Human heart rate variability and sleep stages

Paintable, Fast Gelation, Highly Adhesive Hydrogels for High-fidelity Electrophysiological Monitoring Wirelessly

High-fidelity wireless electrophysiological monitoring is essential for ambulatory healthcare applications. Soft solid-like hydrogels have received significant attention as epidermal electrodes because of their tissue-like mechanical properties and high biocompatibility. However, it is challenging to develop a hydrogel electrode that provides robust contact and high adhesiveness with glabrous skin and hairy scalp for high-fidelity, continuous electrophysiological signal detection. Here, a paintable, fast gelation, highly adhesive, and conductive hydrogel is engineered for high-fidelity wireless electrophysiological monitoring. The hydrogel, consisting of gelatin, gallic acid, sodium citrate, lithium chloride, glycerol, and Tris-HCl buffer solution exhibits a reversible thermal phase transition capability, which endows it with the attributes of on-skin applicability and fast in situ gelation with 15 s, thereby addressing the aforementioned limitations. The introduction of gallic acid enhances the adhesive properties of the hydrogel, facilitating secure electrode attachment to the skin or hairy scalp. To accentuate the potential applications in at-home electrophysiological health monitoring, the hydrogel electrodes are demonstrated for high-fidelity electrocardiogram recording for one hour during various daily activities, as well as in simultaneous electroencephalogram and electrocardiogram recording during a 30 min nap.

High-Frequency Heart Rate Variability Is Prospectively Associated With Sleep Complaints in a Healthy Working Cohort

OBJECTIVE

Vagus nerve functioning, as indexed by high-frequency heart rate variability (HF-HRV), has been implicated in a wide range of mental and physical health conditions, including sleep complaints. This study aimed to test associations between HF-HRV measured during sleep (sleep HF-HRV) and subjective sleep complaints 4 years later.

METHODS

One hundred forty-three healthy employees (91% male; MAge = 47.8 years [time 2], SD = 8.3 years) of an industrial company in Southern Germany completed the Jenkins Sleep Problems Scale, participated in a voluntary health assessment, and were given a 24-hour ambulatory heart rate recording device in 2007. Employees returned for a health assessment and completed the Jenkins Sleep Problems Scale 4 years later.

RESULTS

Hierarchical regression analyses showed that lower sleep HF-HRV measured in 2007 was associated with higher self-reported sleep complaints 4 years later after controlling for covariates (rab,c = -0.096, b = -0.108, 95% CI, -0.298 to 0.081, ΔR2 = 0.009, p = .050).

CONCLUSIONS

These data are the first to show that lower sleep HF-HRV predicted worse sleep 4 years later, highlighting the importance of vagus nerve functioning in adaptability and health.

Association of intra-shift nap duration with heart rate variability in medical night shift workers

Napping during night shifts effectively reduces disease risk and improves work performance, but few studies have investigated the association between napping and physiological changes, particularly in off-duty daily lives. Changes in the autonomic nervous system precede diseases like cardiovascular disease, diabetes, and obesity. Heart rate variability is a good indicator of autonomic nervous system. This study aimed to investigate the link between night shift nap durations and heart rate variability indices in the daily lives of medical workers. As indicators of chronic and long-term alterations, the circadian patterns of heart rate variability indices were evaluated. We recruited 146 medical workers with regular night shifts and divided them into four groups based on their self-reported nap durations. Heart rate variability circadian parameters (midline-estimating statistic of rhythm, amplitude, and acrophase) were obtained by obtaining 24-h electrocardiogram on a day without night shifts, plotting the data of the heart rate variability indices as a function of time, and fitting them into periodic cosine curves. Using clinical scales, depression, anxiety, stress, fatigue, and sleepiness were assessed. Linear regression analysis revealed a positive relationship between 61-120-min naps and 24-h, daytime, and night-time heart rate variability indices, and the parasympathetic activity oscillation amplitude (indexed by high-frequency power, the square root of the mean of the sum of squares of differences between adjacent normal intervals, standard deviation of short-term R-R-interval variability) within one circadian cycle. This study indicated that napping for 61-120 min during night shifts could benefit medical workers' health, providing physiological evidence to promote nap management.

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.

Development of generalizable automatic sleep staging using heart rate and movement based on large databases

Purpose

With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography.

Methods

A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen's κ as key metrics.

Results

The fine-tuned model showed accuracy of 76.6% with Cohen's κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen's κ of 0.673 in another external validation dataset.

Conclusion

These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.

Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability

Introduction

Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV).

Methods

Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested.

Results and Discussion

In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

Normative values of resting heart rate variability in young male contact sport athletes: Reference values for the assessment and treatment of concussion

Objective

The objective of this study was to identify the main determinants of heart rate variability (HRV) in male athletes aged 14 to 21 years who practice competitive contact sports and to integrate these determinants with the aim of defining normative values of short-term HRV in the time and frequency domains.

