increased HR and reduced HRV during the emotion-evoking picture-rating task, which aimed to model the nightmare experience in the daytime, indicating disrupted emotion regulation in NMs under acute distress. In conclusion, trait-like autonomic changes during sleep and state-like autonomic responses to emotion-evoking pictures indicate parasympathetic dysregulation in NMs.

The effects of acute exercise and a nap on heart rate variability and memory in young sedentary adults

Recent evidence suggests that the autonomic nervous system can contribute to memory consolidation during sleep. Whether fluctuations in cardiac autonomic activity during sleep following physical exercise contribute to the process of memory consolidation has not been studied. We assessed the effects of a non-rapid eye movement (NREM) nap following acute exercise on cardiac autonomic regulation assessed with heart rate variability (HRV) to examine if HRV influences memory processes. Fifty-six (59% female) healthy young adults (23.14 ± 3.74 years) were randomly allocated to either the exercise plus nap (ExNap, n = 27) or nap alone (NoExNap, n = 29) groups. The ExNap group performed a 40-minute moderate-intensity cycling, while the NoExNap group was sedentary prior to learning 45 neutral pictures for a later test. Subsequently, participants underwent a 60-minute NREM nap while measuring EKG, followed by a visual recognition test. Our results indicated that heart rate did not significantly differ between the groups (p = .243), whereas vagally mediated HRV indices were lower in the ExNap group compared to the NoExNap group (p < .05). There were no significant differences in sleep variables between the groups (p > .05). Recognition accuracy was significantly higher in the ExNap group than in the NoExNap group (p = .027). In addition, the recognition accuracy of the ExNap group was negatively associated with vagally mediated HRV (p < .05). Pre-nap acute exercise appears to attenuate parasympathetic activity and to alter the relationship between memory and cardiac autonomic activity.

Dietary polyphenols and sleep modulation: Current evidence and perspectives

Polyphenols are plant compounds with several biological activities. This review aims to summarize current knowledge on the potential role of polyphenols in modulating sleep. A total of 28 preclinical studies, 12 intervention studies and four observational studies exploring the role of polyphenol intake on sleep were identified. From animal studies, 26 out of the 28 studies found beneficial effects of polyphenols on sleep architecture. Three out of four human observational studies found a beneficial association between polyphenol intake and sleep parameters. And, among clinical intervention studies, eight from a total of 12 studies found some beneficial effect of polyphenol intake on various sleep parameters, although some discrepancies between studies were found. Overall, emerging evidence suggests a benefit of polyphenol intake on sleep. Several mechanisms of action have been suggested, ranging from effects on neurotransmitters to an action through the gut-brain axis. However, more research in this field is needed, emphasizing the use of nutritional doses in mechanistic studies and interventions targeting participants with sleep problems. This would allow to elucidate whether an additional biological effect of polyphenols is modulation of sleep, a behavior associated with adverse health outcomes.

Bright Morning Lighting Enhancing Parasympathetic Activity at Night: A Pilot Study on Elderly Female Patients with Dementia without a Pacemaker

Exposure to bright morning light (BML) entrains the master circadian clock, modulates physiological circadian rhythms, and reduces sleep-wake disturbances. However, its impact on the autonomic nervous system at night remains unclear. Here, we investigated the effects of BML exposure on parasympathetic nervous system (PSNS) and sympathetic nervous system (SNS) activity at night in elderly women. This nonrandomized controlled pilot study included female participants aged ≥ 60 years who were diagnosed with a type of dementia or cognitive disorder, excluding individuals with pacemakers. The treatment group was exposed to 2500 lx of BML, whereas the control group was exposed to 200 lx of general lighting. We measured heart rate variability to quantify ANS activity. The treatment group displayed significant increases in high-frequency (HF) power (Roy's largest root = 1.62; p < 0.001) and nonsignificant decreases in normalized low-frequency (LF%) power. The corresponding nonsignificant decreases in the low-frequency/high-frequency (LF/HF) ratio and cognitive function were correlated with PSNS activity (Roy's largest root = 1.41; p < 0.001), which improved severe dementia. BML exposure reduced SNS activity and enhanced PSNS activity at night in female participants, which improved cognitive function. Thus, BML therapy may be a useful clinical tool for alleviating cognitive decline.

Neurocognitive, mood changes, and sleepiness in patients with REM-predominant obstructive sleep apnea

PURPOSE

This article focuses on recent evidence linking rapid eye movement (REM) obstructive sleep apnea (OSA) (REM-OSA) to neurocognitive dysfunction and mood changes; the proposed mechanisms for increased risk of neurocognitive dysfunction in REM-OSA, and future research prospects.

METHODS

PubMed and Google Scholar records were examined for articles utilizing pre-defined keywords. In this work, we mainly included studies published after 2017; nevertheless, critical studies published prior to 2017 were considered.

RESULTS

REM-OSA is an under-recognized stage-related sleep-disordered breathing in which obstructive respiratory events happen chiefly in stage REM. The disorder is commonly seen amongst younger patients and females and has recently been linked to cardiometabolic complications. Although less symptomatic than non-REM-OSA and non-stage-specific OSA, current findings indicate that REM-OSA may have neurocognitive repercussions and mood changes and could be linked to insomnia, increased dreams, and nightmares.

