The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics

The use of heart rate variability, oxygen saturation, and anthropometric data with machine learning to predict the presence and severity of obstructive sleep apnea

Introduction

Obstructive sleep apnea (OSA) is a prevalent sleep disorder with a high rate of undiagnosed patients, primarily due to the complexity of its diagnosis made by polysomnography (PSG). Considering the severe comorbidities associated with OSA, especially in the cardiovascular system, the development of early screening tools for this disease is imperative. Heart rate variability (HRV) is a simple and non-invasive approach used as a probe to evaluate cardiac autonomic modulation, with a variety of newly developed indices lacking studies with OSA patients.

Objectives

We aimed to evaluate numerous HRV indices, derived from linear but mainly nonlinear indices, combined or not with oxygen saturation indices, for detecting the presence and severity of OSA using machine learning models.

Methods

ECG waveforms were collected from 291 PSG recordings to calculate 34 HRV indices. Minimum oxygen saturation value during sleep (SatMin), the percentage of total sleep time the patient spent with oxygen saturation below 90% (T90), and patient anthropometric data were also considered as inputs to the models. The Apnea-Hypopnea Index (AHI) was used to categorize into severity classes of OSA (normal, mild, moderate, severe) to train multiclass or binary (normal-to-mild and moderate-to-severe) classification models, using the Random Forest (RF) algorithm. Since the OSA severity groups were unbalanced, we used the Synthetic Minority Over-sampling Technique (SMOTE) to oversample the minority classes.

Results

Multiclass models achieved a mean area under the ROC curve (AUROC) of 0.92 and 0.86 in classifying normal individuals and severe OSA patients, respectively, when using all attributes. When the groups were dichotomized into normal-to-mild OSA vs. moderate-to-severe OSA, an AUROC of 0.83 was obtained. As revealed by RF, the importance of features indicates that all feature modalities (HRV, SpO2, and anthropometric variables) contribute to the top 10 ranks.

Conclusion

The present study demonstrates the feasibility of using classification models to detect the presence and severity of OSA using these indices. Our findings have the potential to contribute to the development of rapid screening tools aimed at assisting individuals affected by this condition, to expedite diagnosis and initiate timely treatment.

Tracking autonomic nervous system activity using surface ECG: Personalized, multiparametric evaluation

We present a concise review of the background, pitfalls, and potential solutions for the noninvasive evaluation and continuous tracking of cardiac autonomic nervous system activity (ANSA), using surface-ECG-accessible parameters, including heart rate (HR), heart-rate variability (HRV), and cardiac repolarization. These parameters have provided insights into the dynamics of cardiac ANSA in controlled experiments and have proved useful in risk assessment with respect to sudden cardiac death and all-cause mortality in some patient populations, as well as in implantable device programming. Yet attempts to translate these parameters from the laboratory environment to ambulatory settings have been hampered by the presence of multiple uncontrolled factors, including changes in blood pressure, body position, physical activity, and respiration frequency. We show that a single-parameter-based, simplified cardiac ANSA evaluation in an uncontrolled ambulatory setting could be inaccurate, and we discuss several approaches to improve accuracy. Discerning cardiac ANSA effects in uncontrolled ambulatory environments requires tracking multiple physiological processes, preferably using multisensor, multiparametric monitoring and controlling some physiological variables (e.g., respiration frequency); data fusion and machine-learning-based analytics are instrumental for developing more accurate personalized ANSA evaluation.

State- and frequency-dependence in autonomic rebalance mediated by intradermal auricular electroacupuncture stimulation

Background

Vagus nerve stimulation (VNS) improves diseases such as refractory epilepsy and treatment-resistant depression, likely by rebalancing the autonomic nervous system (ANS). Intradermal auricular electro-acupuncture stimulation (iaES) produces similar effects. The aim of this study was to determine the effects of different iaES frequencies on the parasympathetic and sympathetic divisions in different states of ANS imbalance.

Methods

We measured heart rate variability (HRV) and heart rate (HR) of non-modeled (normal) rats with the treatment of various frequencies to determine the optimal iaES frequency. The optimized iaES frequency was then applied to ANS imbalance model rats to elucidate its effects.

Results

30 Hz and 100 Hz iaES clearly affected HRV and HR in normal rats. 30 Hz iaES increased HRV, and decreased HR. 100 Hz iaES decreased HRV, and increased HR. In sympathetic excited state rats, 30 Hz iaES increased HRV. 100 Hz iaES increased HRV, and decreased HR. In parasympathetic excited state rats, 30 Hz and 100 Hz iaES decreased HRV. In sympathetic inhibited state rats, 30 Hz iaES decreased HRV, while 100 Hz iaES decreased HR. In parasympathetic inhibited rats, 30 Hz iaES decreased HR and 100 Hz iaES increased HRV.

Conclusion

30 Hz and 100 Hz iaES contribute to ANS rebalance by increasing vagal and sympathetic activity with different amplifications. The 30 Hz iaES exhibited positive effects in all the imbalanced states. 100 Hz iaES suppressed the sympathetic arm in sympathetic excitation and sympathetic/parasympathetic inhibition and suppressed the vagal arm and promoted the sympathetic arm in parasympathetic excitation and normal states.

An Integrative Literature Review of Heart Rate Variability Measures to Determine Autonomic Nervous System Responsiveness Using Pharmacological Manipulation

BACKGROUND

Heart rate variability (HRV) is defined as the difference in the timing of intervals between successive heartbeats and is used as a surrogate measure to the responsiveness of the autonomic nervous system. A review and synthesis of HRV as an indicator of autonomic nervous system responsiveness to pharmacologic stimulation/blockade of sympathetic and/or parasympathetic nervous system branches have not been completed.

PURPOSE

The aim of this integrative review is to synthesize research examining pharmacological modulation of the autonomic nervous system and the response of time domain, frequency domain, and nonlinear measures of HRV.

CONCLUSIONS

Sympathetic nervous system blockade resulted in a consistent decrease in the standard deviation of normal-normal interval metric across studies. Stimulation of the parasympathetic nervous system was associated with an increase in several time, frequency, and nonlinear HRV indices, whereas blockade of the parasympathetic nervous system led to a decrease in similar indices.

CLINICAL IMPLICATIONS

Recommendations to improve the reproducibility of future HRV research are provided for standardization of recording, analysis, and metric decisions and more thorough reporting of HRV indices in published studies. Alterations in autonomic nervous system input to the cardiovascular system are associated with an increased risk for adverse patient outcomes and increased mortality; therefore, understanding the influence of pharmacologic autonomic nervous system modulation on HRV indices and important considerations for reproducible HRV research design will inform future translational research on cardiovascular risk reduction.

Comprehensive viewpoints on heart rate variability at high altitude

OBJECTIVES

Hypoxia is a physiological state characterized by reduced oxygen levels in organs and tissues. It is a common clinicopathological process and a major cause of health problems in highland areas.  Heart rate variability (HRV) is a measure of the balance in autonomic innervation to the heart. It provides valuable information on the regulation of the cardiovascular system by neurohumoral factors, and changes in HRV reflect the complex interactions between multiple systems. In this review, we provide a comprehensive overview of the relationship between high-altitude hypoxia and HRV. We summarize the different mechanisms of diseases caused by hypoxia and explore the changes in HRV across various systems. Additionally, we discuss relevant pharmaceutical interventions. Overall, this review aims to provide research ideas and assistance for in-depth studies on HRV. By understanding the intricate relationship between high-altitude hypoxia and HRV, we can gain insights into the underlying mechanisms and potential therapeutic approaches to mitigate the effects of hypoxia on cardiovascular and other systems.

