Effect of age on complexity and causality of the cardiovascular control: comparison between model-based and model-free approaches

Effects of different postures on the hemodynamics and cardiovascular autonomic control responses to exercise in postural orthostatic tachycardia syndrome

PURPOSE

To assess the effects of two different body positions on the cardiovascular autonomic profile during a single bout of exercise in patients with postural orthostatic tachycardia syndrome (POTS).

METHODS

Thirteen patients with POTS and thirteen healthy controls (C) participated in the study. ECG, respiration, beat-by-beat arterial pressure and O2 consumption (VO2) were continuously recorded while on a cycle ergometer in supine and upright positions, before and during exercise (6 min, 50 Watts). Spectral analysis of RR intervals and systolic arterial pressure (SAP) variability provided indexes of cardiac sympathovagal interaction (LF/HF ratio), cardiac vagal modulation (HFRR, high-frequency component of RR variability, ~ 0.25 Hz), sympathetic vasomotor control (LFSAP, low-frequency component of SAP variability, 0.1 Hz) and baroreflex sensitivity (BRS, αLF).

RESULTS

While supine, patients with POTS showed lower HFRR and αLF, greater heart rate (HR), LF/HF and LFSAP, compared with C, suggesting cardiovascular sympathetic over-activity and reduced BRS. While sitting upright, POTS showed greater HR and reduced HFRR and αLF compared with C. During supine exercise, SAP, HR, LF/HF increased and HFRR and αLF decreased similarly in POTS and C. In POTS, upright sitting exercise was associated with slightly higherV ˙ O 2 , a greater increase in HR whereas LFSAP was lower than in C.

CONCLUSION

Upright exercise was associated with excessive enhancement of HR and a blunted increase of the sympathetic vasomotor control in POTS. Conversely, supine exercise-induced hemodynamic and autonomic changes similar in POTS and C, thus making supine exercise potentially more suitable for physical rehabilitation in POTS.

A model-based spectral directional approach reveals the long-term impact of COVID-19 on cardiorespiratory control and baroreflex

BACKGROUND

Coronavirus disease 19 (COVID-19) patients might develop sequelae after apparent resolution of the infection. Autonomic dysfunction and baroreflex failure have been frequently reported. However, the long-term effect of COVID-19 on cardiorespiratory and cardiovascular neural controls has not been investigated with directional approaches able to open the closed-loop relationship between physiological variables.

METHODS

A model-based causal spectral approach, namely causal squared coherence (CK2), was applied to the beat-to-beat variability series of heart period (HP) and systolic arterial pressure (SAP), and to the respiratory signal (RESP) acquired at rest in supine position and during active standing (STAND) in COVID-19 survivors 9 months after their hospital discharge. Patients were categorized according to their need of ventilatory support during hospitalization as individuals that had no need of continuous positive airway pressure (noCPAP, n = 27), need of continuous positive airway pressure in sub-intensive care unit (CPAP, n = 14) and need of invasive mechanical ventilation in intensive care unit (IMV, n = 8).

RESULTS

The expected decrease of the strength of the HP-RESP dynamic interactions as well as the expected increase of the dependence of HP on SAP along baroreflex during STAND was not observed and this result held regardless of the severity of the disease, namely in noCPAP, CPAP and IMV cohorts. Regardless of the experimental condition, spectral causality markers did not vary across groups either.

CONCLUSIONS

CK2 markers, in association with an orthostatic challenge, were able to characterize the impairment of cardiorespiratory control and baroreflex in COVID-19 patients long after acute infection resolution and could be exploited to monitor the evolution of the COVID-19 patients after hospital discharge.

Predictive Modeling of Heart Rate from Respiratory Signals at Rest in Young Healthy Humans

Biological signals such as respiration (RSP) and heart rate (HR) are oscillatory and physiologically coupled, maintaining homeostasis through regulatory mechanisms. This report models the dynamic relationship between RSP and HR in 45 healthy volunteers at rest. Cross-correlation between RSP and HR was computed, along with regression analysis to predict HR from RSP and its first-order time derivative in continuous signals. A simulation model tested the possibility of replicating the RSP-HR relationship. Cross-correlation results showed a time lag in the sub-second range of these signals (849.21 ms ± SD 344.84). The possible modulation of HR by RSP was mediated by the RSP amplitude and its first-order time derivative (in 45 of 45 cases). A simulation of this process allowed us to replicate the physiological relationship between RSP and HR. These results provide support for understanding the dynamic interactions in cardiorespiratory coupling at rest, showing a short time lag between RSP and HR and a modulation of the HR signal by the first-order time derivative of the RSP. This dynamic would optionally be incorporated into dynamic models of resting cardiopulmonary coupling and suggests a mechanism for optimizing respiration in the alveolar system by promoting synchrony between the gases and hemoglobin in the alveolar pulmonary system.

