Sleep stage dependent patterns of nonlinear heart rate dynamics in postmenopausal women

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients

Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

A review of automated sleep stage scoring based on physiological signals for the new millennia

BACKGROUND AND OBJECTIVE

Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

METHODS

This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

RESULTS

Our review shows that all of these signals contain information for sleep stage scoring.

CONCLUSIONS

The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

Heart rate variability changes in major depressive disorder during sleep: Fractal index correlates with BDI score during REM sleep

We investigated the relationship between autonomic nervous system activity during each sleep stage and the severity of depressive symptoms in patients with major depressive disorder (MDD) and healthy control subjects. Thirty patients with MDD and thirty healthy control subjects matched for sex, age, and body mass index completed standard overnight polysomnography. Depression severity was assessed using the Beck Depression Inventory (BDI). Time- and frequency-domain, and fractal HRV parameters were derived from 5-min electrocardiogram segments during light sleep, deep sleep, rapid eye movement (REM) sleep, and the pre- and post-sleep wake periods. Detrended fluctuation analysis (DFA) alpha-1 values during REM sleep were significantly higher in patients with MDD than in control subjects, and a significant correlation existed between DFA alpha-1 and BDI score in all subjects. DFA alpha-1 was the strongest predictor for the BDI score, along with REM density as a covariate. This study found that compared with controls, patients with MDD show reduced complexity in heart rate during REM sleep, which may represent lower cardiovascular adaptability in these patients, and could lead to cardiac disease. Moreover, DFA alpha-1 values measured during REM sleep may be useful as an indicator for the diagnosis and monitoring of depression.

Heart rate variability in patients with major depressive disorder and healthy controls during non-REM sleep and REM sleep

The objectives of this study are to investigate heart rate variability (HRV) in major depressive disorder patients (MDD) and healthy controls during different sleep stages, and to examine the association of HRV during sleep and depression severity. Polysomnography was recorded from 15 depressive patients with a higher beck depression inventory index (BDI > 25, H-BDI-D), 15 depressive patients with a lower BDI index (BDI ≤ 25, L-BDI-D) and 15 healthy controls. HRV was calculated during the first three rapid eye movements (REM) periods and non-REM stages (i.e. sleep stage 2 and 3) with time domain, power spectral and fractal analysis. As a result, H-BDI-D patients showed the highest short-term fractal alpha-1 exponent during first REM period and healthy controls had the lowest values. Our results suggest an association between the depression severity and the autonomic nerve function, especially during the first REM sleep. The pathophysiological analysis for this property should be conducted in future prospective studies.

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

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

Entropy Information of Cardiorespiratory Dynamics in Neonates during Sleep

Sleep is a central activity in human adults and characterizes most of the newborn infant life. During sleep, autonomic control acts to modulate heart rate variability (HRV) and respiration. Mechanisms underlying cardiorespiratory interactions in different sleep states have been studied but are not yet fully understood. Signal processing approaches have focused on cardiorespiratory analysis to elucidate this co-regulation. This manuscript proposes to analyze heart rate (HR), respiratory variability and their interrelationship in newborn infants to characterize cardiorespiratory interactions in different sleep states (active vs. quiet). We are searching for indices that could detect regulation alteration or malfunction, potentially leading to infant distress. We have analyzed inter-beat (RR) interval series and respiration in a population of 151 newborns, and followed up with 33 at 1 month of age. RR interval series were obtained by recognizing peaks of the QRS complex in the electrocardiogram (ECG), corresponding to the ventricles depolarization. Univariate time domain, frequency domain and entropy measures were applied. In addition, Transfer Entropy was considered as a bivariate approach able to quantify the bidirectional information flow from one signal (respiration) to another (RR series). Results confirm the validity of the proposed approach. Overall, HRV is higher in active sleep, while high frequency (HF) power characterizes more quiet sleep. Entropy analysis provides higher indices for SampEn and Quadratic Sample entropy (QSE) in quiet sleep. Transfer Entropy values were higher in quiet sleep and point to a major influence of respiration on the RR series. At 1 month of age, time domain parameters show an increase in HR and a decrease in variability. No entropy differences were found across ages. The parameters employed in this study help to quantify the potential for infants to adapt their cardiorespiratory responses as they mature. Thus, they could be useful as early markers of risk for infant cardiorespiratory vulnerabilities.

AD, Costa FSM, Bianchi AM. Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.

Cardiac autonomic changes after 40 hours of total sleep deprivation in women

OBJECTIVES

The effect of total sleep deprivation on heart rate variability (HRV) in groups of postmenopausal women on oral hormone therapy (HT) (on-HT, n = 10, 64.2 (1.4) years), postmenopausal women without HT (off-HT, n = 10, 64.6 (1.4) years) and young women (n = 11, 23.1 (0.5) years) was studied using a prospective case-control setup.

METHODS

Polysomnography was performed over an adaptation night, a baseline night, and a recovery night after 40 h of total sleep deprivation. Time and frequency domain and nonlinear HRV from overnight electrocardiogram recordings were compared between groups during baseline and recovery nights. Further, the changes in HRV from baseline to recovery were analysed and compared between groups. Finally, correlations of HRV to percentages of sleep stages and measures of sleep fragmentation were analysed during baseline and recovery.

RESULTS

Young women had higher HRV than older women; the most marked difference was between young and on-HT postmenopausal women. Sleep deprivation induced a decrease in frequency domain HRV in young and in off-HT women, an increase in α2 in off-HT women, and an increase in mean heart rate in on-HT women. The sleep deprivation effect was mainly uncorrelated to changes in sleep parameters.