METHODS

The relevant literature was collected systematically from scientific database, including PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Baidu Scholar, as well as other literature sources, such as classic books of hypoxia.

RESULTS

There is a close relationship between heart rate variability and high-altitude hypoxia. Heart rate variability is an indicator that evaluates the impact of hypoxia on the cardiovascular system and other related systems. By improving the observation of HRV, we can estimate the progress of cardiovascular diseases and predict the impact on other systems related to cardiovascular health. At the same time, changes in heart rate variability can be used to observe the efficacy of preventive drugs for altitude related diseases.

CONCLUSIONS

HRV can be used to assess autonomic nervous function under various systemic conditions, and can be used to predict and monitor diseases caused by hypoxia at high altitude. Investigating the correlation between high altitude hypoxia and heart rate variability can help make HRV more rapid, accurate, and effective for the diagnosis of plateau-related diseases.

Investigating autonomic nervous system dysfunction among patients with post-COVID condition and prolonged cardiovascular symptoms

Heart Rate Variability (HRV) and arterial pressure (AP) variability and their responses to head-up tilt test (HUTT) were investigated in Post-COVID-19 syndrome (PCS) patients reporting tachycardia and/or postural hypotension. Besides tachycardia, PCS patients also showed attenuation of the following HRV parameters: RMSSD [square root of the mean of the sum of the squares of differences between adjacent normal-to-normal (NN) intervals] from statistical measures; the power of RR (beat-to-beat interval) spectra at HF (high frequency) from the linear method spectral analysis; occurrence of 2UV (two unlike variation) pattern of RR from the nonlinear method symbolic analysis; and the new family of statistics named sample entropy, when compared to control subjects. Basal AP and LF (low frequency) power of systolic AP were similar between PCS patients and control subjects, while 0 V (zero variation) patterns of AP from the nonlinear method symbolic analysis were exacerbated in PCS patients. Despite tachycardia and a decrease in RMSSD, no parameter of HRV changed during HUTT in PCS patients compared to control subjects. PCS patients reassessed after 6 months showed higher HF power of RR spectra and a higher percentage of 2UV pattern of RR. Moreover, the reassessed PCS patients showed a lower occurrence of 0 V patterns of AP, while the HUTT elicited HR (heart rate) and AP responses identical to control subjects. The HRV and AP variability suggest an autonomic dysfunction with sympathetic predominance in PCS patients. In contrast, the lack of responses of HRV and AP variability indices during HUTT indicates a marked impairment of autonomic control. Of note, the reassessment of PCS patients showed that the noxious effect of COVID-19 on autonomic control tended to fade over time.

Heart rate variability in male rats

The cardiovascular system is primarily controlled by the autonomic nervous system, and any changes in sympathetic or parasympathetic activity also have an impact on myocardial activity. Heart rate variability (HRV) is a readily available metric used to assess heart rate control by the autonomic nervous system. HRV can provide information about neural (parasympathetic, sympathetic, reflex) and humoral (hormones, thermoregulation) control of myocardial activity. Because there are no relevant reference values for HRV parameters in rats in the scientific literature, all experimental results are only interpreted on the basis of changes from currently measured control or baseline HRV values, which are, however, significantly different in individual studies. Considering the significant variability of published HRV data, the present study focused primarily on comparing control or baseline HRV values under different conditions in in vivo experiments involving rats. The aim of the study was therefore to assess whether there are differences in the starting values before the experiment itself.

Economic Choice and Heart Rate Fractal Scaling Indicate That Cognitive Effort Is Reduced by Depression and Boosted by Sad Mood

BACKGROUND

People with depression typically exhibit diminished cognitive control. Control is subjectively costly, prompting speculation that control deficits reflect reduced cognitive effort. Evidence that people with depression exert less cognitive effort is mixed, however, and motivation may depend on state affect.

METHODS

We used a cognitive effort discounting task to measure propensity to expend cognitive effort and fractal structure in the temporal dynamics of interbeat intervals to assess on-task effort exertion for 49 healthy control subjects, 36 people with current depression, and 67 people with remitted depression.

RESULTS

People with depression discounted more steeply, indicating that they were less willing to exert cognitive effort than people with remitted depression and never-depressed control subjects. Also, steeper discounting predicted worse functioning in daily life. Surprisingly, a sad mood induction selectively boosted motivation among participants with depression, erasing differences between them and control subjects. During task performance, depressed participants with the lowest cognitive motivation showed blunted autonomic reactivity as a function of load.

CONCLUSIONS

Discounting patterns supported the hypothesis that people with current depression would be less willing to exert cognitive effort, and steeper discounting predicted lower global functioning in daily life. Heart rate fractal scaling proved to be a highly sensitive index of cognitive load, and data implied that people with lower motivation for cognitive effort had a diminished physiological capacity to respond to rising cognitive demands. State affect appeared to influence motivation among people with current depression given that they were more willing to exert cognitive effort following a sad mood induction.

Comparison of Heart Autonomic Control between Hemodynamically Stable and Unstable Patients during Hemodialysis Sessions: A Bayesian Approach

Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive RR intervals between hemodynamically stable (HS) and unstable (HU) patients during a hemodialysis session, through linear and nonlinear methods. Forty-six chronic kidney disease patients volunteered in this study. Successive RR intervals and blood pressures were recorded throughout the hemodialysis session. Hemodynamic stability was defined based on the delta of systolic blood pressure (higher SBP-lower SBP). The cutoff for hemodynamic stability was defined as 30 mm Hg, and patients were stratified as: HS ([n = 21]: ≤29.9 mm Hg) or HU ([n = 25]: ≥30 mm Hg). Linear methods (low-frequency [LFnu] and high-frequency [HFnu] spectra) and nonlinear methods (multiscale entropy [MSE] for Scales 1-20, and fuzzy entropy) were applied. The area under the MSE curve at Scales 1-5 (MSE_1-5), 6-20 (MSE_6-20), and 1-20 (MSE_1-20) were also used as nonlinear parameters. Frequentist and Bayesian inferences were applied to compare HS and HU patients. The HS patients exhibited a significantly higher LFnu and lower HFnu. For MSE parameters, Scales 3-20 were significantly higher, as well as MSE_1-5, MSE_6-20, and MSE_1-20 in HS, when compared to HU patients (p < 0.05). Regarding Bayesian inference, the spectral parameters demonstrated an anecdotal (65.9%) posterior probability favoring the alternative hypothesis, while MSE exhibited moderate to very strong probability (79.4 to 96.3%) at Scales 3-20, and MSE_1-5, MSE_6-20, and MSE_1-20. HS patients exhibited a higher heart-rate complexity than HU patients. In addition, the MSE demonstrated a greater potential than spectral methods to differentiate variability patterns in successive RR intervals.