Characterization of cardiorespiratory coupling via a variability-based multi-method approach: Application to postural orthostatic tachycardia syndrome

There are several mechanisms responsible for the dynamical link between heart period (HP) and respiration (R), usually referred to as cardiorespiratory coupling (CRC). Historically, diverse signal processing techniques have been employed to study CRC from the spontaneous fluctuations of HP and respiration (R). The proposed tools differ in terms of rationale and implementation, capturing diverse aspects of CRC. In this review, we classify the existing methods and stress differences with the aim of proposing a variability-based multi-method approach to CRC evaluation. Ten methodologies for CRC estimation, namely, power spectral decomposition, traditional and causal squared coherence,\;information transfer, cross-conditional entropy, mixed prediction, Shannon entropy of the latency between heartbeat and inspiratory/expiratory onset, conditional entropy of the phase dynamics, synchrogram-based analysis, pulse-respiration quotient, and joint symbolic dynamics, are considered. The ability of these techniques was exemplified over recordings acquired from patients suffering from postural orthostatic tachycardia syndrome (POTS) and healthy controls. Analyses were performed at rest in the supine position (REST) and during head-up tilt (HUT). Although most of the methods indicated that at REST, the CRC was lower in POTS patients and decreased more evidently during HUT in POTS, peculiar differences stressed the complementary value of the approaches. The multiple perspectives provided by the variability-based multi-method approach to CRC evaluation help the characterization of a pathological state and/or the quantification of the effect of a postural challenge. The present work stresses the need for the application of multiple methods to derive a more complete evaluation of the CRC in humans.

Comparison of automatic and physiologically-based feature selection methods for classifying physiological stress using heart rate and pulse rate variability indices

Objective.This study evaluates the effectiveness of four machine learning algorithms in classifying physiological stress using heart rate variability (HRV) and pulse rate variability (PRV) time series, comparing an automatic feature selection based on Akaike's criterion to a physiologically-based feature selection approach.Approach.Linear discriminant analysis, support vector machines,K-nearest neighbors and random forest were applied on ten HRV and PRV indices from time, frequency and information domains, selected with the two feature selection approaches. Data were collected from 127 healthy individuals during different stress conditions (rest, postural and mental stress).Main results.Our results highlight that, while specific stress classification is feasible, distinguishing between postural and mental stress remains challenging. The used classifiers exhibited similar performance, with automatic Akaike Information Criterion-based feature selection proving overall better than the physiology-driven approach. Additionally, PRV-based features performed comparably to HRV-based ones, indicating their potential in outpatient monitoring using wearable devices.Significance.The obtained findings help to determine the most relevant HRV/PRV features for stress classification, potentially useful to highlight different physiological mechanisms involved during both challenges accompanied by a shift in the sympathovagal balance. The proposed approach may have implications for advancing stress assessment methodologies in clinical settings and real-world contexts for well-being evaluation.

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.

The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network control

Homeostatic regulation plays a fundamental role in maintenance of multicellular life. At different scales and in different biological systems, this principle allows a better understanding of biological organization. Consequently, a growing interest in studying cause-effect relations between physiological systems has emerged, such as in the fields of cardiovascular and cardiorespiratory regulations. For this, mathematical approaches such as Granger causality (GC) were applied to the field of cardiovascular physiology in the last 20 years, overcoming the limitations of previous approaches and offering new perspectives in understanding cardiac, vascular and respiratory homeostatic interactions. In clinical practice, continuous recording of clinical data of hospitalized patients or by telemetry has opened new applicability for these approaches with potential early diagnostic and prognostic information. In this review, we describe a theoretical background of approaches based on linear GC in time and frequency domains applied to detect couplings between time series of RR intervals, blood pressure and respiration. Interestingly, these tools help in understanding the contribution of homeostatic negative feedback and the anticipatory feedforward mechanisms in homeostatic cardiovascular and cardiorespiratory controls. We also describe experimental and clinical results based on these mathematical tools, consolidating previous experimental and clinical evidence on the coupling in cardiovascular and cardiorespiratory studies. Finally, we propose perspectives allowing to complete the understanding of these interactions between cardiovascular and cardiorespiratory systems, as well as the interplay between brain and cardiac, and vascular and respiratory systems, offering a high integrative view of cardiovascular and cardiorespiratory homeostatic regulation.