Methods

Participants (n = 369) were aged 14 to 21 years and included 221 football players and 148 ice hockey players. HRV was measured for 5 min at rest, and standard HRV parameters in the time and frequency domains were calculated. Heart rate (HR), age, body mass index (BMI), number of sports weekly practices (WSP) and concussion history (mTBI) were considered determinants potentially able to influence HRV.

Results

Multiple regression analysis revealed that HR was the primary determinant of standard HRV parameters. The models accounted for 13% to 55% of the total variance of HRV and the contribution of HR to this model was the strongest (β ranged from -0.34 to -0.75). HR was the only determinant that significantly contributes to all HRV parameters. To counteract this dependence, we calculated HRV corrected by the mean RR interval (RRm). Such corrections do not remove any physiological differences in HRV; they simply remove the mathematical bias. HRV parameters were therefore normalized, and their normative limits were developed relative to the mean heart rate. After correction, the correlation coefficients between HR and all corrected HRV parameters were not statistically significant and ranged from -0.001 to 0.045 (p > 0.40 for all). The automatically corrected HRV calculator, which recalculates standard HRV parameters and converts them into corrected parameters in addition to determining whether a given value is within normal limits, facilitates clinical interpretation.

Conclusion

This study provides for the first time corrected normative values of short-term and resting state HRV parameters in competitive contact sport athletes aged 14 to 21 years. These values were developed independently of the major determinants of HRV. The baseline values for HRV parameters given here could be used in clinical practice when assessing and monitoring cerebral concussions. They may assist in decision making for a safe return to play.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages

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

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

PURPOSE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Discrepancies in the Time Course of Sleep Stage Dynamics, Electroencephalographic Activity and Heart Rate Variability Over Sleep Cycles in the Adaptation Night in Healthy Young Adults

OBJECTIVE

The aim of the present study was to characterize the cyclic sleep processes of sleep-stage dynamics, cortical activity, and heart rate variability during sleep in the adaptation night in healthy young adults.

METHODS

Seventy-four healthy adults participated in polysomnographic recordings on two consecutive nights. Conventional sleep variables were assessed according to standard criteria. Sleep-stage continuity and dynamics were evaluated by sleep runs and transitions, respectively. These variables were compared between the two nights. Electroencephalographic and cardiac activities were subjected to frequency domain analyses. Cycle-by-cycle analysis was performed for the above variables in 34 subjects with four sleep cycles and compared between the two nights.

RESULTS

Conventional sleep variables reflected lower sleep quality in the adaptation night than in the experimental night. Bouts of stage N1 and stage N2 were shorter, and bouts of stage Wake were longer in the adaptation night than in the experimental night, but there was no difference in stage N3 or stage REM. The normalized transition probability from stage N2 to stage N1 was higher and that from stage N2 to N3 was lower in the adaptation night, whereas that from stage N3 to other stages did not differ between the nights. Cycle-by-cycle analysis revealed that sleep-stage distribution and cortical beta EEG power differed between the two nights in the first sleep cycle. However, the HF amplitude of the heart rate variability was lower over the four sleep cycles in the adaptation night than in the experimental night.

CONCLUSION

The results suggest the distinct vulnerability of the autonomic adaptation processes within the central nervous system in young healthy subjects while sleeping in a sleep laboratory for the first time.

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.

Autonomic Modulation of Cardiac Activity Across Levels of Sleep Depth in Individuals With Depression and Sleep Complaints

OBJECTIVE

We assessed mean heart rate (HR) and HR variability (HRV) across wake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep, and across varying levels of NREM sleep depth in individuals with depression and sleep complaints.

METHODS

Retrospective polysomnographic data were obtained for 25 individuals diagnosed as having depression (84% female; mean age = 33.8 ± 12.2 years) and 31 mentally healthy controls (58.1% female; mean age = 37.2 ± 12.4 years). All were free of psychotropic and cardiovascular medication, cardiovascular disease, and sleep-related breathing disorders. HR and time-domain HRV parameters were computed on 30-second electrocardiography segments and averaged across the night for each stage of sleep and wake.

RESULTS

Compared with the control group, the depression group had higher HR across wake, REM, and all levels of NREM depth (F(1,51) = 6.3, p = .015). Significant group by sleep stage interactions were found for HRV parameters: SD of normal-to-normal intervals (SDNN; F(2.1,107.7) = 4.4, p = .014) and root mean square differences of successive R-R intervals (RMSSD; F(2.2,113.5) = 3.2, p = .041). No significant group difference was found for SDNN or RMSSD during wake (all, p ≥ .32). However, compared with the control group, the depression group had significantly lower SDNN in REM (p = .040) and all NREM stages (all p ≤ .045), and lower RMSSD during NREM 2 (p = .033) and NREM 3 (p = .034).

CONCLUSIONS

This study suggests that the abnormalities in autonomic cardiac regulation associated with depression and sleep problems are more prominent during sleep, especially NREM sleep, than during wake. This may be due to abnormalities in parasympathetic modulation of cardiac activity.