Muscle metaboreflex activation during hypercapnia modifies nonlinear heart rhythm dynamics, increasing the complexity of the sinus node autonomic regulation in humans

Muscle metaboreflex activation during hypercapnia leads to enhanced pressive effects that are poorly understood while autonomic responses including baroreflex function are not documented. Thus, we assessed heart rate variability (HRV) that is partly due to autonomic influences on sinus node with linear tools (spectral analysis of instantaneous heart period), baroreflex set point and sensitivity with the heart period-arterial pressure transfer function and sequences methods, and system coupling through the complexity of RR interval dynamics with nonlinear tools (Poincaré plots and approximate entropy (ApEn)). We studied ten healthy young men at rest and then during muscle metaboreflex activation (MMA, postexercise muscle ischemia) and hypercapnia (HCA, PetCO2 =  + 10 mmHg from baseline) separately and combined (MMA + HCA). The strongest pressive responses were observed during MMA + HCA, while baroreflex sensitivity was similarly lowered in the three experimental conditions. HRV was significantly different in MMA + HCA compared to MMA and HCA separately, with the lowest total power spectrum (p < 0.05), including very low frequency (p < 0.05), low frequency (p < 0.05), and high frequency (tendency) power spectra decreases, and the lowest Poincaré plot short-term variability index (SD1): SD1 = 36.2 ms (MMA + HCA) vs. SD1 = 43.1 ms (MMA, p < 0.05) and SD1 = 46.1 ms (HCA, p < 0.05). Moreover, RR interval dynamic complexity was significantly increased only in the MMA + HCA condition (ApEn increased from 1.04 ± 0.04, 1.07 ± 0.02, and 1.05 ± 0.03 to 1.10 ± 0.03, 1.13 ± 0.04, and 1.17 ± 0.03 in MMA, HCA, and MMA + HCA conditions, respectively; p < 0.01). These results suggest that in healthy young men, muscle metaboreflex activation during hypercapnia leads to interactions that reduce parasympathetic influence on the sinus node activity but complexify its dynamics.

Autonomic nervous system and cardiac neuro-signaling pathway modulation in cardiovascular disorders and Alzheimer's disease

The heart is a functional syncytium controlled by a delicate and sophisticated balance ensured by the tight coordination of its several cell subpopulations. Accordingly, cardiomyocytes together with the surrounding microenvironment participate in the heart tissue homeostasis. In the right atrium, the sinoatrial nodal cells regulate the cardiac impulse propagation through cardiomyocytes, thus ensuring the maintenance of the electric network in the heart tissue. Notably, the central nervous system (CNS) modulates the cardiac rhythm through the two limbs of the autonomic nervous system (ANS): the parasympathetic and sympathetic compartments. The autonomic nervous system exerts non-voluntary effects on different peripheral organs. The main neuromodulator of the Sympathetic Nervous System (SNS) is norepinephrine, while the principal neurotransmitter of the Parasympathetic Nervous System (PNS) is acetylcholine. Through these two main neurohormones, the ANS can gradually regulate cardiac, vascular, visceral, and glandular functions by turning on one of its two branches (adrenergic and/or cholinergic), which exert opposite effects on targeted organs. Besides these neuromodulators, the cardiac nervous system is ruled by specific neuropeptides (neurotrophic factors) that help to preserve innervation homeostasis through the myocardial layers (from epicardium to endocardium). Interestingly, the dysregulation of this neuro-signaling pathway may expose the cardiac tissue to severe disorders of different etiology and nature. Specifically, a maladaptive remodeling of the cardiac nervous system may culminate in a progressive loss of neurotrophins, thus leading to severe myocardial denervation, as observed in different cardiometabolic and neurodegenerative diseases (myocardial infarction, heart failure, Alzheimer's disease). This review analyzes the current knowledge on the pathophysiological processes involved in cardiac nervous system impairment from the perspectives of both cardiac disorders and a widely diffused and devastating neurodegenerative disorder, Alzheimer's disease, proposing a relationship between neurodegeneration, loss of neurotrophic factors, and cardiac nervous system impairment. This overview is conducive to a more comprehensive understanding of the process of cardiac neuro-signaling dysfunction, while bringing to light potential therapeutic scenarios to correct or delay the adverse cardiovascular remodeling, thus improving the cardiac prognosis and quality of life in patients with heart or neurodegenerative disorders.

Longitudinally Tracking Maternal Autonomic Modulation During Normal Pregnancy With Comprehensive Heart Rate Variability Analyses

Changes in the maternal autonomic nervous system are essential in facilitating the physiological changes that pregnancy necessitates. Insufficient autonomic adaptation is linked to complications such as hypertensive diseases of pregnancy. Consequently, tracking autonomic modulation during progressing pregnancy could allow for the early detection of emerging deteriorations in maternal health. Autonomic modulation can be longitudinally and unobtrusively monitored by assessing heart rate variability (HRV). Yet, changes in maternal HRV (mHRV) throughout pregnancy remain poorly understood. In previous studies, mHRV is typically assessed only once per trimester with standard HRV features. However, since gestational changes are complex and dynamic, assessing mHRV comprehensively and more frequently may better showcase the changing autonomic modulation over pregnancy. Subsequently, we longitudinally (median sessions = 8) assess mHRV in 29 healthy pregnancies with features that assess sympathetic and parasympathetic activity, as well as heart rate (HR) complexity, HR responsiveness and HR fragmentation. We find that vagal activity, HR complexity, HR responsiveness, and HR fragmentation significantly decrease. Their associated effect sizes are small, suggesting that the increasing demands of advancing gestation are well tolerated. Furthermore, we find a notable change in autonomic activity during the transition from the second to third trimester, highlighting the dynamic nature of changes in pregnancy. Lastly, while we saw the expected rise in mean HR with gestational age, we also observed increased autonomic deceleration activity, seemingly to counter this rising mean HR. These results are an important step towards gaining insights into gestational physiology as well as tracking maternal health via mHRV.

Heart rate variability and the dawn of complex physiological signal analysis: methodological and clinical perspectives

Spontaneous beat-to-beat variations of heart rate (HR) have intrigued scientists and casual observers for centuries; however, it was not until the 1970s that investigators began to apply engineering tools to the analysis of these variations, fostering the field we now know as heart rate variability or HRV. Since then, the field has exploded to not only include a wide variety of traditional linear time and frequency domain applications for the HR signal, but also more complex linear models that include additional physiological parameters such as respiration, arterial blood pressure, central venous pressure and autonomic nerve signals. Most recently, the field has branched out to address the nonlinear components of many physiological processes, the complexity of the systems being studied and the important issue of specificity for when these tools are applied to individuals. When the impact of all these developments are combined, it seems likely that the field of HRV will soon begin to realize its potential as an important component of the toolbox used for diagnosis and therapy of patients in the clinic. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Short-term effect of ligature-induced periodontitis on cardiovascular variability and inflammatory response in spontaneously hypertensive rats

BACKGROUND

We previously reported that periodontal disease (PD) induces high arterial pressure variability (APV) consistent with sympathetic overactivity and elicits myocardial inflammation in Balb/c mice. However, it is unknown whether PD can change APV and heart rate variability (HRV) in spontaneously hypertensive (SHR) and normotensive Wistar-Kyoto (WKY) rats. This study aimed to evaluate the hemodynamic level, HRV, and APV associating with myocardial inflammation and plasma concentrations of oxide nitric (NO) in SHR and WKY rats with PD.

METHODS

Three weeks after bilateral ligation of the first mandibular molar, or Sham operation, the rats received catheters into the femoral artery and had their arterial pressure (AP) recorded the following day. Subsequently, plasma, heart, and jaw were collected. The NO was quantified by the chemiluminescence method in plasma, and the myocardial IL-1β concentrations were evaluated by ELISA. In the jaw was evaluated linear alveolar bone loss induced by PD.