Age as an Effect Modifier of the Effects of Transcutaneous Auricular Vagus Nerve Stimulation (taVNS) on Heart Rate Variability in Healthy Subjects

Background: Evidence suggests that vagus nerve stimulation can modulate heart rate variability (HRV). However, there is a lack of mechanistic studies in healthy subjects assessing the effects of bilateral transcutaneous auricular vagus nerve stimulation (taVNS) on HRV. Our study aims to investigate how taVNS can influence the HRV response, including the influence of demographic variables in this response. Methods: Therefore, we conducted a randomized controlled study with 44 subjects, 22 allocated to active and 22 to sham taVNS. Results: Our results showed a significant difference between groups in the high-frequency (HF) metric. Active taVNS increased the HF metric significantly as compared to sham taVNS. Also, we found that age was a significant effect modifier of the relationship between taVNS and HF-HRV, as a larger increase in HF-HRV was seen in the older subjects. Importantly, there was a decrease in HF-HRV in the sham group. Conclusions: These findings suggest that younger subjects can adapt and maintain a constant level of HF-HRV regardless of the type of stimulation, but in the older subjects, only the active taVNS recipients were able to maintain and increase their HF-HRV. These results are important because they indicate that taVNS can enhance physiological regulation processes in response to external events.

Heartbeat-evoked potentials following voluntary hyperventilation in epilepsy patients: respiratory influences on cardiac interoception

Introduction

Current evidence indicates a modulating role of respiratory processes in cardiac interoception, yet whether altered breathing patterns influence heartbeat-evoked potentials (HEP) remains inconclusive.

Methods

Here, we examined the effects of voluntary hyperventilation (VH) as part of a clinical routine examination on scalp-recorded HEPs in epilepsy patients (N = 80).

Results

Using cluster-based permutation analyses, HEP amplitudes were compared across pre-VH and post-VH conditions within young and elderly subgroups, as well as for the total sample. No differences in the HEP were detected for younger participants or across the full sample, while an increased late HEP during pre-VH compared to post-VH was fond in the senior group, denoting decreased cardiac interoceptive processing after hyperventilation.

Discussion

The present study, thus, provides initial evidence of breathing-related HEP modulations in elderly epilepsy patients, emphasizing the potential of HEP as an interoceptive neural marker that could partially extend to the representation of pulmonary signaling. We speculate that aberrant CO2-chemosensing, coupled with disturbances in autonomic regulation, might constitute the underlying pathophysiological mechanism behind the obtained effect. Available databases involving patient records of routine VH assessment may constitute a valuable asset in disentangling the interplay of cardiac and ventilatory interoceptive information in various patient groups, providing thorough clinical data to parse, as well as increased statistical power and estimates of effects with higher precision through large-scale studies.

Describing the Response of Cerebral Autoregulation to Postural Challenge via State Space Correspondence Methods

Postural stimulus influences the cerebral autoregulation (CA), but it remains to be elucidated whether its impact is transient. Two nonlinear state space correspondence (SSC) methods, based on cross-predictability (CP) and cloud size ratio (CSR), were exploited to describe the CA from spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv) during sustained postural challenge. We continuously recorded MAP and MCBv in 12 healthy subjects (age: 27 ± 8 yrs; 5 males) at rest in supine position and during head-up tilt (HUT) at 60° prolonged for 20 minutes after the onset of the orthostatic challenge. We found that: i) markers of MCBv-MAP association computed by CP and CSR techniques are significantly associated but they feature evident differences especially in the early phase of the HUT; ii) both the methods detect an increase of the degree of association from MAP to MCBv during the late phase of HUT. We conclude that, even though the two SSC methods cannot be considered interchangeable, both techniques indicate that HUT affects the CA, and its modifications are not limited to the early phase of HUT.