CONCLUSION

Currently available evidence indicates that REM-OSA may present with insomnia and nightmares and could affect cognitive function and mood.

The Virtual Sleep Lab-A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto "gold standard" for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person's sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.

Rapid eye movement sleep parasympathetic activity predicts wake hyperarousal symptoms following a traumatic event

Heart rate variability (HRV) can be used to assess changes in output of the parasympathetic nervous system (PNS). Considering that patients with post-traumatic stress disorder (PTSD) often experience disturbances in sleep, arousal, and autonomic functioning, we sought to explore the association of PNS activity during sleep with hyperarousal symptoms of PTSD. Because a broad literature supports the importance of rapid eye movement (REM) sleep in PTSD, REM-sleep features were specifically examined as predictors of PTSD symptom severity. A total of 90 participants, primarily civilian and female, aged 18-40 years who had experienced a traumatic event in the last 2 years, underwent an ambulatory polysomnography (PSG) acclimation night followed by a second PSG night from which sleep physiological measures were computed. Participants underwent an ambulatory polysomnography (PSG) acclimation night followed by a second PSG night from which sleep physiological measures were computed. PTSD severity was measured using the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PCL-5). Dependent variables were total PCL-5 score as well as its hyperarousal symptom subscore. Predictors included REM latency, percentage, density, segment length, and an index of parasympathetic tone (root mean square of the successive differences in the R-R interval or RMSSD). Hierarchical regression models were conducted to analyse the association of REM features with PCL-5 total and hyperarousal subscales. Using hierarchical regression, REM-sleep RMSSD accounted for a significant proportion of the variation in outcome variables, even when accounting for other REM-sleep features. The present findings support hypothesised relationships between PTSD symptomatology and REM-sleep physiology and, specifically, that lowered parasympathetic tone in REM may be an important associate of the hyperarousal symptom cluster in PTSD.

Bidirectional associations between physical activity and sleep in older adults: a multilevel analysis using polysomnography

Although recent studies have examined the bidirectional associations between physical activity and sleep parameters, few have focused on older adults utilizing objective assessments, such as polysomnography. This micro-longitudinal observational study included 92 Japanese older adults (aged 65–86 years) who underwent objective evaluations of sleep quality using polysomnography and completed subjective sleep-related questionnaires. Activity levels were assessed using an accelerometer. Polysomnography, subjective sleep-related questionnaires, and accelerometer were administered for 7 consecutive days. Multilevel models (participant-, day-level) were used to examine the temporal associations of objective and subjective sleep parameters with sedentary behavior and physical activity. In the day-level analysis, higher levels of sedentary behavior during daytime were associated with longer rapid eye movement (REM) sleep, shorter REM latency, lower levels of non-REM sleep (stage N3), and reduced delta power during daytime. Higher levels of low-intensity physical activity during daytime were associated with lower levels of REM sleep, longer REM latency, and increased stage N3 sleep in the day-level analysis. Higher levels of moderate-to-vigorous physical activity were associated with increased REM latency. Longer subjective sleep time was associated with increased next-day moderate-to-vigorous physical activity. Thus, low-intensity physical activity may provide objective benefits related to deep sleep parameters in older adults.

Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview

Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.

Effect of Cognitive-Behavioral Therapy on Nocturnal Autonomic Activity in Patients with Fibromyalgia: A Preliminary Study

Objective: fibromyalgia is a complex chronic pain syndrome characterized by widespread musculoskeletal pain, insomnia and autonomic alterations. Cognitive-behavioral therapy (CBT) is regarded as a promising treatment in fibromyalgia, but its impact on autonomic function remains uncertain. In this research, we studied the effect of CBT on autonomic functions in fibromyalgia. Methods: Twenty-five participants underwent overnight polysomnographic recordings before and after CBT programs focused on pain (CBT-P) or a hybrid modality focused on pain and insomnia (CBT-C). Sleep quality, daily pain, depression and anxiety were assessed by self-reported questionnaires. We analyzed heart rate variability (HRV) using high-frequency power (HF) as a marker for parasympathetic activity, and low-frequency power (LF) and the LF/HF ratio as relative sympathetic markers during wakefulness and at each sleep stage. Results: After treatment, 14 patients (/25, 58.0%) reported improvement in their sleep: 6 in the CBT-P condition (/12, 50%), and 8 in the CBT-C condition (/13, 61.5%). We found that, regardless of the type of CBT, patients who reported improvement in sleep quality (n = 14, 58%) had an increase in HF during stages N2 (p < 0.05) and N3 (p < 0.05). These changes were related to improvement in sleep quality (N2, r = −0.43, p = 0.033) but not to pain, depression or anxiety. Conclusions: This study showed an improvement in parasympathetic cardiac control during non-rapid-eye-movement sleep following CBT in fibromyalgia participants who reported better sleep after this therapy. CBT may have a cardio-protective effect and HRV could be used as a sleep monitoring tool in fibromyalgia.

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.

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.

Sex-Related Differences in Polygraphic Parameters in a Population of Patients with Obstructive Sleep Apnea Syndrome

BACKGROUND

Sex-related differences in the prevalence and clinical presentation of Obstructive Sleep Apnea Syndrome (OSAS) have been widely documented. The aim of this study was to investigate the influence of patients' sex on polygraphic parameters with particular attention to sleep autonomic changes in a population of OSAS patients.