RESULTS

The linear alveolar bone loss in jaws of SHR with PD was higher than in all other groups. AP and heart rate were higher in SHR than in their WKY counterparts. SHR with PD showed lower AP than control SHR. HRV and APV were different between SHR and WKY rats; however, no differences in these parameters were found between the animals with PD and their control counterparts. Plasma NO and myocardial IL-1β concentrations were higher in SHR with PD as compared to control WKY. A significant correlation was found between linear alveolar bone loss and plasma NO and myocardial IL-1β concentrations.

CONCLUSION

Our results demonstrated that short-term PD lowered the AP in SHR, which might be due to the higher levels of plasma NO. Even though PD did not affect either HRV or APV, it did induce myocardial inflammation, which can determine cardiovascular dysfunction in long-term PD.

Heart rate fragmentation, a novel approach in heart rate variability analysis, is altered in rats 4 and 12 weeks after myocardial infarction

An approach recently proposed to assess ultra-rapid patterns of heart rate variability, namely heart rate fragmentation (HRF), is increased in aging and coronary disease. We aimed to evaluate and to correlate HRF with cardiac functional parameters in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Subsequently, cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero, one, two, or three inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced in MI rats. Ejection fraction was smaller 4 (28 ± 3 vs 68 ± 2%, p < 0.0001) and 12 weeks after MI (38 ± 3 vs 70 ± 3%, p < 0.0001). Fractional shortening was also smaller 4 (13 ± 2 vs 41 ± 2%, p < 0.0001) and 12 weeks after MI (20 ± 2 vs 41 ± 3%, p < 0.0001). PIP was increased in MI rats 4 (74 ± 2 vs 69 ± 1%, p = 0.03) and 12 weeks after surgery (70 ± 2 vs 63 ± 1%, p = 0.02). We found a significant negative correlation between cardiac functional parameters and HRF at both 4 and 12 weeks after MI. These findings reveal that MI increases HRF, reinforcing the importance of this approach to explore pathophysiological conditions. Evaluation of heart rate fragmentation (HRF) in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero (W0), one (W1), two (W3), or three (W3) inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced, while PIP was increased in all MI rats. Fluent patterns (W0 and W1) decreased in MI rats after 12 weeks. Altogether, the findings reveal that MI increases HRF, reinforcing the potential of this approach to explore pathophysiological conditions.

Machine learning model on heart rate variability metrics identifies asymptomatic toddlers exposed to zika virus during pregnancy

Objective. Although the Zika virus (ZIKV) seems to be prominently neurotropic, there are some reports of involvement of other organs, particularly the heart. Of special concern are those children exposed prenatally to ZIKV and born without microcephaly or other congenital anomalies. Electrocardiogram (ECG)-derived heart rate variability (HRV) metrics represent an attractive, low-cost, widely deployable tool for early identification of developmental functional alterations in exposed children born without such overt clinical symptoms. We hypothesized that HRV in such children would yield a biomarker of fetal ZIKV exposure. Our objective was to test this hypothesis in young children exposed to ZIKV during pregnancy.Approach. We investigated the HRV properties of 21 children aged 4-25 months from Brazil. The infants were divided into two groups, the ZIKV-exposed (n = 13) and controls (n = 8). Single-channel ECG was recorded in each child at ∼15 months of age and HRV was analyzed in 5 min segments to provide a comprehensive characterization of the degree of variability and complexity of the heart rate.Main results.Using a cubic support vector machine classifier we identified babies as Zika cases or controls with a negative predictive value of 92% and a positive predictive value of 86%. Our results show that a machine learning model derived from HRV metrics can help differentiate between ZIKV-affected, yet asymptomatic, and non-ZIKV-exposed babies. We identified the box count as the best HRV metric in this study allowing such differentiation, regardless of the presence of microcephaly.Significance.We show that it is feasible to measure HRV in infants and toddlers using a small non-invasive portable ECG device and that such an approach may uncover the memory ofin uteroexposure to ZIKV. We discuss putative mechanisms. This approach may be useful for future studies and low-cost screening tools involving this challenging to examine population.

Sulprostone-Induced Gastric Dysrhythmia in the Ferret: Conventional and Advanced Analytical Approaches

Nausea and emesis resulting from disease or drug treatment may be associated with disrupted gastric myoelectric activity (GMA). Conventional analytical techniques can determine the relative degrees of brady-, normo-, and tachygastric power, but lose information relative to the basic slow wave shape. The aim of the present study was to investigate the application of advanced analytical techniques in the analysis of disrupted GMA recorded after administration of sulprostone, a prostaglandin E_3/1 agonist, in ferrets. Ferrets were implanted with radiotelemetry devices to record GMA, blood pressure, heart rate (HR) and core body temperature 1 week before the administration of sulprostone (30 μg/kg) or vehicle (saline, 0.5 mL/kg). GMA was initially analyzed using fast Fourier transformations (FFTs) and a conventional power partitioning. Detrended fluctuation analysis (DFA) was also applied to the GMA recordings to reveal information relative to the fluctuation of signals around local trends. Sample entropy (SampEn) analysis was used for examining the regularity of signals. Conventional signal processing techniques revealed that sulprostone increased the dominant frequency (DF) of slow waves, with an increase in the percentage power of the tachygastric range and a decrease in the percentage power of the normogastric range. DFA revealed that sulprostone decreased the fluctuation function, indicative of a loss of the variability of GMA fluctuations around local trends. Sulprostone increased SampEn values, indicating a loss of regularity in the GMA data. Behaviorally, sulprostone induced emesis and caused defecation. It also increased blood pressure and elevated HR, with an associated decrease in HR variability (HRV). Further analysis of HRV revealed a decrease in both low-frequency (LF) and high-frequency (HF) components, with an overall increase in the LF/HF ratio. Sulprostone did not affect core body temperature. In conclusion, DFA and SampEn permit a detailed analysis of GMA, which is necessary to understand the action of sulprostone to modulate gastric function. The action to decrease HRV and increase the LF/HF ratio may be consistent with a shift toward sympathetic nervous system dominance, commonly seen during nausea.

Heart Rate Variability and Erectile Function in Younger Men: A Pilot Study

Erectile dysfunction (ED) in younger men is an increasing concern. In middle aged and older men, ED was related to lower resting heart rate variability (HRV), but research in younger men is lacking. The present study examined, in a nonclinical sample of 105 men between 18 and 39 years, the association of ED with several parameters of resting HRV. Scores of the 5-item version of the International Index of Erectile Function (IIEF-5) below 22 were considered as indicating ED. Eighteen men (17.1%) reported ED (mild in 16, mild to moderate in 2). Welch's tests revealed that ED was associated with lower low-frequency power (LF), lower high-frequency power (HF), lower standard deviation of interbeat intervals, and lower standard deviation of the heart rate, which is influenced by both sympathetic and parasympathetic activity. After removing outliers, ED was unrelated to HF. In younger men, erections might be facilitated by a combination of higher parasympathetic tone and relatively higher sympathetic tone in the heart, as indicated by LF and greater standard deviation of the heart rate, a largely overlooked parameter in HRV research.

PyBioS: A freeware computer software for analysis of cardiovascular signals

BACKGROUND AND OBJECTIVE

Several software applications have been proposed in the past years as computational tools for assessing biomedical signals. Many of them are focused on heart rate variability series only, with their strengths and limitations depending on the necessity of the user and the scope of the application. Here, we introduce new software, named PyBioS, intended for the analysis of cardiovascular signals, even though any type of biomedical signal can be used. PyBioS has some functionalities that differentiate it from the other software.