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

Respiratory sinus arrhythmia (RSA), vagal tone and biobehavioral integration: Beyond parasympathetic function

Linchpin to the entire area of psychophysiological research and discussion of the vagus is the respiratory and cardiovascular phenomenon known as respiratory sinus arrhythmia (RSA; often synonymous with high-frequency heart-rate variability when it is specifically linked to respiratory frequency), i.e. rhythmic fluctuations in heart rate synchronized to inspiration and expiration. This article aims 1) to clarify concepts, terms and measures commonly employed during the last half century in the scientific literature, which relate vagal function to psychological processes and general aspects of health; and 2) to expand upon an earlier theoretical model, emphasizing the importance of RSA well beyond the current focus upon parasympathetic mechanisms. A close examination of RSA and its relations to the vagus may 1) dispel certain commonly held beliefs about associations between psychological functioning, RSA and the parasympathetic nervous system (for which the vagus nerve plays a major role), and 2) offer fresh perspectives about the likely functions and adaptive significance of RSA, as well as RSA's relationship to vagal control. RSA is neither an invariably reliable index of cardiac vagal tone nor of central vagal outflow to the heart. The model here presented posits that RSA represents an evolutionarily entrenched, cardiovascular and respiratory phenomenon that significantly contributes to meeting continuously changing metabolic, energy and behavioral demands.

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis-the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both.

The degree of engagement of cardiac and sympathetic arms of the baroreflex does not depend on the absolute value and sign of arterial pressure variations

Objective.The percentages of cardiac and sympathetic baroreflex patterns detected via baroreflex sequence (SEQ) technique from spontaneous variability of heart period (HP) and systolic arterial pressure (SAP) and of muscle nerve sympathetic activity (MSNA) burst rate and diastolic arterial pressure (DAP) are utilized to assess the level of the baroreflex engagement. The cardiac baroreflex patterns can be distinguished in those featuring both HP and SAP increases (cSEQ++) and decreases (cSEQ--), while the sympathetic baroreflex patterns in those featuring a MSNA burst rate decrease and a DAP increase (sSEQ+-) and vice versa (sSEQ-+). The present study aims to assess the modifications of the involvement of the cardiac and sympathetic arms of the baroreflex with age and postural stimulus intensity.Approach.We monitored the percentages of cSEQ++ (%cSEQ++) and cSEQ-- (%cSEQ--) in 100 healthy subjects (age: 21-70 years, 54 males, 46 females), divided into five sex-balanced groups consisting of 20 subjects in each decade at rest in supine position and during active standing (STAND). We evaluated %cSEQ++, %cSEQ--, and the percentages of sSEQ+- (%sSEQ+-) and sSEQ-+ (%sSEQ-+) in 12 young healthy subjects (age 23 ± 2 years, 3 females, 9 males) undergoing incremental head-up tilt.Main results.We found that: (i) %cSEQ++ and %cSEQ-- decreased with age and increased with STAND and postural stimulus intensity; (ii) %sSEQ+- and %sSEQ-+ augmented with postural challenge magnitude; (iii) the level of cardiac and sympathetic baroreflex engagement did not depend on either the absolute value of arterial pressure or the direction of its changes.Significance.This study stresses the limited ability of the cardiac and sympathetic arms of the baroreflex in controlling absolute arterial pressure values and the equivalent ability of both positive and negative arterial pressure changes in soliciting them.

Revisiting nonlinearity of heart rate variability in healthy aging

Aging is commonly regarded as a physiological process in which the dynamic complexity of physiological time series and organ systems is gradually lost. This notion is derived from the identification of a decline of nonlinear measures with the advance of aging. However, additional research on cardiovascular control studied through heart rate variability (HRV), i.e., the instantaneous changes in heart rate, shows that despite the constriction of its statistical distribution, the nonlinear organization remains present in advanced age. Here, we used surrogate data testing to investigate the presence of nonlinear information in HRV time series from a publicly available database of 1121 healthy human subjects from 18 to 92 years old. We also studied the influence of basic clinical features, such as sex, body mass index (BMI), and mean heart rate (HR), on such nonlinear information. We found that the percentage of nonlinear time series after 30 years of age diminishes significantly (p < 0.01). Furthermore, larger BMI and HR are associated with the presence of more linear information in HRV, while the female sex is associated with the manifestation of nonlinear information. This work provides a common background for the contextualized interpretation of nonlinear testing and shows that the nonlinear content of HRV time series diminishes through aging.

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients

A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM-24 patients) and dilated (DCM-17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.