METHODS

Sixty OSAS patients aged 55-65 years (30 men, 30 women) were enrolled. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI) and daytime sleepiness with the Epworth Sleepiness Scale (ESS). The presence of respiratory events and autonomic changes during the night was investigated by polygraphy.

RESULTS

Similar main cardiovascular risk factors prevalence was observed in both men and women. We observed a significant difference in PSQI (higher in women, p=0.0001) and ESS (higher in men, p=0.004) scores. Snoring (p=0.033), supine AHI (p=0.004), T90 (p=0.021), LO2 (p=0.0001), LF/HF ratio and LF (p=0.0001) were significantly higher in men. Sex differences in PSQI mean score and LF/HF ratio variability were preserved in all the subgroups of OSA severity.

CONCLUSION

The influence of sex in modulating cardiovascular risk is a widely discussed topic. In our study, men showed more severe polygraphic parameters and an increase in LF/HF ratio compared to women. The results of our investigation suggest the relevance of delivering information about the different expressions of OSAS in men and women in order to improve diagnostic skills and in-depth prevention approaches.

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.

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

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

An artificial neural network for automated behavioral state classification in rats

Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.

Phenotyping REM OSA by means of peripheral arterial tone-based home sleep apnea testing and polysomnography: A critical assessment of the sensitivity and specificity of both methods

The clinical relevance of rapid eye movement sleep-related obstructive sleep apnea (REM OSA) is supported by its associated adverse health outcomes and impact on optimal treatment strategies. To date, no assessment of REM OSA phenotyping performance has been conducted for any type of sleep testing technology. The objective of this study was to assess this for polysomnography and peripheral arterial tone-based home sleep apnea testing (PAT HSAT). In a dataset comprising 261 participants, the sensitivity and specificity of the agreement on REM OSA phenotyping was assessed for two independent scorings of polysomnography and a synchronously administered PAT HSAT. The sensitivity and specificity of REM OSA phenotyping were 0.87 and 0.89, respectively, for the polysomnography inter-scorer comparison, and 0.68 and 0.97 for the PAT HSAT on a single-night basis, using the conventional minimum required rapid eye movement sleep time of 30 min. Polysomnography-based REM OSA phenotyping was found to be sensitive and specific even for a single-night testing protocol. Peripheral arterial tone-based REM OSA phenotyping showed a lower sensitivity but a slightly higher specificity compared to polysomnography. In order to increase performance and conclusiveness of peripheral arterial tone-based REM OSA phenotyping, a multi-night protocol of 2-5 nights could be considered. Finally, the minimum required rapid eye movement sleep time could be lowered from the conventional 30 min to 15 min without significantly lowering REM OSA phenotyping sensitivity and specificity, while increasing the level of phenotyping conclusiveness.

Increased afternoon step count increases heart rate variability in patients with cardiovascular risk factors

AIMS AND OBJECTIVES

The present study investigated whether morning or afternoon activity is more effective at increasing the high-frequency (HF) index, a parasympathetic index, in patients with cardiovascular risk factors.

BACKGROUND

A decreased HF index, a heart rate variability (HRV) parameter, is a well-established marker of poor cardiovascular prognosis. Because blood pressure and sympathetic tone are higher in the morning, physical activity and exercise in the afternoon has been recommended for patients with cardiovascular diseases. However, there have been no reports concerning the superior effects of afternoon exercise on parasympathetic activity and sleep.

DESIGN

This observational study was a post hoc comparison.

METHODS

Patients' physical activity was measured for 1 month to determine their habits. Patients' HF index was measured by 24-h Holter electrocardiography. The study enrolled 56 patients. Each patient's morning step count (before lunch) and afternoon step count (between lunch and dinner) were compared. We adhered to the STROBE guidelines in the present study.

RESULTS

Thirty-one patients took more steps in the morning, and 25 patients took more steps in the afternoon. The present study showed that those who took more steps in the afternoon had a significantly higher HF index during the first hour after sleep onset and during sleep than those who took more steps in the morning (p = .003, .047).

CONCLUSIONS

The present study showed that those who took more steps in the afternoon had a significantly higher HF index during the first hour after sleep onset and a higher HF index during sleep than those who took more steps in the morning.

RELEVANCE TO CLINICAL PRACTICE

Exercise in the afternoon may improve the prognosis in patients with cardiovascular disease by not only preventing excessive blood pressure, afterload, and sympathetic tone but also positively influencing the parasympathetic system and sleep.

Autonomic dysfunction in SARS-COV-2 infection acute and long-term implications COVID-19 editor's page series

Abstract

The autonomic nervous system (ANS) is a complex network of nerves originating in the brain, brain stem, spinal cord, heart and extracardiac organs that regulates neural and physiological responses to internal and external environments and conditions. A common observation among patients with the 2019 Coronavirus (CoV) (SARS-severe acute respiratory syndrome CoV-2) (SARS-CoV-2) or COVID-19 [CO for corona, VI for virus, D for disease and 19 for when the outbreak was first identified (31 December 2019)] in the acute and chronic phases of the disease is tachycardia, labile blood pressure, muscular fatigue and shortness of breath. Because abnormalities in the ANS can contribute to each of these symptoms, herein a review of autonomic dysfunction in SARS-COV-2 infection is provided to guide diagnostic testing, patient care and research initiatives.