METHODS

PyBioS was developed in Python language with an intuitive, user-friendly graphical user interface. The basic steps for using PyBioS comprise the opening or creation (simulation) of signals, their visualization, preprocessing and analysis. Currently, PyBioS has 8 preprocessing tools and 15 analysis methods, the later providing more than 50 metrics for analysis of the signals' dynamics.

RESULTS

The possibility to create simulated signals and save the preprocessed signals is a strength of PyBioS. Besides, the software allows batch processing of files, making the analysis of a large amount of data easy and fast. Finally, PyBioS has plenty of analysis methods implemented, with the focus on nonlinear and complexity analysis of signals and time series.

CONCLUSIONS

Although PyBioS is not intended to overcome all the necessities from users, it has useful functionalities that may be helpful in many situations. Moreover, PyBioS is continuously under improvement and several simulated signals, tools and analysis methods are still to be implemented. Also, a new module is being implemented on it to provide machine learning algorithms for classification and regression of data extracted from the biomedical signals.

The effects of non-invasive mechanical ventilation on cardiac autonomic dysfunction in spinal muscular atrophy

In patients with spinal muscular atrophy (SMA), obstructive sleep apnea syndrome (OSAS) constitutes an important cause of cardiovascular morbidity and mortality. We investigated heart rate variability (HRV) to evaluate the effects of non-invasive mechanical ventilation on cardiac autonomic dysfunction in patients with SMA and OSAS. Six patients with SMA (type 1 and 2) and six age- and sex-matched healthy children were consecutively enrolled. A whole-night diagnostic polysomnography was performed, and SMA patients with OSAS were given non-invasive mechanical ventilation therapy. HRV analysis was performed on the basis of whole-night electrocardiography recordings via a computer-base program. Apnea-hypopnea index (AHI) was 9.2 ± 6.2/hr in SMA patients, while it was 0.4 ± 0.5/hr in controls (p = 0.036). All SMA patients had OSAS, while none of the controls had OSAS (p = 0.012). Mean percentage of successive R wave of QRS complex (R-R) intervals>50 ms was significantly lower in SMA patients than those in controls (p = 0.031). Significant correlations were found between AHI and high-frequency power, low/high-frequency ratio in wakefulness and in sleep (p<0.05). Repeated HRV analysis in SMA patients following OSAS therapy showed significant reductions in average R-R duration (p = 0.028) and percentage of successive R-R intervals>50 ms (p = 0.043). Our study demonstrates the beneficial effects of non-invasive mechanical ventilation on cardiac autonomic dysfunction in SMA patients with OSAS.

Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a “global” system parameter and proxy for organismic demands.

Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction

BACKGROUND

Ambient air pollutants can increase cardiovascular mortality. One possible mechanism is the effect on the autonomic balance of the cardiovascular system. Studies on acute effects of particulate matter (PM) exposure on heart rate variability (HRV), a surrogate marker for autonomic balance, in patients with prior myocardial infarction (MI) revealed inconsistent results.

METHOD

We prospectively enrolled participants with acute MI. These participants received a 24-hour Holter electrocardiography examination and echocardiography six months after the index MI. Linear [standard deviation of all normal to normal intervals, standard deviation of NN intervals (SDNN), and a low-frequency to high-frequency ratio (LF/HF)] and non-linear parameters of heart rate variability [multiscale entropy (MSE)] were calculated to show autonomic balance. Data for PM2.5, PM2.5-10, and PM10, were obtained from a fixed-site station in Taiwan. Linear mixed effect models were used to estimate acute effects (within 0-3 days) of PM exposure (per 10 μg/m3) on heart rate variability.

RESULTS

A total of 90 participants were enrolled in this study with a mean age of 58.7 (13.3) and 83 (92.2%) male participants. Traditional HRV parameters, SDNN and LF/HF, were positively correlated with two-day lagged PM2.5-10 and PM10 [adjusted beta coefficient: SDNN: 130.3 and 58.5; LH/HF: 0.32 and 0.21 (all p < or = 0.01)]. MSE slopes 1-5 were negatively correlated with same-day PM2.5-10 and PM10 (adjusted beta coefficient -0.011 (p = 0.01) and -0.005 (p = 0.02), respectively). The left ventricular ejection fraction was negatively correlated with one-day lagged PM2.5-10, and PM10 (adjusted beta coefficient -0.49 and -0.4, respectively; both p < 0.05), after adjusting for MI size.

CONCLUSION

Our results suggest that coarse PM may acutely affect cardiac autonomic balance. MSE is a sensitive marker for detecting changes in autonomic imbalance in patients with prior MI following acute PM exposure.

Modified multiscale fuzzy entropy: A robust method for short-term physiologic signals

The introduction of the multiscale entropy (MSE) method was a milestone in the field of complex physiological signal analysis. However, since MSE is inapplicable for short signals, several variants of MSE have been proposed. One of the most important variants of MSE is the modified multiscale entropy (MMSE), even though it can still produce biased estimates due to the hard similarity criteria of sample entropy. Taking the advantages of MMSE and the concept of fuzzy entropy, we propose the modified multiscale fuzzy entropy (MMFE). We evaluated the robustness of MMSE and MMFE using segmented stochastic noises and actual heart rate variability series and compared it with the classical MSE results obtained with the full signals. Results show that MMFE is much more robust than MMSE for short physiological time series, resembling MSE for series as shorter as 400 samples. We also show the existence of an exponential relationship between the MMFE fuzzy parameter and the signal size. We suggest the use of this relationship to choose the optimal MMFE parameter as part of the method.

Autonomic regulation of the heart and arrhythmogenesis in trained breath-hold divers

AbstractBreath-hold divers are known to develop cardiac autonomic changes and brady-arrthymias during prolonged breath-holding (BH). The effects of BH-induced hypoxemia were investigated upon both cardiac autonomic status and arrhythmogenesis by comparing breath-hold divers (BHDs) to non-divers (NDs). Eighteen participants (9 BHDs, 9 NDs) performed a maximal voluntary BH with face immersion. BHDs were asked to perform an additional BH at water surface to increase the degree of hypoxemia. Beat-to-beat changes in heart rate (HR), short-term fractal scaling exponent (DFAα1), the number of arrhythmic events [premature ventricular contractions (PVCs), premature atrial contractions (PACs)] and peripheral oxygen saturation (SpO₂) were recorded during and immediately following BH. The corrected QT-intervals (QTc) were analyzed pre- and post-acute BH. A regression-based model was used to split BH into a normoxic (NX) and a hypoxemic phase (HX). During the HX phase of BH, BHDs showed a progressive decrease in DFAα1 during BH with face immersion (p < 0.01) and BH with whole-body immersion (p < 0.01) whereas NDs did not (p > 0.05). In addition, BHDs had more arrhythmic events during the HX of BH with whole-body immersion when compared to the corresponding NX phase (5.9 ± 6.7 vs 0.4 ± 1.3; p < 0.05; respectively). The number of PVCs was negatively correlated with SpO₂ during BH with whole-body immersion (r = -0.72; p < 0.05). The hypoxemic stage of voluntary BH is concomitant with significant cardiac autonomic changes toward a synergistic sympathetic and parasympathetic stimulation. Co-activation led ultimately to increased bradycardic response and cardiac electrophysiological disturbances.

Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations

This study aims to uncover brain areas that are functionally linked to complex cardiovascular oscillations in resting-state conditions. Multi-session functional magnetic resonance imaging (fMRI) and cardiovascular data were gathered from 34 healthy volunteers recruited within the human connectome project (the '100-unrelated subjects' release). Group-wise multi-level fMRI analyses in conjunction with complex instantaneous heartbeat correlates (entropy and Lyapunov exponent) revealed the existence of a specialized brain network, i.e. a complex central autonomic network (CCAN), reflecting what we refer to as complex autonomic control of the heart. Our results reveal CCAN areas comprised the paracingulate and cingulate gyri, temporal gyrus, frontal orbital cortex, planum temporale, temporal fusiform, superior and middle frontal gyri, lateral occipital cortex, angular gyrus, precuneous cortex, frontal pole, intracalcarine and supracalcarine cortices, parahippocampal gyrus and left hippocampus. The CCAN visible at rest does not include the insular cortex, thalamus, putamen, amygdala and right caudate, which are classical CAN regions peculiar to sympatho-vagal control. Our results also suggest that the CCAN is mainly involved in complex vagal control mechanisms, with possible links with emotional processing networks.

Slow 0.1 Hz Breathing and Body Posture Induced Perturbations of RRI and Respiratory Signal Complexity and Cardiorespiratory Coupling

Objective: We explored the physiological background of the non-linear operating mode of cardiorespiratory oscillators as the fundamental question of cardiorespiratory homeodynamics and as a prerequisite for the understanding of neurocardiovascular diseases. We investigated 20 healthy human subjects for changes using electrocardiac RR interval (RRI) and respiratory signal (Resp) Detrended Fluctuation Analysis (DFA, α1RRI, α2RRI, α1Resp, α2Resp), Multiple Scaling Entropy (MSERRI1-4, MSERRI5-10, MSEResp1-4, MSEResp5-10), spectral coherence (CohRRI-Resp), cross DFA (ρ1 and ρ2) and cross MSE (XMSE1-4 and XMSE5-10) indices in four physiological conditions: supine with spontaneous breathing, standing with spontaneous breathing, supine with 0.1 Hz breathing and standing with 0.1 Hz breathing. Main results: Standing is primarily characterized by the change of RRI parameters, insensitivity to change with respiratory parameters, decrease of CohRRI-Resp and insensitivity to change of in ρ1, ρ2, XMSE1-4, and XMSE5-10. Slow breathing in supine position was characterized by the change of the linear and non-linear parameters of both signals, reflecting the dominant vagal RRI modulation and the impact of slow 0.1 Hz breathing on Resp parameters. CohRRI-Resp did not change with respect to supine position, while ρ1 increased. Slow breathing in standing reflected the qualitatively specific state of autonomic regulation with striking impact on both cardiac and respiratory parameters, with specific patterns of cardiorespiratory coupling. Significance: Our results show that cardiac and respiratory short term and long term complexity parameters have different, state dependent patterns. Sympathovagal non-linear interactions are dependent on the pattern of their activation, having different scaling properties when individually activated with respect to the state of their joint activation. All investigated states induced a change of α1 vs. α2 relationship, which can be accurately expressed by the proposed measure-inter-fractal angle θ. Short scale (α1 vs. MSE1-4) and long scale (α2 vs. MSE5-10) complexity measures had reciprocal interrelation in standing with 0.1 Hz breathing, with specific cardiorespiratory coupling pattern (ρ1 vs. XMSE1-4). These results support the hypothesis of hierarchical organization of cardiorespiratory complexity mechanisms and their recruitment in ascendant manner with respect to the increase of behavioral challenge complexity. Specific and comprehensive cardiorespiratory regulation in standing with 0.1 Hz breathing suggests this state as the potentially most beneficial maneuver for cardiorespiratory conditioning.

First evidence that intrinsic fetal heart rate variability exists and is affected by hypoxic pregnancy

KEY POINTS

We introduce a technique to test whether intrinsic fetal heart rate variability (iFHRV) exists and we show the utility of the technique by testing the hypothesis that iFHRV is affected by chronic fetal hypoxia, one of the most common adverse outcomes of human pregnancy complicated by fetal growth restriction. Using an established late gestation ovine model of fetal development under chronic hypoxic conditions, we identify iFHRV in isolated fetal hearts and show that it is markedly affected by hypoxic pregnancy. Therefore, the isolated fetal heart has intrinsic variability and carries a memory of adverse intrauterine conditions experienced during the last third of pregnancy.

ABSTRACT

Fetal heart rate variability (FHRV) emerges from influences of the autonomic nervous system, fetal body and breathing movements, and from baroreflex and circadian processes. We tested whether intrinsic heart rate variability (iHRV), devoid of any external influences, exists in the fetal period and whether it is affected by chronic fetal hypoxia. Chronically catheterized ewes carrying male singleton fetuses were exposed to normoxia (n = 6) or hypoxia (10% inspired O₂ , n = 9) for the last third of gestation (105-138 days of gestation (dG); term ∼145 dG) in isobaric chambers. At 138 dG, isolated hearts were studied using a Langendorff preparation. We calculated basal intrinsic FHRV (iFHRV) indices reflecting iFHRV's variability, predictability, temporal symmetry, fractality and chaotic behaviour, from the systolic peaks within 15 min segments in each heart. Significance was assumed at P < 0.05. Hearts of fetuses isolated from hypoxic pregnancy showed approximately 4-fold increases in the Grid transformation as well as the AND similarity index (sgridAND) and a 4-fold reduction in the scale-dependent Lyapunov exponent slope. We also detected a 2-fold reduction in the Recurrence quantification analysis, percentage of laminarity (pL) and recurrences, maximum and average diagonal line (dlmax, dlmean) and the Multiscale time irreversibility asymmetry index. The iHRV measures dlmax, dlmean, pL and sgridAND correlated with left ventricular end-diastolic pressure across both groups (average R²  = 0.38 ± 0.03). This is the first evidence that iHRV originates in fetal life and that chronic fetal hypoxia significantly alters it. Isolated fetal hearts from hypoxic pregnancy exhibit a time scale-dependent higher complexity in iFHRV.

The critical role of respiratory sinus arrhythmia on temporal cardiac dynamics

Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear. Whether or not these parameters can finely classify the severity for CHF patients is uncertain as well. In this work, an electrocardiogram was monitored in advanced CHF patients using 24-h Holter in four conditions, including baseline, one and three months after atenolol therapy, and healthy individuals. Slope and area under the curve (AUC) of multiscale entropy (MSE) curve over short (scales 1-5) and long (scales 6-20) scales, and detrended fluctuation analysis (DFA) scaling exponents at short (4-11 beats) and intermediate (>11 beats) window sizes were calculated. The results show that short-time scale MSE-derived parameters (slope: -0.08 ± 0.10, -0.03 ± 0.10, 0.02 ± 0.06, 0.08 ± 0.06; AUC: 4.03 ± 2.11, 4.69 ± 1.28, 4.73 ± 0.94, and 6.17 ± 1.23) and short-time scale DFA exponent (0.79 ± 0.16, 0.95 ± 0.22, 1.11 ± 0.19, and 1.35 ± 0.20) can hierarchically classify all four conditions. More importantly, simulated R-R intervals with different fractions and amplitude of respiratory sinus arrhythmia (RSA) components were examined to validate our hypothesis regarding the essentiality of RSA in the improvement of cardiovascular function, and its tight association with unpredictability and fractal property of HRV, which is in line with our hypothesis that RSA contributes significantly to the generation of the unpredictability and fractal behavior of HR dynamics.NEW & NOTEWORTHY Temporal cardiac properties provide useful diagnostic parameters for patients with congestive heart failure (CHF). Our study hierarchically classified CHF patients with β-blocker treatment by using multiscale entropy and detrended fluctuation analysis. Also, we provided the evidence to validate the critical role of respiratory sinus arrhythmia in the fractal properties of heart rate variability.