Statistical Learning Models for Sleep Quality Prediction Using Electrocardiograms

Background:

The sleep quality prediction has implications beyond trivial. It enables the holistic management of the clinical ramifications of treating sleep disorders, which include providing a foundational framework for mitigating sleep medication abuse and sleep medication dosage control due to the foreknowledge of the quality of a future sleep episode. Sleep Quality (SQ) is presented as a function of sleep stages and as such, predicting sleep quality will involve predicting the future realization of a sleep episode in terms of transition between different sleep stages. Electrocardiograms (ECG) provided by the National Sleep Research Resource (NSRR) are analyzed and a Sleep Quality (SQ) value is predicted on an interval (0,1).

Methods:

This research uses Support Vector Machines (SVM) and a polynomial regression model to forecast the Sleep Quality (SQ) of a future sleep episode. The statistical learning models are trained on the features extracted from the Electrocardiograms (ECG) signals in the training set. The datasets are composed of ECG signal from patients in the NSSR Sleep Health Heart Study (SHHS).

Results:

A confusion matrix comparing measured vs. predicted is presented as a measure of the performance of the SVM sleep stage as well as the comparison of the observed vs. predicted hypnogram in some cases. The Sleep Quality (SQ) values derived from classified forecasted PSD is compared with the measured Sleep Quality (SQ) values. Finally, a paired t-test is used to compare the predicted Sleep Quality (SQ) with the measured Sleep Quality (SQ) to determine if the difference between the two sets is significant.

Conclusion:

This research presents a simple method to forecast Sleep Quality (SQ) values. Consequently, it can be used to establish a personal Sleep Quality (SQ) history for clinical diagnosis and treatment.

Non-restorative Sleep Caused by Autonomic and Electroencephalography Parameter Dysfunction Leads to Subjective Fatigue at Wake Time in Shift Workers

Sleep is a physiological state that plays important role in the recovery of fatigue. However, the relationship between the physiological status of sleep and subjective fatigue remains unknown. In the present study, we hypothesized that the non-recovery of fatigue at wake time due to non-restorative sleep might be ascribed to changes in specific parameters of electroencephalography (EEG) and heart rate variability (HRV) in poor sleepers. Twenty healthy female shift-working nurses participated in the study. Subjective fatigue was assessed using the visual analog scale (VAS) at bedtime and wake time. During sleep on the night between 2 consecutive day shifts, the EEG powers at the frontal pole, HRV based on electrocardiograms, and distal-proximal gradient of skin temperature were recorded and analyzed. The results indicated that the subjects with high fatigue on the VAS at wake time exhibited (1) a decrease in deep non-rapid eye movement (NREM) (stageN3) sleep duration in the first sleep cycle; (2) a decrease in REM latency; (3) a decrease in ultra-slow and delta EEG powers, particularly from 30 to 65 min after sleep onset; (4) a decrease in the total power of HRV, particularly from 0 to 30 min after sleep onset; (5) an increase in the very low frequency component of HRV; and (6) a smaller increase in the distal-proximal gradient of skin temperature, than those of the subjects with low fatigue levels. The correlational and structural equation modeling analyses of these parameters suggested that an initial decrease in the total power of HRV from 0 to 30 min after sleep onset might inhibit the recovery from fatigue during sleep (i.e., increase the VAS score at wake time) via its effects on the ultra-slow and delta powers from 30 to 65 min after sleep onset, stageN3 duration in the first sleep cycle, REM latency, and distal-proximal gradient of skin temperature. These findings suggest an important role of these physiological factors in recovery from fatigue during sleep, and that interventions to modify these physiological factors might ameliorate fatigue at wake time.

An Analysis of Electrocardiograms for Instantaneous Sleep Potential Determination

Background:

The use of electrocardiograms to establish a relationship between the electrical activity of the heart and the intricacies of sleep is explored to propose a method to predict the time before sleep onset.

Recorded Electrocardiograms (ECG) from the National Sleep Research Resource (NSRR) database are analyzed to extract the frequency domain characteristics and used to develop statistical learning models to predict the time before sleep onset. This is known as Time to Sleep (TTS) and is presented as a measure of wakefulness known as Sleep Potential (SP).

Methods:

Recorded ECG signals that encapsulate a progression from stage 0 (Awake) to stage 5 are sampled at 125 Hz. The Heart Rate Variability (HRV) information is derived by extracting a sequence of R peaks from the QRS complexes. A Fast Fourier Transform (FFT) of the RR tachogram ensues and features are extracted and used to train the multi-layer neural network.

Results:

A comparison of the measuredvs.predicted values is presented to evaluate the performance of the Deep Neural Network (DNN) in predicting Sleep Potential (SP) values (time before sleep onset) from different points in the ECG derived power spectrum.

Conclusion:

The research demonstrates a way to generate information on sleep using ECG data which can be provided in real-time from various ambulatory ECG devices. Sleep Potential (SP) values can be very useful in documenting sleep history for better diagnosis and treatment of sleep disorders. It can also be used in the prevention of sleep-related accidents, especially car wrecks.