Graphic abstract

The autonomic nervous system is a complex network of nerves originating in the brain, brain stem, spinal cord, heart and extracardiac organs that regulates neural and physiological responses to internal and external environments and conditions. A common collection of signs and symptoms among patients with the 2019 Coronavirus (CoV) (SARS-severe acute respiratory syndrome CoV-2) (SARS-CoV-2) or COVID-19 [CO for corona, VI for virus, D for disease and 19 for when the outbreak was first identified (31 December 2019)] is tachycardia, labile blood pressure, muscular fatigue and shortness of breath. Abnormalities in the autonomic nervous system (ANS) can contribute to each of these identifiers, potentially offering a unifying pathobiology for acute, subacute and the long-term sequelae of SARS-CoV-2 infection (PASC) and a target for intervention.

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.

Heart rate variability during wakefulness as a marker of obstructive sleep apnea severity

STUDY OBJECTIVES

Patients with obstructive sleep apnea (OSA) exhibit heterogeneous heart rate variability (HRV) during wakefulness and sleep. We investigated the influence of OSA severity on HRV parameters during wakefulness in a large international clinical sample.

METHODS

1247 subjects (426 without OSA and 821 patients with OSA) were enrolled from the Sleep Apnea Global Interdisciplinary Consortium. HRV parameters were calculated during a 5-minute wakefulness period with spontaneous breathing prior to the sleep study, using time-domain, frequency-domain and nonlinear methods. Differences in HRV were evaluated among groups using analysis of covariance, controlling for relevant covariates.

RESULTS

Patients with OSA showed significantly lower time-domain variations and less complexity of heartbeats compared to individuals without OSA. Those with severe OSA had remarkably reduced HRV compared to all other groups. Compared to non-OSA patients, those with severe OSA had lower HRV based on SDNN (adjusted mean: 37.4 vs. 46.2 ms; p < 0.0001), RMSSD (21.5 vs. 27.9 ms; p < 0.0001), ShanEn (1.83 vs. 2.01; p < 0.0001), and Forbword (36.7 vs. 33.0; p = 0.0001). While no differences were found in frequency-domain measures overall, among obese patients there was a shift to sympathetic dominance in severe OSA, with a higher LF/HF ratio compared to obese non-OSA patients (4.2 vs. 2.7; p = 0.009).

CONCLUSIONS

Time-domain and nonlinear HRV measures during wakefulness are associated with OSA severity, with severe patients having remarkably reduced and less complex HRV. Frequency-domain measures show a shift to sympathetic dominance only in obese OSA patients. Thus, HRV during wakefulness could provide additional information about cardiovascular physiology in OSA patients.

CLINICAL TRIAL INFORMATION

A Prospective Observational Cohort to Study the Genetics of Obstructive Sleep Apnea and Associated Co-Morbidities (German Clinical Trials Register - DKRS, DRKS00003966) https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00003966.

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

Methods developed for automatic sleep stage detection make use of large amounts of data in the form of polysomnographic (PSG) recordings to build predictive models. In this study, we investigate the effect of several dimensionality reduction techniques, i.e., principal component analysis (PCA), factor analysis (FA), and autoencoders (AE) on common classifiers, e.g., random forests (RF), multilayer perceptron (MLP), long-short term memory (LSTM) networks, for automated sleep stage detection. Experimental testing is carried out on the MGH Dataset provided in the "You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018". The signals used as input are the six available (EEG) electoencephalographic channels and combinations with the other PSG signals provided: ECG - electrocardiogram, EMG - electromyogram, respiration based signals - respiratory efforts and airflow. We observe that a similar or improved accuracy is obtained in most cases when using all dimensionality reduction techniques, which is a promising result as it allows to reduce the computational load while maintaining performance and in some cases also improves the accuracy of automated sleep stage detection. In our study, using autoencoders for dimensionality reduction maintains the performance of the model, while using PCA and FA the accuracy of the models is in most cases improved.

An unbiased, efficient sleep-wake detection algorithm for a population with sleep disorders: change point decoder

STUDY OBJECTIVES

The usage of wrist-worn wearables to detect sleep-wake states remains a formidable challenge, particularly among individuals with disordered sleep. We developed a novel and unbiased data-driven method for the detection of sleep-wake and compared its performance with the well-established Oakley algorithm (OA) relative to polysomnography (PSG) in elderly men with disordered sleep.

METHODS

Overnight in-lab PSG from 102 participants was compared with accelerometry and photoplethysmography simultaneously collected with a wearable device (Empatica E4). A binary segmentation algorithm was used to detect change points in these signals. A model that estimates sleep or wake states given the changes in these signals was established (change point decoder, CPD). The CPD's performance was compared with the performance of the OA in relation to PSG.

RESULTS

On the testing set, OA provided sleep accuracy of 0.85, wake accuracy of 0.54, AUC of 0.67, and Kappa of 0.39. Comparable values for CPD were 0.70, 0.74, 0.78, and 0.40. The CPD method had sleep onset latency error of -22.9 min, sleep efficiency error of 2.09%, and underestimated the number of sleep-wake transitions with an error of 64.4. The OA method's performance was 28.6 min, -0.03%, and -17.2, respectively.