A framework based on the information domain to measure coupling changes in electrophysiological signals

Objectives.In this paper, the features of physiological signals of healthy dataset are extracted using the linear and non-linear techniques, and a comparison has been made on healthy young and old subjects to study the aging and gender-related changes in the contribution of Heart Rate (HR), Blood Pressure (BP), and Respiration (RESP).Methods. To quantify the coupling changes in cardiovascular, cardiorespiratory, and vasculorespiratory complexity, an information domain approach based on compensated transfer entropy (cTE) is proposed.Result. The results show that there is a substantial decrease in the flow of information from BP tro the time interval between successive R-peaks (RR) and from RR to BP. There is also a significant decrease in the flow of information from RESP to BP and RESP to RR but there is no significant change in the information flow from BP to RESP and RR to RESP.Conclusion. We have done linear and non-linear analysis on the healthy datasets of young and old subjects. As already existed techniques lacks in studying complex behaviours of electrophysiological signals so to overcome these limitations, we have proposed compensated transfer entropy (cTE). We conducted an investigation to determine the degree to which recordings of RESP, BP, and HR can be utilized to predict changes in the other parameters. Specifically, the proposed analysis examined the relationship between these variables and assessed their consistency across different age groups and genders. By analyzing the data, we aimed to gain insights into the interdependencies and predictive potential of these physiological measures in relation to each other.

On the Validity of Single Regression Strategy for Granger Causality Assessment in Cardiovascular and Cardiorespiratory Control Studies

Granger causality (GC) analysis is based on the comparison between prediction error variances computed over the full and restricted models after identifying the coefficients of appropriate vector regressions. GC markers can be computed via a double regression (DR) approach identifying two separate, independent models and a single regression (SR) strategy optimizing the description of the dynamics of the target over the full model and, then, reusing some parts of it in the restricted model. The present study compares the SR and DR strategies over heart period (HP), systolic arterial pressure (SAP) and respiration (R) beat-to-beat series collected during a graded orthostatic challenge induced by head-up tilt in 17 healthy individuals (age: 21-36 yrs; median: 29 yrs; 9 females and 8 males). We found that the DR approach was more powerful than the SR one in detecting the expected stronger involvement of the baroreflex during the challenge, while the expected weaker cardiorespiratory coupling was identified by both SR and DR strategies. The less powerful ability of the SR approach was the result of the greater variance of GC markers compared to the DR strategy. We conclude that, contrary to the suggestions present in literature, the SR approach is not necessarily associated with a smaller dispersion of GC markers. Moreover, we suggest that additional factors, such as the strategy utilized to build embedding spaces and metric utilized to compare prediction error variances, might play an important role in differentiating SR and DR approaches.

On the Different Abilities of Cross-Sample Entropy and K-Nearest-Neighbor Cross-Unpredictability in Assessing Dynamic Cardiorespiratory and Cerebrovascular Interactions

Nonlinear markers of coupling strength are often utilized to typify cardiorespiratory and cerebrovascular regulations. The computation of these indices requires techniques describing nonlinear interactions between respiration (R) and heart period (HP) and between mean arterial pressure (MAP) and mean cerebral blood velocity (MCBv). We compared two model-free methods for the assessment of dynamic HP-R and MCBv-MAP interactions, namely the cross-sample entropy (CSampEn) and k-nearest-neighbor cross-unpredictability (KNNCUP). Comparison was carried out first over simulations generated by linear and nonlinear unidirectional causal, bidirectional linear causal, and lag-zero linear noncausal models, and then over experimental data acquired from 19 subjects at supine rest during spontaneous breathing and controlled respiration at 10, 15, and 20 breaths·minute-1 as well as from 13 subjects at supine rest and during 60° head-up tilt. Linear markers were computed for comparison. We found that: (i) over simulations, CSampEn and KNNCUP exhibit different abilities in evaluating coupling strength; (ii) KNNCUP is more reliable than CSampEn when interactions occur according to a causal structure, while performances are similar in noncausal models; (iii) in healthy subjects, KNNCUP is more powerful in characterizing cardiorespiratory and cerebrovascular variability interactions than CSampEn and linear markers. We recommend KNNCUP for quantifying cardiorespiratory and cerebrovascular coupling.