Multiscale Entropy of Cardiac and Postural Control Reflects a Flexible Adaptation to a Cognitive Task

In humans, physiological systems involved in maintaining stable conditions for health and well-being are complex, encompassing multiple interactions within and between system components. This complexity is mirrored in the temporal structure of the variability of output signals. Entropy has been recognized as a good marker of systems complexity, notably when calculated from heart rate and postural dynamics. A degraded entropy is generally associated with frailty, aging, impairments or diseases. In contrast, high entropy has been associated with the elevated capacity to adjust to an ever-changing environment, but the link is unknown between entropy and the capacity to cope with cognitive tasks in a healthy young to middle-aged population. Here, we addressed classic markers (time and frequency domains) and refined composite multiscale entropy (MSE) markers (after pre-processing) of heart rate and postural sway time series in 34 participants during quiet versus cognitive task conditions. Recordings lasted 10 min for heart rate and 51.2 s for upright standing, providing time series lengths of 500–600 and 2048 samples, respectively. The main finding was that entropy increased during cognitive tasks. This highlights the possible links between our entropy measures and the systems complexity that probably facilitates a control remodeling and a flexible adaptability in our healthy participants. We conclude that entropy is a reliable marker of neurophysiological complexity and adaptability in autonomic and somatic systems.

Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study

Identifying prognostic factors by affordable tools is crucial for guiding gastric cancer (GC) treatments especially at earlier stages for timing interventions. The autonomic function that is clinically assessed by heart rate variability (HRV) is involved in tumorigenesis. This pilot study was aimed to examine whether nonlinear indices of HRV can be biomarkers of GC severity. Sixty-one newly-diagnosed GC patients were enrolled. Presurgical serum fibrinogen (FIB), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA199) were examined. Resting electrocardiogram (ECG) of 5-min was collected prior to surgical treatments to enable the HRV analysis. Twelve nonlinear HRV indices covering the irregularity, complexity, asymmetry, and temporal correlation of heartbeat fluctuations were obtained. Increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with higher FIB level. Increased irregularity and decreased complexity were also associated with higher CEA level. These associations were independent of age, sex, BMI, alcohol consumption, history of diabetes, left ventricular ejection fraction, and anemia. The results support the hypothesis that perturbations in nonlinear dynamical patterns of HRV predict increased GC severity. Replication in larger samples as well as the examination of longitudinal associations of HRV nonlinear features with cancer prognosis/survival are warranted.

Effects of Acute Normobaric Hypoxia on Non-linear Dynamics of Cardiac Autonomic Activity During Constant Workload Cycling Exercise

Aim

Measurements of Non-linear dynamics of heart rate variability (HRV) provide new possibilities to monitor cardiac autonomic activity during exercise under different environmental conditions. Using detrended fluctuation analysis (DFA) technique to assess correlation properties of heart rate (HR) dynamics, the present study examines the influence of normobaric hypoxic conditions (HC) in comparison to normoxic conditions (NC) during a constant workload exercise.

Materials and Methods

Nine well trained cyclists performed a continuous workload exercise on a cycle ergometer with an intensity corresponding to the individual anaerobic threshold until voluntary exhaustion under both NC and HC (15% O₂). The individual exercise duration was normalized to 10% sections (10-100%). During exercise HR and RR-intervals were continuously-recorded. Besides HRV time-domain measurements (meanRR, SDNN), fractal correlation properties using short-term scaling exponent alpha1 of DFA were calculated. Additionally, blood lactate (La), oxygen saturation of the blood (SpO₂), and rating of perceived exertion (RPE) were recorded in regular time intervals.

Results

We observed significant changes under NC and HC for all parameters from the beginning to the end of the exercise (10% vs. 100%) except for SpO₂ and SDNN during NC: increases for HR, La, and RPE in both conditions; decreases for SpO₂ and SDNN during HC, meanRR and DFA-alpha1 during both conditions. Under HC HR (40-70%), La (10-90%), and RPE (50-90%) were significantly-higher, SpO₂ (10-100%), meanRR (40-70%), and DFA-alpha1 (20-60%) were significantly-lower than under NC.

Conclusion

Under both conditions, prolonged exercise until voluntary exhaustion provokes a lower total variability combined with a reduction in the amplitude and correlation properties of RR fluctuations which may be attributed to increased organismic demands. Additionally, HC provoked higher demands and loss of correlation properties at an earlier stage during the exercise regime, implying an accelerated alteration of cardiac autonomic regulation.

A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.

Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex

Fluctuations in resting-state cerebral hemodynamics show scale-free behavior over two distinct scaling ranges. Changes in such bimodal (multi) fractal pattern give insight to altered cerebrovascular or neural function. Our main goal was to assess the distribution of local scale-free properties characterizing cerebral hemodynamics and to disentangle the influence of aging on these multifractal parameters. To this end, we obtained extended resting-state records (N = 2¹⁴) of oxyhemoglobin (HbO), deoxyhemoglobin (HbR) and total hemoglobin (HbT) concentration time series with continuous-wave near-infrared spectroscopy technology from the brain cortex. 52 healthy volunteers were enrolled in this study: 24 young (30.6 ± 8.2 years), and 28 elderly (60.5 ± 12.0 years) subjects. Using screening tests on power-law, multifractal noise, and shuffled data sets we evaluated the presence of true multifractal hemodynamics reflecting long-range correlation (LRC). Subsequently, scaling-range adaptive bimodal signal summation conversion (SSC) was performed based on standard deviation (σ) of signal windows across a range of temporal scales (s). Building on moments of different order (q) of the measure, σ(s), multifractal SSC yielded generalized Hurst exponent function, H(q), and singularity spectrum, D(h) separately for a fast and slow component (the latter dominating the highest temporal scales). Parameters were calculated reflecting the estimated measure at s = N (focus), degree of LRC [Hurst exponent, H(2) and maximal Hölder exponent, h_max] and measuring strength of multifractality [full-width-half-maximum of D(h) and ΔH₁₅ = H(−15)−H(15)]. Correlation-based signal improvement (CBSI) enhanced our signal in terms of interpreting changes due to neural activity or local/systemic hemodynamic influences. We characterized the HbO-HbR relationship with the aid of fractal scale-wise correlation coefficient, r_σ(s) and SSC-based multifractal covariance analysis. In the majority of subjects, cerebral hemodynamic fluctuations proved bimodal multifractal. In case of slow component of raw HbT, h_max, and Ĥ(2) were lower in the young group explained by a significantly increased r_σ(s) among elderly at high temporal scales. Regarding the fast component of CBSI-pretreated HbT and that of HbO-HbR covariance, h_max, and focus were decreased in the elderly group. These observations suggest an attenuation of neurovascular coupling reflected by a decreased autocorrelation of the neuronal component concomitant with an accompanying increased autocorrelation of the non-neuronal component in the elderly group.

Statistical considerations in reporting cardiovascular research

The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71% (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63% (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.

The impact of continuous positive airway pressure on heart rate variability in obstructive sleep apnea patients during sleep: A meta-analysis

BACKGROUND

Heart rate variability (HRV), modulated by cardiac autonomic function, is impaired in obstructive sleep apnea (OSA). However, the effect of continuous positive airway pressure (CPAP) on HRV is debated.