CONCLUSIONS

The CPD aggregates information from both cardiac and motion signals for state determination as well as the cross-dimensional influences from these domains. Therefore, CPD classification achieved balanced performance and higher AUC, despite underestimating sleep-wake transitions. The CPD could be used as an alternate framework to investigate sleep-wake dynamics within the conventional time frame of 30-s epochs.

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study

Background

Polysomnography (PSG) is considered the only reliable way to distinguish between different sleep stages. Wearable devices provide objective markers of sleep; however, these devices often rely only on accelerometer data, which do not enable reliable sleep stage detection. The alteration between sleep stages correlates with changes in physiological measures such as heart rate variability (HRV). Utilizing HRV measures may thus increase accuracy in wearable algorithms.

Objective

We examined the validity of the Firstbeat sleep analysis method, which is based on HRV and accelerometer measurements. The Firstbeat method was compared against PSG in a sample of healthy adults. Our aim was to evaluate how well Firstbeat distinguishes sleep stages, and which stages are most accurately detected with this method.

Methods

Twenty healthy adults (mean age 24.5 years, SD 3.5, range 20-37 years; 50% women) wore a Firstbeat Bodyguard 2 measurement device and a Geneactiv actigraph, along with taking ambulatory SomnoMedics PSG measurements for two consecutive nights, resulting in 40 nights of sleep comparisons. We compared the measures of sleep onset, wake, combined stage 1 and stage 2 (light sleep), stage 3 (slow wave sleep), and rapid eye movement (REM) sleep between Firstbeat and PSG. We calculated the sensitivity, specificity, and accuracy from the 30-second epoch-by-epoch data.

Results

In detecting wake, Firstbeat yielded good specificity (0.77), and excellent sensitivity (0.95) and accuracy (0.93) against PSG. Light sleep was detected with 0.69 specificity, 0.67 sensitivity, and 0.69 accuracy. Slow wave sleep was detected with 0.91 specificity, 0.72 sensitivity, and 0.87 accuracy. REM sleep was detected with 0.92 specificity, 0.60 sensitivity, and 0.84 accuracy. There were two measures that differed significantly between Firstbeat and PSG: Firstbeat underestimated REM sleep (mean 18 minutes, P=.03) and overestimated wake time (mean 14 minutes, P<.001).

Conclusions

This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. The Firstbeat method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation.

Reduced Electromyographic Fatigue Threshold After Performing a Cognitive Fatiguing Task

ABSTRACT

Ferris, JR, Tomlinson, MA, Ward, TN, Pepin, ME, and Malek, MH. Reduced electromyographic fatigue threshold after performing a cognitive fatiguing task. J Strength Cond Res 35(1): 267-274, 2021-Cognitive fatigue tasks performed before exercise may reduce exercise capacity. The electromyographic fatigue threshold (EMGFT) is the highest exercise intensity that can be maintained without significant increase in the electromyography (EMG) amplitude vs. time relationship. To date, no studies have examined the effect of cognitive fatigue on the estimation of the EMGFT. The purpose of this study, therefore, was to determine whether cognitive fatigue before performing exercise reduces the estimated EMGFT. Eight healthy college-aged men were recruited from a university student population and visited the laboratory on multiple occasions. In a randomized order, subjects performed either the cognitive fatigue task (AX continuous performance test) for 60 minutes on one visit (experimental condition) or watched a video on trains for 60 minutes on the other visit (control condition). After each condition, subjects performed the incremental single-leg knee-extensor ergometry test while the EMG amplitude was recorded from the rectus femoris muscle and heart rate was monitored throughout. Thereafter, the EMGFT was calculated for each subject for each visit and compared using paired samples t-test. For exercise outcomes, there were no significant mean differences for maximal power output between the 2 conditions (control: 51 ± 5 vs. fatigue: 50 ± 3 W), but there was a significant decrease in EMGFT between the 2 conditions (control: 31 ± 3 vs. fatigue: 24 ± 2 W; p = 0.013). Moreover, maximal heart rate was significantly different between the 2 conditions (control: 151 ± 5 vs. fatigue: 132 ± 6; p = 0.027). These results suggest that performing the cognitive fatiguing task reduces the EMGFT with a corresponding reduction in maximal heart rate response.

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.

Social Context Influences Resting Physiology in Dogs

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

Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography

This paper presents a combined use of actigraphy and ballistocardiography to measure sleep stages without a disruptive sleep environment. Although polysomnography (PSG) is considered the gold standard for measuring sleep stages, the intrusive setup may lead to an unnatural sleep and impact the actual sleep quality. To address this issue a novel approach to measure sleep stages is presented by combining the acceleration measurements of actigraphy with the cardiological measurements of ballistocardiography. The combined measurements are compared with PSG for verification of the closest match possible with minimal interference for the sleeping individual. The experimental results of the study show that sleep/wake states can be classified accurately using the integrated approach.