Integrative perspective of the healthy aging process considering the metabolome, cardiac autonomic modulation and cardiorespiratory fitness evaluated in age groups

The aging process causes changes at all organic levels. Although metabolism, cardiac autonomic modulation (CAM), and cardiorespiratory fitness (CRF) are widely studied as a function of age, they are mainly studied in isolation, thus making it difficult to perceive their concomitant variations. This study aimed to investigate the integrated changes that occur in the metabolome, CAM, and CRF throughout aging in apparently healthy individuals. The subjects (n = 118) were divided into five groups according to age (20-29, 30-39, 40-49, 50-59, and 60-70 years old) and underwent blood collection, autonomic assessment, and a cardiopulmonary exercise test for metabolomics analysis using mass spectrometry and nuclear magnetic resonance, cardiac autonomic modulation analysis, and CRF by peak oxygen consumption analysis, respectively. The Tukey's post hoc and effect size with confidence interval were used for variables with a significant one-way ANOVA effect (P < 0.01). The main changes were in the oldest age group, where the CRF, valine, leucine, isoleucine, 3-hydroxyisobutyrate, and CAM reduced and hippuric acid increased. The results suggest significant changes in the metabolome, CAM, and CRF after the age of sixty as a consequence of aging impairments, but with some changes in the metabolic profile that may be favorable to mitigate the aging deleterious effects.

Functional Connectome of the Human Brain with Total Correlation

Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pairwise measures such as correlation or mutual information. In this work, we build on this idea to infer a large-scale (whole-brain) connectivity network based on Total Correlation and show the possibility of using this kind of network as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of Total Correlation and clustering results are trustable compared to ground truth values. Second, the inferred large-scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies, but, interestingly, can estimate additional relations beyond pairwise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.

Comparing Cross-Sample Entropy and K-Nearest-Neighbor Cross-Predictability Approaches for the Evaluation of Cardiorespiratory and Cerebrovascular Dynamic Interactions

Quantification of the cardiorespiratory and cerebrovascular couplings is a relevant clinical issue given that their changes are considered signs of pathological status. The inherent nonlinearity of mechanisms underlying cardiorespiratory and cerebrovascular links requires nonlinear tools for their reliable evaluation. In the present study we compare two nonlinear methods for the assessment of coupling strength between two time series, namely cross-sample entropy (CSampEn) and k-nearest-neighbor cross-predictability (KNNCP). CSampEn uses a strategy that fixes the pattern length, while KNNCP optimizes the pattern length to maximize cross-predictability. CSampEn and KNNCP were applied to the beat-to-beat series of heart period (HP) and respiration (R) during a controlled breathing protocol with the aim at assessing cardiorespiratory coupling and to the beat-to-beat series of mean cerebral blood flow (MCBF) and mean arterial pressure (MAP) during an orthostatic stressor with the aim at evaluating cerebrovascular coupling. Although both the methods have the possibility to quantify the degree of HP-R and MCBF-MAP association, they exhibited different statistical power and even diverse trends in response to the considered physiological challenges. CSampEn and KNNCP are not interchangeable and should be utilized in association more than in alternative for the quantification of the HP-R and MCBF-MAP coupling strength. Clinical Relevance - This study proves that cross-entropy and cross-predictability might lead to different conclusions about cardiorespiratory and cerebrovascular couplings.

Beyond the Baroreflex: A New Measure of Autonomic Regulation Based on the Time-Frequency Assessment of Variability, Phase Coherence and Couplings

For decades the role of autonomic regulation and the baroreflex in the generation of the respiratory sinus arrhythmia (RSA) - modulation of heart rate by the frequency of breathing - has been under dispute. We hypothesized that by using autonomic blockers we can reveal which oscillations and their interactions are suppressed, elucidating their involvement in RSA as well as in cardiovascular regulation more generally. R-R intervals, end tidal CO2, finger arterial pressure, and muscle sympathetic nerve activity (MSNA) were measured simultaneously in 7 subjects during saline, atropine and propranolol infusion. The measurements were repeated during spontaneous and fixed-frequency breathing, and apnea. The power spectra, phase coherence and couplings were calculated to characterise the variability and interactions within the cardiovascular system. Atropine reduced R-R interval variability (p < 0.05) in all three breathing conditions, reduced MSNA power during apnea and removed much of the significant coherence and couplings. Propranolol had smaller effect on the power of oscillations and did not change the number of significant interactions. Most notably, atropine reduced R-R interval power in the 0.145-0.6 Hz interval during apnea, which supports the hypothesis that the RSA is modulated by a mechanism other than the baroreflex. Atropine also reduced or made negative the phase shift between the systolic and diastolic pressure, indicating the cessation of baroreflex-dependent blood pressure variability. This result suggests that coherent respiratory oscillations in the blood pressure can be used for the non-invasive assessment of autonomic regulation.

Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods

Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.

Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.