CONCLUSIONS

Acute total sleep deprivation has a deleterious effect on the autonomic nervous system in young women, but an even more pronounced effect in postmenopausal women. Hormone therapy use in late postmenopause does not give protection against these changes. These harmful effects may partly explain the increased cardiovascular morbidity and overall mortality associated with sleep loss.

Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.

Short-term complexity of cardiac autonomic control during sleep: REM as a potential risk factor for cardiovascular system in aging

INTRODUCTION

Sleep is a complex phenomenon characterized by important modifications throughout life and by changes of autonomic cardiovascular control. Aging is associated with a reduction of the overall heart rate variability (HRV) and a decrease of complexity of autonomic cardiac regulation. The aim of our study was to evaluate the HRV complexity using two entropy-derived measures, Shannon Entropy (SE) and Corrected Conditional Entropy (CCE), during sleep in young and older subjects.

METHODS

A polysomnographic study was performed in 12 healthy young (21.1±0.8 years) and 12 healthy older subjects (64.9±1.9 years). After the sleep scoring, heart period time series were divided into wake (W), Stage 1-2 (S1-2), Stage 3-4 (S3-4) and REM. Two complexity indexes were assessed: SE(3) measuring the complexity of a distribution of 3-beat patterns (SE(3) is higher when all the patterns are identically distributed and it is lower when some patterns are more likely) and CCE(min) measuring the minimum amount of information that cannot be derived from the knowledge of previous values.

RESULTS

Across the different sleep stages, young subjects had similar RR interval, total variance, SE(3) and CCE(min). In the older group, SE(3) and CCE(min) were reduced during REM sleep compared to S1-2, S3-4 and W. Compared to young subjects, during W and sleep the older subjects showed a lower RR interval and reduced total variance as well as a significant reduction of SE(3) and CCE(min). This decrease of entropy measures was more evident during REM sleep.

CONCLUSION

Our study indicates that aging is characterized by a reduction of entropy indices of cardiovascular variability during wake/sleep cycle, more evident during REM sleep. We conclude that during aging REM sleep is associated with a simplification of cardiac control mechanisms that could lead to an impaired ability of the cardiovascular system to react to cardiovascular adverse events.

Sleep-disordered breathing in children is associated with impairment of sleep stage-specific shift of cardiac autonomic modulation

We examined the effects of sleep stages and sleep-disordered breathing (SDB) on autonomic modulation in 700 children. Apnea hypopnea index (AHI) during one 9 h night-time polysomnography was used to define SDB. Sleep stage-specific autonomic modulation was measured by heart rate variability (HRV) analysis of the first available 5 min RR intervals from each sleep stage. The mean [standard deviation (SD)] age was 112 (21) months (49% male and 25% non-Caucasian). The average AHI was 0.79 (SD = 1.03) h(-1), while 73.0%, 25.8% and 1.2% of children had AHI <1 (no SDB), 1-5 (mild SDB) and >or=5 (moderate SDB), respectively. In the no SDB group, the high frequency (HF) and root mean square SD (RMSSD) increased significantly from wake to Stage 2 and slow wave sleep (SWS), and then decreased dramatically when shifting into rapid eye movement (REM) sleep. In the moderate SDB group, the pattern of HRV shift was similar to that of no SDB. However, the decreases in HF and RMSSD from SWS to REM were more pronounced in moderate SDB children [between-group differences in HF (-24% in moderate SDB versus -10% in no SDB) and RMSSD (-27% versus -12%) were significant (P < 0.05)]. The REM stage HF is significantly lower in the moderate SDB group compared to the no SDB group [mean (standard error): 4.49 (0.43) versus 5.80 (0.05) ms(2), respectively, P < 0.05]. Conclusions are that autonomic modulation shifts significantly towards higher parasympathetic modulation from wake to non-rapid eye movement sleep, and reverses to a less parasympathetic modulation during REM sleep. However, the autonomic modulation is impaired among children with moderate SDB in the directions of more reduction in parasympathetic modulation from SWS to REM sleep and significantly weaker parasympathetic modulation in REM sleep, which may lead to higher arrhythmia vulnerability, especially during REM sleep.

Differentiating between light and deep sleep stages using an ambulatory device based on peripheral arterial tonometry

The objective of this study is to develop and assess an automatic algorithm based on the peripheral arterial tone (PAT) signal to differentiate between light and deep sleep stages. The PAT signal is a measure of the pulsatile arterial volume changes at the finger tip reflecting sympathetic tone variations and is recorded by an ambulatory unattended device, the Watch-PAT100, which has been shown to be capable of detecting wake, NREM and REM sleep. An algorithm to differentiate light from deep sleep was developed using a training set of 49 patients and was validated using a separate set of 44 patients. In both patient sets, Watch-PAT100 data were recorded simultaneously with polysomnography during a full night sleep study. The algorithm is based on 14 features extracted from two time series of PAT amplitudes and inter-pulse periods (IPP). Those features were then further processed to yield a prediction function that determines the likelihood of detecting a deep sleep stage epoch during NREM sleep periods. Overall sensitivity, specificity and agreement of the automatic algorithm to identify standard 30 s epochs of light and deep sleep stages were 66%, 89%, 82% and 65%, 87%, 80% for the training and validation sets, respectively. Together with the already existing algorithms for REM and wake detection we propose a close to full stage detection method based solely on the PAT and actigraphy signals. The automatic sleep stages detection algorithm could be very useful for unattended ambulatory sleep monitoring assessing sleep stages when EEG recordings are not available.