Obstructive sleep apnea during REM sleep and daytime cerebral functioning: A regional cerebral blood flow study using high-resolution SPECT

Obstructive sleep apnea (OSA) predominantly during rapid eye movement (REM) sleep may have impacts on brain health, even in milder OSA cases. Here, we evaluated whether REM sleep OSA is associated with abnormal daytime cerebral functioning using high-resolution single-photon emission computed tomography (SPECT). We tested 96 subjects (25 F, age: 65.2 ± 6.4) with a wide range of OSA severity from no OSA to severe OSA (apnea-hypopnea index: 0-97 events/h). More respiratory events during REM sleep were associated with reduced daytime regional cerebral blood flow (rCBF) in the bilateral ventromedial prefrontal cortex and in the right insula extending to the frontal cortex. More respiratory events during non-REM (NREM) sleep were associated with reduced daytime rCBF in the left sensorimotor and temporal cortex. In subjects with a lower overall OSA severity (apnea-hypopnea index<15), more respiratory events during REM sleep were also associated with reduced daytime rCBF in the insula and extending to the frontal cortex. Respiratory events that characterized OSA during NREM versus REM sleep are associated with distinct patterns of daytime cerebral perfusion. REM sleep OSA could be more detrimental to brain health, as evidenced by reduced daytime rCBF in milder forms of OSA.

Non-contact home-adapted device estimates sleep stages in middle-aged men: A preliminary study

BACKGROUND

Sleep monitoring is essential to maintain a healthy life, especially for the elderly who want to age well. Among various forms of sleep devices, the non-wearable and home-adapted device might be preferred because it can be easily used.

OBJECTIVE

In this study, we evaluated the performance of a non-contact home-adapted device compared to polysomnography (PSG), a gold standard method.

METHODS

As a preliminary study, eight subjects were recorded over fourteen nights through PSG. The non-contact home-adapted device comprised a microwave sensor, passive infrared sensor, and smartphone application. Through the device, heart rate, respiratory rate, and body movement were collected and used to estimate sleep stages. Sleep structure was labeled in four classes: wake, REM, light, and deep sleep, and were classified using a weighted k-nearest neighbor algorithm.

RESULTS

The device correctly estimated sleep structures with an overall epoch-by-epoch accuracy of 98.65% ± 0.05% based on leave-one-out cross-validation. The device showed significantly positive correlations with PSG in sleep indices including total sleep time, sleep efficiency, and wake after sleep onset.

CONCLUSIONS

Our findings demonstrate a good performance of this non-contact and home-adapted device and suggest its suitability for sleep monitoring.

A Contact-Free, Ballistocardiography-Based Monitoring System (Emfit QS) for Measuring Nocturnal Heart Rate and Heart Rate Variability: Validation Study

Background

Heart rate (HR) and heart rate variability (HRV) measurements are widely used to monitor stress and recovery status in sedentary people and athletes. However, effective HRV monitoring should occur on a daily basis because sparse measurements do not allow for a complete view of the stress-recovery balance. Morning electrocardiography (ECG) measurements with HR straps are time-consuming and arduous to perform every day, and thus compliance with regular measurements is poor. Contact-free, ballistocardiography (BCG)-based Emfit QS is effortless for daily monitoring. However, to the best of our knowledge, there is no study on the accuracy of nocturnal HR and HRV measured via BCG under real-life conditions.

Objective

The aim of this study was to evaluate the accuracy of Emfit QS in measuring nocturnal HR and HRV.

Methods

Healthy participants (n=20) completed nocturnal HR and HRV recordings at home using Emfit QS and an ECG-based reference device (Firstbeat BG2) during sleep. Emfit QS measures BCG by a ferroelectret sensor installed under a bed mattress. HR and the root mean square of successive differences between RR intervals (RMSSD) were determined for 3-minute epochs and the sleep period mean.

Results

A trivial mean bias was observed in the mean HR (mean –0.8 bpm [beats per minute], SD 2.3 bpm, P=.15) and Ln (natural logarithm) RMSSD (mean –0.05 ms, SD 0.25 ms, P=.33) between Emfit QS and ECG. In addition, very large correlations were found in the mean values of HR (r=0.90, P<.001) and Ln RMSSD (r=0.89, P<.001) between the devices. A greater amount of erroneous or missing data (P<.001) was observed in the Emfit QS measurements (28.3%, SD 14.4%) compared with the reference device (1.1%, SD 2.3%). The results showed that 5.0% of the mean HR and Ln RMSSD values were outside the limits of agreement.

Conclusions

Based on the present results, Emfit QS provides nocturnal HR and HRV data with an acceptable, small mean bias when calculating the mean of the sleep period. Thus, Emfit QS has the potential to be used for the long-term monitoring of nocturnal HR and HRV. However, further research is needed to assess reliability in HR and HRV detection.

Lacunar stroke and heart rate variability during sleep

OBJECTIVE

Autonomic instability during sleep can influence the risk of cerebrovascular diseases. In this study, we performed a polygraphy to evaluate, heart rate variability (HRV) in a group of patients with lacunar stroke that is a condition at high risk of recurrence.

METHODS

Twenty-one lacunar stroke patients were enrolled one month after stroke occurrence. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). The presence of common sleep disorders and autonomic changes during the night was investigated by a polygraphy monitoring. Results were compared with those obtained in a group of 21 healthy subjects.

RESULTS

Patients and controls were similar for age, sex distribution and main cardiovascular risk factors with the exception of the body mass index. Significant differences were detected for all polysomnographic (PSG) parameters and for the PSQI score. By considering HRV values, patients with pathologic values (12 patients, HRV>2) showed significantly higher values in BMI (31 ± 2.9 vs. 26.6 ± 3.6, p = 0.006) and PSQI scores (7.9 ± 2.6 vs. 4.2 ± 1.9) with respect to patients with normal HRV values (nine patients, HRV≤2).