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

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

Hypoglossal Nerve Stimulation and Heart Rate Variability: Analysis of STAR Trial Responders

OBJECTIVE

Hypoglossal nerve stimulation represents a novel therapy for the treatment of moderate-severe obstructive sleep apnea; nonetheless, its cardiovascular effects are not known. We examine the effects of hypoglossal nerve stimulation on heart rate variability, a measure of autonomic function.

STUDY DESIGN

Substudy of the STAR trial (Stimulation Therapy for Apnea Reduction): a multicenter prospective single-group cohort.

SETTING

Academic and private practice centers in the United States and Europe.

SUBJECTS AND METHODS

A subset of responder participants (n = 46) from the STAR trial was randomized to therapy withdrawal or therapy maintenance 12 months after surgery. Heart rate variability analysis included standard deviation of the R-R interval (SDNN), low-frequency power of the R-R interval, and high-frequency power of the R-R interval. Analysis was performed by sleep with 5-minute sliding window epochs during baseline, 12 months, and the maintenance/withdrawal period.

RESULTS

A significant improvement from baseline to 12 months in heart rate variability was seen for SDNN and low frequency across all sleep stages. SDNN analysis demonstrated no change in the wake period (mean ± SD: 0.042 ± 0.01 vs 0.077 ± 0.07, P = .19). Reduction in SDNN was correlated to improvement in apnea-hypopnea index ( r = 0.39, P = .03). In the therapy withdrawal group, no significant changes in SDNN were seen for N1/N2, N3, or rapid eye movement sleep.

CONCLUSION

Hypoglossal nerve stimulation therapy appears to reduce heart rate variability during sleep. This reduction was not affected by a 1-week withdrawal period. Larger prospective studies are required to better understand the effect of hypoglossal nerve stimulation on autonomic dysfunction in obstructive sleep apnea.

Sleep-wake classification via quantifying heart rate variability by convolutional neural network

OBJECTIVE

Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.

APPROACH

We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities.

MAIN RESULTS

On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics.

SIGNIFICANCE

This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.

Pervasive health monitor and analysis based on multi-parameter smart armband

With the growing attention on personal health, keeping track of the health related parameters has become an important issue, which is quite useful to increase people's living quality and reduce unpredicted risks. However, conventional physical checks are discrete and transient, which is incapable for the health monitor of daily living. Dedicated to everyday physiological monitor, we have developed a multi-parameter smart armband which is able record pulse, temperature and triaxial accelerations continuously. With the wearable device and signal processing algorithm, experiments of data acquisition in the daily living have been implemented on the volunteers. The long period record of 38 hours has demonstrated its feasibility of a total record without disturbing. And both historical and cross comparisons on the parameter correlation analysis have proven the valuable health information that the armband could reveal. As an integrated sensor module, the smart armband is simple and non-obtrusive, thus opens a promising approach towards the pervasive health monitor, especially for the elder population.

Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

Resting state functional magnetic resonance imaging (rs-fMRI) provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV) derived from simultaneous electrocardiogram (ECG) recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.

REM sleep estimation based on autonomic dynamics using R-R intervals

OBJECTIVE

We developed an automatic algorithm to determine rapid eye movement (REM) sleep on the basis of the autonomic activities reflected in heart rate variations.

APPROACH

The heart rate variability (HRV) parameters were calculated using the R-R intervals from an electrocardiogram (ECG). A major autonomic variation associated with the sleep cycle was extracted from a combination of the obtained parameters. REM sleep was determined with an adaptive threshold applied to the acquired feature. The algorithm was optimized with the data from 26 healthy subjects and obstructive sleep apnea (OSA) patients and was validated with data from a separate group of 25 healthy and OSA subjects.

MAIN RESULTS

According to an epoch-by-epoch (30 s) analysis, the average of Cohen's kappa and the accuracy were respectively 0.63 and 87% for the training set and 0.61 and 87% for the validation set. In addition, the REM sleep-related information extracted from the results of the proposed method revealed a significant correlation with those from polysomnography (PSG).

SIGNIFICANCE

The current algorithm only using R-R intervals can be applied to mobile and wearable devices that acquire heart-rate-related signals; therefore, it is appropriate for sleep monitoring in the home and ambulatory environments. Further, long-term sleep monitoring could provide useful information to clinicians and patients for the diagnosis and treatments of sleep-related disorders and individual health care.

Parasympathetic Vagal Control of Cardiac Function

This brief review focuses on four new topics, with novel and clinically significant consequences, concerning the powerful influence of parasympathetic activity on cardiac function. In this short summary, we will highlight very recent and important work, published in the last 3-4 years, that (1) challenges the paradigm that parasympathetic activity to the heart is involved in the control of heart rate but plays little role in other cardiac functions, (2) characterizes important long-range synaptic pathways to parasympathetic cardiac vagal neurons that are involved in "higher" brain functions (such as arousal and emotional challenges), (3) asks whether implantable chronic vagal nerve stimulation is a promising clinical tool for treating cardiovascular diseases, and (4) describes newly identified neuropeptides and other modulators that can influence the generation and maintenance of parasympathetic activity to the heart.