Effects of Supplemental Oxygen on Cardiovascular and Respiratory Interactions by Extended Partial Directed Coherence in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic and restrictive disease characterized by fibrosis and inflammatory changes in lung tissue producing a reduction in diffusion capacity and leading to exertional chronic arterial hypoxemia and dyspnea. Furthermore, clinically, supplemental oxygen (SupplO₂) has been prescribed to IPF patients to improve symptoms. However, the evidence about the benefits or disadvantages of oxygen supplementation is not conclusive. In addition, the impact of SupplO₂ on the autonomic nervous system (ANS) regulation in respiratory diseases needs to be evaluated. In this study the interactions between cardiovascular and respiratory systems in IPF patients, during ambient air (AA) and SupplO₂ breathing, are compared to those from a matched healthy group. Interactions were estimated by time series of successive beat-to-beat intervals (BBI), respiratory amplitude (RESP) at BBI onset, arterial systolic (SYS) and diastolic (DIA) blood pressures. The paper explores the Granger causality (GC) between systems in the frequency domain by the extended partial directed coherence (ePDC), considering instantaneous effects. Also, traditional linear and nonlinear markers as power in low (LF) and high frequency (HF) bands, symbolic dynamic indices as well as arterial baroreflex, were calculated. The results showed that for IPF during AA phase: 1) mean BBI and power of BBI-HF band, as well as mean respiratory frequency were significantly lower (p < 0.05) and higher (p < 0.001), respectively, indicating a strong sympathetic influence, and 2) the RESP → SYS interaction was characterized by Mayer waves and diminished RESP → BBI, i.e., decreased respiratory sinus arrhythmia. In contrast, during short-term SupplO₂ phase: 1) oxygen might produce a negative influence on the systolic blood pressure variability, 2) the arterial baroreflex reduced significantly (p < 0.01) and 3) reduction of RSA reflected by RESP → BBI with simultaneous increase of Traube-Hering waves in RESP → SYS (p < 0.001), reflected increased sympathetic modulation to the vessels. The results gathered in this study may be helpful in the management of the administration of SupplO₂.

Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

Quantitative Complexity Theory Used in the Prediction of Head-Up Tilt Testing Outcome

Background

Head-up tilt testing (HUTT), a well-established tool in the diagnosis of vasovagal syncope, is time-consuming, and every provoked vasovagal reaction may result in consolidating the reflex mechanism. Therefore, identification of parameters that could shorten the duration of HUTT and prevent fainting is desirable. Quantitative complexity theory (QCT) may provide holistic information on the cardiovascular reaction in HUTT. The aim of the present article was to evaluate the prognostic value of complexity in comparison with traditional haemodynamic parameters (HR and BP) in predicting the HUTT outcome.

Methods

Eighty-one healthy volunteers (74 men; mean age: 37.8 years) were included in this retrospective analysis of data collected within the project realized in Department of Cardiology and Internal Diseases, Military Institute of Medicine between January 2012 and October 2014. The subjects underwent HUTT, with beat-to-beat haemodynamic monitoring with a Niccomo™. The chosen haemodynamic parameters (including BP, HR, stroke volume, cardiac output, systemic vascular resistance) have been used in complexity analysis.

Results

HUTT was positive in 54 (66.7%) study participants. The values of complexity were already higher in fainting subjects than those were in nonfainting ones 300 s before HUTT termination (HUTT_end), with a significant upward trend starting 150 s before (pre)syncope. An area under the curve (AUC) over 0.700 was observed for complexity from 120 s before HUTT_end, with a sensitivity of 63% and specificity of 78% at this time point. The prognostic value of complexity was superior to that of the HR and mean arterial pressure (MAP).

Conclusions

Complexity has been shown to be a sensitive marker of cardiovascular haemodynamic response to orthostatic stress and proved to be superior over HR and BP in predicting HUTT outcomes.

Multicomponent exercise training in cardiovascular complexity in prefrail older adults: a randomized blinded clinical pilot study

The aim of this study was to investigate the effects of multicomponent training on baroreflex sensitivity (BRS) and heart rate (HR) complexity of prefrail older adults. Twenty-one prefrail community-dwelling older adults were randomized and divided into multicomponent training intervention group (MulTI) and control group (CG). MulTI performed multicomponent exercise training over 16 weeks and CG was oriented to follow their own daily activities. The RR interval (RRi) and blood pressure (BP) series were recorded for 15 min in supine and 15 min in orthostatic positions, and calculation of BRS (phase, coherence, and gain) and HR complexity (sample entropy) were performed. A linear mixed model was applied for group, assessments, and their interaction effects in supine position. The same test was used to assess the active postural maneuver and it was applied separately to each group considering assessments (baseline and post-intervention) and positions (supine and orthostatic). The significance level established was 5%. Cardiovascular control was impaired in prefrail older adults in supine position. Significant interactions were not observed between groups or assessments in terms of cardiovascular parameters. A 16-week multicomponent exercise training did not improve HR complexity or BRS in supine rest or in active postural maneuver in prefrail older adults.