CONCLUSIONS

The results of this study suggest that the prevalence of nocturnal autonomic dysfunction is high in lacunar stroke patients even in the absence of the commonest sleep-related disorders. An abnormal HRV may contribute to increase the risk of stroke recurrence. Based on our results, the indication to polygraphy in patients with lacunar stroke should be carefully considered.

The Complexity of Dreams: a Multiscale Entropy Study on Cardiovascular Variability Series

Uncovering the physiological correlates of dreams is one of the most ambitious aim of multidisciplinary neuroscientific research. Here we investigated Autonomic Nervous System (ANS) dynamics associated with a dream recall, with a particular focus on the complexity assessment on cardiovascular control. We recorded electrocardiogram and arterial blood pressure signals from eight healthy subjects during rapid-eye-movement sleep before awakenings. Recordings were then split into two groups: the ones with a dream experience, and the ones without recall of dream experiences. The randomness of cardiovascular variability series was assessed through Sample Entropy metrics, which did not show any statistical difference between groups. On the other hand, a multiscale complexity analysis based on Distribution Entropy and Fuzzy Entropy revealed that a higher cardiovascular complexity is associated with a dreaming experience.

Heart Rhythm Analyzed via Shapelets Distinguishes Sleep From Awake

Automatically determining when a person falls asleep from easily available vital signals is important, not just for medical applications but also for practical ones, such as traffic safety or smart homes. Heart dynamics and respiration cycle couple differently during sleep and awake. Specifically, respiratory modulation of heart rhythm or respiratory sinus arrhythmia (RSA) is more prominent during sleep, as both sleep and RSA are connected to strong vagal activity. The onset of sleep can be recognized or even predicted as the increase of cardio-respiratory coupling. Here, we employ this empirical fact to design a method for detecting the change of consciousness status (sleep/awake) based only on heart rate variability (HRV) data. Our method relies on quantifying the (self)similarity among shapelets - short chunks of HRV time series - whose "shapes" are related to the respiration cycle. To test our method, we examine the HRV data of 75 healthy individuals recorded with microsecond precision. We find distinctive patterns stable across age and sex, that are not only indicative of sleep and awake, but allow to pinpoint the change from awake to sleep almost immediately. More systematic analysis along these lines could lead to a reliable prediction of sleep.

Lying Awake at Night: Cardiac Autonomic Activity in Relation to Sleep Onset and Maintenance

Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band (LF_nu) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band (HF_nu) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20–40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. LF_nu, HF_nu, and LF/HF differed between the long and the short WASO groups. A higher LF_nu and LF/HF and a lower HF_nu was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance.

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

The purpose of this investigational study was to assess the effects of melatonin replacement therapy on cardiac autonomic modulation in pinealectomized patients. This was an open-label, single-arm, single-center, proof-of-concept study consisting of a screening period, a 3-month treatment period with melatonin (3 mg/day), and a 6-month washout period. The cardiac autonomic function was determined through heart rate variability (HRV) measures during polysomnography. Pinealectomized patients (n = 5) with confirmed absence of melatonin were included in this study. Melatonin treatment increased vagal-dominated HRV indices including root mean square of the successive R-R interval differences (RMSSD) (39.7 ms, 95% CI 2.0-77.4, p = 0.04), percentage of successive R-R intervals that differ by more than 50 ms (pNN50) (17.1%, 95% CI 9.1-25.1, p = 0.003), absolute power of the high-frequency band (HF power) (1,390 ms², 95% CI 511.9-2,267, p = 0.01), and sympathetic HRV indices like standard deviation of normal R-R wave interval (SDNN) (57.6 ms, 95% CI 15.2-100.0, p = 0.02), and absolute power of the low-frequency band (LF power) (4,592 ms², 95% CI 895.6-8,288, p = 0.03). These HRV indices returned to pretreatment values when melatonin treatment was discontinued. The HRV entropy-based regularity parameters were not altered in this study, suggesting that there were no significant alterations of the REM-NREM ratios between the time stages of the study. These data show that 3 months of melatonin treatment may induce an improvement in cardiac autonomic modulation in melatonin-non-proficient patients. ClinicalTrials.gov Identifier: NCT03885258.

Autonomic cardiovascular adaptations to acute head-out water immersion, head-down tilt and supine position

PURPOSE

Thermoneutral head-out water immersion (WI) and 6° head-down tilt (HDT) have been considered as suitable models to increase central blood volume and simulate autonomic cardiovascular adaptations to microgravity, swimming or scuba diving. However, any differences in autonomic cardiovascular adaptations are still unclear. In this study, we hypothesized that WI induces a higher activation of arterial baroreceptors and the parasympathetic system.

METHODS

Ten healthy men underwent 30 min of WI, HDT, and a supine position (SP). RR intervals (RRI) and blood pressure (BP) were continuously monitored. High frequency power (HF), low frequency power (LF) and LF/HF ratio were calculated to study sympathetic and parasympathetic activities, and a spontaneous baroreflex method was used to study arterial baroreflex sensitivity (aBRS). Lung transfer of nitric oxide and carbon monoxide (TLNO/TLCO), vital capacity and alveolar volume (Vc/VA) were measured to study central blood redistribution.

RESULTS

We observed (1) a similar increase in RRI and decrease in BP; (2) a similar increase in HF power during all experimental conditions, whereas LF increased after; (3) a similar rise in aBRS; (4) a similar increase in Vc/VA and decrease in TLNO/TLCO in all experimental conditions.