OBJECTIVES

To investigate the associations between CPAP and HRV in OSA.

METHODS

Based on literature from five databases published through August 2017, we performed a meta-analysis of cohort studies of OSA treated with CPAP. The change of low-frequency band (LF), high-frequency band (HF) and the ratio between LF and HF (LHR) were analyzed.

RESULTS

Eleven studies were included. Decreased LF (SMD = -0.32, 95%CI: -0.62,-0.01; P = 0.043) and HF (SMD = -0.51, 95%CI: -0.95, -0.08, P = 0.020) were shown while measured on CPAP. When measured off CPAP, HF was increased remarkably (SMD: 0.31, 95%CI: 0.02, 0.60, P = 0.034).

CONCLUSIONS

CPAP can improve autonomic activity, which might be one mechanism to reduce the risk of cardiovascular diseases in OSA.

Cardiovascular assessment of supportive doctor-patient communication using multi-scale and multi-lag analysis of heartbeat dynamics

Emphatic doctor-patient communication has been associated with improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we studied linear heartbeat dynamics through the extraction of time-frequency domain measurements, as well as heartbeat nonlinear and complex dynamics through novel approaches to compute multi-scale and multi-lag series analyses: namely, the multi-scale distribution entropy and lagged Poincaré plot symbolic analysis. Heart rate variability series were recorded from 54 healthy female subjects who were blind to the aim of the experiment. Participants were randomly assigned into two groups: 27 subjects watched a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient, whereas the remaining 27 watched the same video including four additional supportive comments by the clinician. Considering differences between the beginning and the end of each communication video, results from non-parametric Wilcoxon tests demonstrated that, at a group level, significant differences occurred in heartbeat linear and nonlinear dynamics, with lower complexity during nonsupportive communication. Furthermore, a support vector machine algorithm, validated using a leave-one-subject-out procedure, was able to discern the supportive experience at a single-subject level with an accuracy of 83.33% when nonlinear features were considered, dropping to 51.85% when using standard HRV features only. In conclusion, heartbeat nonlinear and complex dynamics can be a viable tool for the psycho-physiological evaluation of supportive doctor-patient communication. Graphical Abstract Scheme of the three main stages of the study: signal acquisition during doctor-patient communication, ECG signal processing and pattern recognition results.

Mortality Prediction in Severe Congestive Heart Failure Patients with Multifractal Point-Process Modeling of Heartbeat Dynamics

Multifractal analysis of human heartbeat dynamics has been demonstrated to provide promising markers of Congestive Heart Failure (CHF). Yet, it crucially builds on the interpolation of RR intervals series, which has been generically performed with limited links to CHF pathophysiology. We devise a novel methodology estimating multifractal autonomic dynamics from heartbeat-derived series defined in the continuous time. We hypothesize that markers estimated from our novel framework are also effective for mortality prediction in severe CHF. We merge multifractal analysis within a methodological framework based on inhomogeneous point process models of heartbeat dynamics. Specifically, wavelet coefficients and wavelet leaders are computed over measures extracted from instantaneous statistics of probability density functions characterizing and predicting the time until the next heartbeat event occurs. The proposed approach is tested on data from 94 CHF patients, aiming at predicting survivor and non-survivor individuals as determined after a 4 years follow up. Instantaneous markers of vagal and sympatho-vagal dynamics display power-law scaling for a large range of scales, from s to s. Using standard SVM algorithms, the proposed inhomogeneous point-process representation based multifractal analysis achieved the best CHF mortality prediction accuracy of 79.11 % (sensitivity 90.48%, specificity 67.74%). Our results suggest that heartbeat scaling and multifractal properties in CHF patients are not generated at the sinus-node level, but rather by the intrinsic action of vagal short-term control and of sympatho-vagal fluctuations associated with circadian cardiovascular control, especially within the VLF band. These markers might provide critical information in devising a clinical tool for individualized prediction of survivor and non-survivor CHF patients.

Changes in the Complexity of Heart Rate Variability with Exercise Training Measured by Multiscale Entropy-Based Measurements

Quantifying complexity from heart rate variability (HRV) series is a challenging task, and multiscale entropy (MSE), along with its variants, has been demonstrated to be one of the most robust approaches to achieve this goal. Although physical training is known to be beneficial, there is little information about the long-term complexity changes induced by the physical conditioning. The present study aimed to quantify the changes in physiological complexity elicited by physical training through multiscale entropy-based complexity measurements. Rats were subject to a protocol of medium intensity training ( n = 13 ) or a sedentary protocol ( n = 12 ). One-hour HRV series were obtained from all conscious rats five days after the experimental protocol. We estimated MSE, multiscale dispersion entropy (MDE) and multiscale SDiff q from HRV series. Multiscale SDiff q is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiff q , three attributes (q-attributes) were derived, namely SDiff q m a x , q m a x and q z e r o . MSE, MDE and multiscale q-attributes presented similar profiles, except for SDiff q m a x . q m a x showed significant differences between trained and sedentary groups on Time Scales 6 to 20. Results suggest that physical training increases the system complexity and that multiscale q-attributes provide valuable information about the physiological complexity.

Heartbeat Complexity Modulation in Bipolar Disorder during Daytime and Nighttime

This study reports on the complexity modulation of heartbeat dynamics in patients affected by bipolar disorder. In particular, a multiscale entropy analysis was applied to the R-R interval series, that were derived from electrocardiographic (ECG) signals for a group of nineteen subjects comprised of eight patients and eleven healthy control subjects. They were monitored using a textile-based sensorized t-shirt during the day and overnight for a total of 47 diurnal and 27 nocturnal recordings. Patients showed three different mood states: depression, hypomania and euthymia. Results show a clear loss of complexity during depressive and hypomanic states as compared to euthymic and healthy control states. In addition, we observed that a more significant complexity modulation among healthy and pathological mood states occurs during the night. These findings suggest that bipolar disorder is associated with an enhanced sleep-related dysregulation of the Autonomic Nervous System (ANS) activity, and that heartbeat complex dynamics may serve as a viable marker of pathological conditions in mental health.

Comparison between spectral analysis and symbolic dynamics for heart rate variability analysis in the rat

Spectral analysis of heart rate (HR) has been widely used to assess the autonomic cardiovascular control. A nonlinear approach, known as symbolic analysis, has been reported to be very useful to assess the autonomic control of cardiovascular system in humans, but very few studies reported on the differences between these two approaches on experimental models. Two distinct approaches were used to elicit autonomic changes in conscious Wistar rats: (1) pharmacological blockade of cardiac autonomic receptors with atenolol (ATE, N = 9) or methylatropine (ATR, N = 9) and (2) mild changes in arterial pressure (AP) induced by phenylephrine (PHE, N = 9) or sodium nitroprusside (NPS, N = 9). Series of cardiac interval (CI) and systolic AP (SAP) were assessed using spectral analysis and symbolic dynamics. Results show that, for spectral analysis, the power in high frequency band of CI and the power in low frequency band of SAP are the most reliable indices of vagal and sympathetic modulation, respectively. For symbolic analysis, results point 0V% and 1V% to be related to sympathetic and 2UV% to vagal modulation. Interestingly, the incidence of 1V patterns, hitherto with unknown meaning, was revealed the best index of sympathetic modulation in the rat and should be accounted for in the future studies.

Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains.NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.