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

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

Sleep disturbances and autonomic dysfunction in patients with postural orthostatic tachycardia syndrome

Many patients with postural tachycardia syndrome (PoTS) suffer from fatigue, daytime sleepiness, and sleeping disturbances. The objective of this study was to compare subjective and objective sleep quality of PoTS patients with a group of healthy controls. All patients completed a Pittsburgh Sleep Quality Index questionnaire and the Epworth Sleepiness Scale. The patients sleep architecture, heart rate, and heart rate variability (HRV) measurements were taken during one night at the sleep laboratorium. All data was collected at the Sleep Unit, at Helios Klinikum Wuppertal. Thirty-eight patients diagnosed with PoTS were compared to 31 healthy controls, matched in age and gender. Patients with PoTS reached significantly higher scores in sleep questionnaires, which means that they were more sleepy and had a lower sleep quality. Polysomnography showed a significantly higher proportion of stage 2 sleep. The results of HRV analysis in different sleep stages confirmed changes in autonomic activity in both groups. PoTS patients, however, showed a diminished variability of the low-frequency (LF) band, high-frequency (HF) band, and LF/HF ratio in different sleep stages. It can therefore be gathered that PoTS could be considered as potential differential diagnosis for sleep disturbances since PoTS patients had a subjective diminished sleep quality, reached higher levels of daytime sleepiness, and showed a higher proportion of stage 2 sleep. PoTS patients showed furthermore a reduction of LF/HF ratio variability in different sleep stages.

Optogenetic stimulation of locus ceruleus neurons augments inhibitory transmission to parasympathetic cardiac vagal neurons via activation of brainstem α1 and β1 receptors

Locus ceruleus (LC) noradrenergic neurons are critical in generating alertness. In addition to inducing cortical arousal, the LC also orchestrates changes in accompanying autonomic system function that compliments increased attention, such as during stress, excitation, and/or exposure to averse or novel stimuli. Although the association between arousal and increased heart rate is well accepted, the neurobiological link between the LC and parasympathetic neurons that control heart rate has not been identified. In this study, we test directly whether activation of noradrenergic neurons in the LC influences brainstem parasympathetic cardiac vagal neurons (CVNs). CVNs were identified in transgenic mice that express channel-rhodopsin-2 (ChR2) in LC tyrosine hydroxylase neurons. Photoactivation evoked a rapid depolarization, increased firing, and excitatory inward currents in ChR2-expressing neurons in the LC. Photostimulation of LC neurons did not alter excitatory currents, but increased inhibitory neurotransmission to CVNs. Optogenetic activation of LC neurons increased the frequency of isolated glycinergic IPSCs by 27 ± 8% (p = 0.003, n = 26) and augmented GABAergic IPSCs in CVNs by 21 ± 5% (p = 0.001, n = 26). Inhibiting α1, but not α2, receptors blocked the evoked responses. Inhibiting β1 receptors prevented the increase in glycinergic, but not GABAergic, IPSCs in CVNs. This study demonstrates LC noradrenergic neurons inhibit the brainstem CVNs that generate parasympathetic activity to the heart. This inhibition of CVNs would increase heart rate and risks associated with tachycardia. The receptors activated within this pathway, α1 and/or β1 receptors, are targets for clinically prescribed antagonists that promote slower, cardioprotective heart rates during heightened vigilant states.

Function and modulation of premotor brainstem parasympathetic cardiac neurons that control heart rate by hypoxia-, sleep-, and sleep-related diseases including obstructive sleep apnea

Parasympathetic cardiac vagal neurons (CVNs) in the brainstem dominate the control of heart rate. Previous work has determined that these neurons are inherently silent, and their activity is largely determined by synaptic inputs to CVNs that include four major types of synapses that release glutamate, GABA, glycine, or serotonin. Whereas prior reviews have focused on glutamatergic, GABAergic and glycinergic pathways, and the receptors in CVNs activated by these neurotransmitters, this review focuses on the alterations in CVN activity with hypoxia-, sleep-, and sleep-related cardiovascular diseases including obstructive sleep apnea.

The lateral paragigantocellular nucleus modulates parasympathetic cardiac neurons: a mechanism for rapid eye movement sleep-dependent changes in heart rate