CONCLUSIONS

These results showed a cardiac parasympathetic dominance to the same extent, underpinned by a similar arterial baroreflex activation during WI and HDT as well as control SP. Future studies may address their association with cold or hyperoxia to assess their ability to replicate autonomic cardiovascular adaptations to microgravity, swimming or scuba diving.

Overanxious and underslept

Are you feeling anxious? Did you sleep poorly last night? Sleep disruption is a recognized feature of all anxiety disorders. Here, we investigate the basic brain mechanisms underlying the anxiogenic impact of sleep loss. Additionally, we explore whether subtle, societally common reductions in sleep trigger elevated next-day anxiety. Finally, we examine what it is about sleep, physiologically, that provides such an overnight anxiety-reduction benefit. We demonstrate that the anxiogenic impact of sleep loss is linked to impaired medial prefrontal cortex activity and associated connectivity with extended limbic regions. In contrast, non-rapid eye movement (NREM) slow-wave oscillations offer an ameliorating, anxiolytic benefit on these brain networks following sleep. Of societal relevance, we establish that even modest night-to-night reductions in sleep across the population predict consequential day-to-day increases in anxiety. These findings help contribute to an emerging framework explaining the intimate link between sleep and anxiety and further highlight the prospect of non-rapid eye movement sleep as a therapeutic target for meaningfully reducing anxiety.

Overnight heart rate variability and next day cortisol response during simulated on-call conditions

OBJECTIVE

This study had two specific objectives, 1) to investigate the impact of being on-call on overnight heart rate variability during sleep and; 2) to examine whether being on-call overnight impacted next-day salivary cortisol concentrations.

METHODS

Data are reported from three within-subject laboratory studies (n = 24 in each study) that assessed varying on-call conditions. Healthy male participants (n = 72 total) completed a four-night laboratory protocol, comprising an adaptation night, a control night, and two counterbalanced on-call nights with varying on-call conditions. These on-call conditions were designed to determine the impact of, Study 1: the likelihood of receiving a call (definitely, maybe), Study 2: task stress (high-stress, low-stress), and Study 3: chance of missing the alarm (high-chance, low-chance), on measures of physiological stress. Overnight heart rate variability (HRV) (during sleep) was measured using two-lead electrocardiography, and time- and frequency-domain variables were analysed. Saliva samples were collected at 15-min time intervals from 0700-0800 h to determine cortisol awakening response outcomes and at four daily time points (0930 h, 1230 h, 1430 h, and 1730 h) to assess diurnal cortisol profiles.

RESULTS

There were few differences in HRV measures during sleep across all three studies. The only exception was in Study 1 where the standard deviation of the time interval between consecutive heartbeats and the root mean square of consecutive differences between heartbeats were lower across all sleep stages in the definitely condition, when compared to control. Across all three studies, being on-call overnight also had little impact on next-day cortisol awakening response (CAR), with the exception of Study 2 where the 1) CAR area under the curve with respect to increase was blunted in the high-stress condition, compared to the control and low-stress conditions and, 2) CAR reactivity was higher in low-stress condition, compared with the high-stress condition. In Study 1, diurnal cortisol area under the curve with respect to ground was lower in the on-call conditions (definitely and maybe) when compared to control. There were no differences in diurnal cortisol measures in Study 3.

CONCLUSION

This is the first study to investigate how different aspects of being on-call affect physiological stress responses. Overall, relatively little differences in measures of overnight heart rate variability and next-day cortisol response were recorded in all three studies. Further research utilising real on-call work tasks, not just on-call expectations (as in the current study) will help determine the impact of on-call work on the physiological stress response.

Association of Social Jetlag With Sleep Quality and Autonomic Cardiac Control During Sleep in Young Healthy Men

Social jetlag (SJL), the difference in sleep timing between work and free days is a consequence of the discrepancy between the individual’s circadian rhythm and the social clock. SJL is considered a chronic stress factor and has been linked to various health problems. In this field study, we examined for the first time the association between SJL and cardiac regulation during sleep. 33 healthy young men aged 20–26 years participated in the study. The median SJL was used as a cut-off value to assign the participants into two groups with either lower or higher SJL. As a marker of autonomic control we analyzed heart rate variability (HRV) and addressed intra-individual differences between workdays and free days. In subjects with higher SJL, pNN50, an indicator of vagal activity was lower in the first 3 h of sleep on workday as compared to free day (day × sleep block × group, p = 0.015), indicating a more adaptable regulation on free days, when subjects slept according to their own preference. However, in subjects with lower SJL, no HRV differences were found between the two nights. SJL showed correlation with the free day-workday differences of both pNN50 and another vagal index, RMSSD in the first 2 h of sleep (p = 0.023 and 0.047, respectively). In subjects with higher SJL, a different HF power on workdays and free days (p = 0.031) also indicated that a shift in sleep timing is accompanied by an altered parasympathetic activity in the first few hours of sleep. Furthermore, subjective sleep quality on workdays was negatively associated with SJL (p = 0.02), and subjects with higher SJL reported worse sleep quality on workday than on free day (p = 0.027). Taken together, our data call attention on the potential effect of SJL on sleep quality and vagal activity during sleep.

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

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