Rapid eye movement (REM) sleep is generally associated with a withdrawal of parasympathetic activity and heart rate increases; however, episodic vagally mediated heart rate decelerations also occur during REM sleep. This alternating pattern of autonomic activation provides a physiological basis for REM sleep-induced cardiac arrhythmias. Medullary neurons within the lateral paragigantocellular nucleus (LPGi) are thought to be active after REM sleep recovery and play a role in REM sleep control. In proximity to the LPGi are parasympathetic cardiac vagal neurons (CVNs) within the nucleus ambiguus (NA), which are critical for controlling heart rate. This study examined brain stem pathways that may mediate REM sleep-related reductions in parasympathetic cardiac activity. Electrical stimulation of the LPGi evoked inhibitory GABAergic postsynaptic currents in CVNs in an in vitro brain stem slice preparation in rats. Because brain stem cholinergic mechanisms are involved in REM sleep regulation, we also studied the role of nicotinic neurotransmission in modulation of GABAergic pathway from the LGPi to CVNs. Application of nicotine diminished the GABAergic responses evoked by electrical stimulation. This inhibitory effect of nicotine was prevented by the alpha7 nicotinic receptor antagonist alpha-bungarotoxin. Moreover, hypoxia/hypercapnia (H/H) diminished LPGi-evoked GABAergic current in CVNs, and this inhibitory effect was also prevented by alpha-bungarotoxin. In conclusion, stimulation of the LPGi evokes an inhibitory pathway to CVNs, which may constitute a mechanism for the reduced parasympathetic cardiac activity and increase in heart rate during REM sleep. Inhibition of this pathway by nicotinic receptor activation and H/H may play a role in REM sleep-related and apnea-associated bradyarrhythmias.

Reproducibility and seasonal variation of ambulatory short-term heart rate variability in healthy subjects during a self-selected rest period and during sleep

Although ambulatory measurements of heart rate variability (HRV) are widely used, the reproducibility and seasonal variation of ambulatory sampled short-term HRV measurements in healthy participants has not been investigated before. In the present study we collected ambulatory ECGs from 19 healthy participants monthly for 12 months, and for a sub-group of 12 participants weekly for one month. Frequency-domain HRV-metrics were calculated for 5 min ECG segments during (i) a 15-min self-selected rest period (awake period), and (ii) a 30-min sleep period starting 45 min after estimated sleep onset. Total, within- and between-subject coefficient of variation (CV) and seasonal variation were estimated for ln (TP), ln (LFP), ln (HFP), ln (LF/HF), LFnu, HFnu, the mean heart period and the ECG derived respiratory frequency.The within- and between-subject CV varied considerably between different variables, from < 10% for ln (TP) and ln (LFP) to >100% for ln (LF/HF). Within- and between-subject CV of ln (HFP), LFnu and HFnu were 10-40%. A weak, but significant, seasonal variation was found for ln (TP) (p = 0.05), ln (LFP) (p<0.05) and the respiratory frequency (p<0.01), but the seasonal variation did not affect the within-subject CV. Furthermore, sample size calculations demonstrated that the reproducibility was sufficient for ambulatory HRV measurements to be used to study autonomic cardiac regulation in healthy populations.

THE RELATIONSHIP OF HRV TO SLEEP EEG AND SLEEP RHYTHM

Previous studies have shown that there exists a cycle of NREM (non-rapid eye movement)-REM (rapid eye movement) during normal human sleep, and heart rate variability (HRV) has a close relationship to sleep stages and sleep cycle. This article reports the relationship between the electroencephalographic activity and the HRV spectral power in several specific frequency bands. The authors discovered that relationships do exist between HRV and electroencephalogram (EEG) during sleep. In particular, it was found that, prior to the changes of EEG, the changes of HRV usually indicate the shift of sleep stages. HRV frequency analysis indicates that the very-low-frequency components of HRV are closely related to sleep EEG. Results show that the rhythm of the spectral power oscillations in some specific frequency bands of HRV is almost the same as the sleep cycle, which reflects the rhythm of sleep to a certain extent.

Acute stress affects heart rate variability during sleep

OBJECTIVE

Although stress can elicit profound and lasting effects on sleep, the pathways whereby stress affects sleep are not well understood. In this study, we used autoregressive spectral analysis of the electrocardiogram (EKG) interbeat interval sequence to characterize stress-related changes in heart rate variability during sleep in 59 healthy men and women.

METHODS

Participants (N = 59) were randomly assigned to a control or stress condition, in which a standard speech task paradigm was used to elicit acute stress in the immediate presleep period. EKG was collected throughout the night. The high frequency component (0.15-0.4 Hz Eq) was used to index parasympathetic modulation, and the ratio of low to high frequency power (0.04-0.15 Hz Eq/0.15-0.4 Hz Eq) of heart rate variability was used to index sympathovagal balance.

RESULTS

Acute psychophysiological stress was associated with decreased levels of parasympathetic modulation during nonrapid eye movement (NREM) and rapid eye movement sleep and increased levels of sympathovagal balance during NREM sleep. Parasympathetic modulation increased across successive NREM cycles in the control group; these increases were blunted in the stress group and remained essentially unchanged across successive NREM periods. Higher levels of sympathovagal balance during NREM sleep were associated with poorer sleep maintenance and lower delta activity.

CONCLUSIONS

Changes in heart rate variability associated with acute stress may represent one pathway to disturbed sleep. Stress-related changes in heart rate variability during sleep may also be important in association with chronic stressors, which are associated with significant morbidity and increased risk for mortality.

Heart rate variability during sleep as a function of the sleep cycle

In this work, in order to evaluate whether autonomic differences distinguish REM sleep and NREM sleep through the whole sleeping period, statistical analysis on spectral power associated with low frequency and high frequency bands were performed on the whole polysomnographic recording, considering the sleep cycle as a unit of sleep. Our results from nine subjects show that power associated with low frequency is higher in REM sleep than in NREM sleep, while power associated with high frequency is significantly higher in NREM sleep than in REM sleep. Differences between REM sleep and NREM sleep are not of the same magnitude within the whole sleep episode and, independent of sleep stages, specific trends are observable in the autonomic control of heart rate during the night.

Comparison of two different approaches in the detection of intermittent cardiorespiratory coordination during night sleep

BACKGROUND

The objective was to evaluate and to compare two completely different detection algorithms of intermittent (short-term) cardiorespiratory coordination during night sleep. The first method is based on a combination of respiratory flow and electrocardiogram recordings and determines the relative phases of R waves between successive onsets of inspiration. Intermittent phase coordination is defined as phase recurrence with accuracy alpha over at least k heartbeats. The second, recently introduced method utilizes only binary coded variations of heart rate (acceleration = 1, deceleration = 0) and identifies binary pattern classes which can be assigned to respiratory sinus arrhythmia (RSA). It is hypothesized that RSA pattern class recurrence over at least k heartbeats is strongly related with the intermittent phase coordination defined above.

RESULTS

Both methods were applied to night time recordings of 20 healthy subjects. In subjects <45 yrs and setting k = 3 and alpha = 0.03, the phase and RSA pattern recurrence were highly correlated. Furthermore, in most subjects the pattern predominance (PP) showed a pronounced oscillation which is most likely linked with the dynamics of sleep stages. However, the analysis of bivariate variation and the use of surrogate data suggest that short-term phase coordination mainly resulted from central adjustment of heart rate and respiratory rate rather than from real phase synchronization due to physiological interaction.

CONCLUSION

Binary pattern analysis provides essential information on short-term phase recurrence and reflects nighttime sleep architecture, but is only weakly linked with true phase synchronization which is rare in physiological processes of man.

Interpreting heart rate variability sleep/wake patterns in cardiac patients

Heart rate variability (HRV) measurement is an important tool in cardiac care that can provide clinicians and researchers with a 24-hour noninvasive measure of autonomic nervous system activity. Sleep and wake have profoundly different effects on HRV patterns and therefore significant implications for HRV interpretation. This article provides a brief overview of the processes underlying HRV, the standard measures of HRV, a basic overview of wake and sleep, the HRV patterns associated with different sleep and wake states, and the patterns of HRV exhibited in common cardiac conditions. The article concludes with an overview of some general health history factors that are important to consider when interpreting HRV patterns in the clinical and research setting.

Effects of light and sleep stages on heart rate variability in humans

Effects of light intensity and sleep stages on heart rate variability (HRV) were investigated in young healthy subjects. The low-frequency (LF)/high-frequency (HF) ratio was significantly increased by exposing either to bright lights of 10 000 lx or to extreme darkness (< 0.01 lx), while HF and LF components of HRV were not changed, when compared with those under dim light (100 lx). However, LF was significantly increased at REM sleep, when compared with that at the pre-sleep wake. In contrast, HF was increased at all stages of sleep, and the LF/HF ratio was decreased at slow wave sleep during the baseline night.

Hemodynamic effects of MF 10058, a new cardioselective muscarinic M(2) receptor antagonist, in conscious dogs

The 5-¿4-[4-(diethylamino)butyl]-1-piperidinyl¿acetyl-5H-dibenz[b, f]-azepine (MF 10058) is a new potent and selective muscarinic M(2) receptor antagonist. The hemodynamic effects of MF 10058 were investigated in conscious freely moving dogs. Placebo and three doses of MF 10058 (2, 4 and 8 mg/kg) were orally administered according to a randomised four-way crossover design. Heart rate, cardiac conduction times, systolic and diastolic blood pressure were telemetrically recorded for 12-24 h after dosing. After placebo administration, a consistent reduction over time in heart rate was observed during the night-time period (-15%, P=0.019). MF 10058 administration antagonised the nocturnal bradycardia and shortened QT interval. The effect of the drug reached statistically significance, compared to placebo, with the highest dose of 8 mg/kg (+19% on heart rate, P=0.013; -4% on QT interval, P=0.049). The effect on heart rate lasted for the entire 24-h observation period (+16%, P=0.030). Nocturnal systolic and diastolic blood pressure were not significantly affected by MF 10058. No other signs of peripheral or central cholinergic block were observed at any dose. The results of this study demonstrated that oral administration of MF 10058 produces long-lasting hemodynamic effects in the conscious dog. The drug has a therapeutic potential for the treatment of bradycardic disorders.

Time-frequency analysis of heart rate variability using short-time fourier analysis

This study was done to introduce new parameters derived by time frequency analysis of heart rate variability data. Four simulation experiments were carried out to compare the short-time Fourier transform (STFT) analysis method to the traditional overall spectral analysis method. Sinusoidal signals were generated with identical total power in the high-frequency band, but varying time-frequency and time-amplitude information. The STFT method was also applied to heart rate variability data from the stages of normal human sleep. Data analysis included computation of the power in the high-frequency band by overall spectral analysis. The instability coefficients (ICs) of the frequency and power in the high-frequency band were derived by STFT analysis. The ICs derived by the STFT method were able to describe time-frequency and time-amplitude variations in sinusoidal signals which contained identical total power in a specified frequency range. The ICs of the frequency and power were able to differentiate variations in vagal activity between the stages of human sleep and waking. The ICs represent new parameters derived by the STFT method, and allow the detection and quantification of short-lasting time-frequency and time-amplitude variations that remain obscured by overall spectral analysis.

Neuroendocrine processes underlying ultradian sleep regulation in man

Sleep is not a uniform state but is characterized by the cyclic alternation between rapid eye movement (REM) and non-REM sleep with a periodicity of 90-110 min. This cycle length corresponds to one of the oscillations in electroencephalographic (EEG) activity in the delta frequency band (0.5-3.5 Hz), which reflect the depth of sleep. To demonstrate the intimate link between EEG and neuroendocrine rhythmic activities in man, we adopted a procedure permitting simultaneous analysis of sleep EEG activity in the delta band and of two activating systems: the adrenocorticotropic system and the autonomic nervous system. Adrenocorticotropic activity was evaluated by calculating the cortisol secretory rate in blood samples taken at 10-min intervals. Autonomic activity was estimated by two measures of heart rate variability: 1) by the ratio of low-frequency (LF) to high-frequency (HF) power from spectral analysis of R-R intervals; and 2) by the interbeat autocorrelation coefficient of R-R intervals (rRR intervals between two successive cardiac beats). The results revealed that oscillations in delta wave activity, adrenocorticotropic activity, and autonomic activity are linked in a well-defined manner. Delta wave activity developed when cortisol secretory rates had returned to low levels and sympathetic tone was low or decreasing, as reflected by a low LF/HF ratio and by low levels in rRR. Conversely, the decrease in delta wave activity occurred together with an increase in the LF/HF ratio and in rRR. REM sleep was associated with a decrease in cortisol secretory rates preceding REM sleep onset, whereas the LF/HF ratio and rRR remained high. These results demonstrate a close coupling of adrenocorticotropic, autonomic, and EEG ultradian rhythms during sleep in man. They suggest that low neuroendocrine activity is a prerequisite for the increase in slow wave activity.

Cardiac autonomic function during sleep in psychogenic and organic erectile dysfunction

The present study investigated the sympathetic/parasympathetic balance during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep in patients with psychogenic and organic erectile dysfunction. The cardiac autonomic balance was assessed from the power of the low frequency (LF) and high frequency (HF) spectral components of heart-rate variability in 11 patients with psychogenic erectile dysfunction and 11 patients with organic erectile dysfunction as determined by monitoring sleep-related erections. Spectral analysis of heart-rate variability was calculated for at least four successive 4-min epochs of electrocardiogram recordings during NREM sleep and for all available 4-min epochs during REM sleep. Statistical analysis revealed that organic patients had a significantly higher LF/HF ratio (P < 0.01) during both stages of sleep, which resulted from a significantly lower power in the HF component (P < 0.004) and higher power in the LF component (P < 0.01) in these patients, in both REM and NREM sleep stages. These results demonstrate that patients complaining of daytime sexual dysfunction and found by sleep-related erection monitoring to suffer from organic erectile dysfunction, have altered cardiac autonomic balance during both stages of sleep.

Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men

We have recently demonstrated that the overnight profiles of cardiac interbeat autocorrelation coefficient of R-R intervals (rRR) calculated at 1-min intervals are related to the changes in sleep electroencephalographic (EEG) mean frequency, which reflect depth of sleep. Other quantitative measures of the Poincaré plots, i.e., the standard deviation of normal R-R intervals (SDNN) and the root mean square difference among successive R-R normal intervals (RMSSD), are commonly used to evaluate heart rate variability. The present study was designed to compare the nocturnal profiles of rRR, SDNN, and RMSSD with the R-R spectral power components: high-frequency (HF) power, reflecting parasympathetic activity; low-frequency (LF) power, reflecting a predominance of sympathetic activity with a parasympathetic component; and the LF-to-HF ratio (LF/HF), regarded as an index of sympathovagal balance. rRR, SDNN, RMSSD, and the spectral power components were calculated every 5 min during sleep in 15 healthy subjects. The overnight profiles of rRR and LF/HF showed coordinate variations with highly significant correlation coefficients (P < 0.001 in all subjects). SDNN correlated with LF power (P < 0.001), and RMSSD correlated with HF power (P < 0.001). The overnight profiles of rRR and EEG mean frequency were found to be closely related with highly cross-correlated coefficients (P < 0. 001). SDNN and EEG mean frequency were also highly cross correlated (P < 0.001 in all subjects but 1). No systematic relationship was found between RMSSD and EEG mean frequency. In conclusion, rRR appears to be a new tool for evaluating the dynamic beat-to-beat interval behavior and the sympathovagal balance continuously during sleep. This nonlinear method may provide new insight into autonomic disorders.