Postural Changes on Heart Rate Variability among Older Population: A Preliminary Study

OBJECTIVE

This study aims to investigate an association between body postures and autonomic nervous system (ANS) responses through analysis of short-term heart rate variability (HRV) data obtained through electrocardiography.

METHODS

Forty older individuals were recruited to form the sample. HRV measurements were taken in three positions-sitting, supine, and standing-and compared.

RESULTS

Results demonstrated statistically significant differences in the HRV parameters used to examine the parasympathetic nervous system (PNS) and the sympathetic nervous system (SNS), specifically in the measurements obtained from the sitting position and the supine position (P < 0.001 for PNS and P = 0.011 for SNS). The differences in these parameters were, however, negligible between the sitting and the standing positions. Moreover, the ANS responses obtained in the sitting position were strongly and positively correlated with those in the standing position (r = 0.854 for PNS and r = 0.794 for SNS). These results suggested that the PNS and SNS parameters obtained while sitting were likely to be affected by orthostatic hypotension in much the same way as those in the standing position, as compared to the supine position.

CONCLUSIONS

As such, sitting may not be the best position for older individuals in the assessment of their autonomic responses, whereas the supine position is recommended as the baseline posture in the old-age population. These findings are useful for future research in clinical settings that require accuracy in the ANS responses as determined by the HRV measurements.

Lack of association between heart period variability asymmetry and respiratory sinus arrhythmia in healthy and chronic heart failure individuals

Temporal asymmetry is a peculiar aspect of heart period (HP) variability (HPV). HPV asymmetry (HPVA) is reduced with aging and pathology, but its origin is not fully elucidated. Given the impact of respiration on HPV resulting in the respiratory sinus arrhythmia (RSA) and the asymmetric shape of the respiratory pattern, a possible link between HPVA and RSA might be expected. In this study we tested the hypothesis that HPVA is significantly associated with RSA and asymmetry of the respiratory rhythm. We studied 42 middle-aged healthy (H) subjects, and 56 chronic heart failure (CHF) patients of whom 26 assigned to the New York Heart Association (NYHA) class II (CHF-II) and 30 to NYHA class III (CHF-III). Electrocardiogram and lung volume were monitored for 8 minutes during spontaneous breathing (SB) and controlled breathing (CB) at 15 breaths/minute. The ratio of inspiratory (INSP) to expiratory (EXP) phases, namely the I/E ratio, and RSA were calculated. HPVA was estimated as the percentage of negative HP variations, traditionally measured via the Porta’s index (PI). Departures of PI from 50% indicated HPVA and its significance was tested via surrogate data. We found that RSA increased during CB and I/E ratio was smaller than 1 in all groups and experimental conditions. In H subjects the PI was about 50% during SB and it increased significantly during CB. In both CHF-II and CHF-III groups the PI was about 50% during SB and remained unmodified during CB. The PI was uncorrelated with RSA and I/E ratio regardless of the experimental condition and group. Pooling together data of different experimental conditions did not affect conclusions. Therefore, we conclude that the HPVA cannot be explained by RSA and/or I/E ratio, thus representing a peculiar feature of the cardiac control that can be aroused in middle-aged H individuals via CB.

Beat-to-beat blood pressure variability: an early predictor of disease and cardiovascular risk

Blood pressure (BP) varies on the long, short and very-short term. Owing to the hidden physiological and pathological information present in BP time-series, increasing interest has been given to the study of continuous, beat-to-beat BP variability (BPV) using invasive and noninvasive methods. Different linear and nonlinear parameters of variability are employed in the characterization of BP signals in health and disease. Although linear parameters of beat-to-beat BPV are mainly measures of dispersion, such as standard deviation (SD), nonlinear parameters of BPV quantify the degree of complexity/irregularity- using measures of entropy or self-similarity/correlation. In this review, we summarize the value of linear and nonlinear parameters in reflecting different information about the pathophysiology of changes in beat-to-beat BPV independent of or superior to mean BP. We then provide a comparison of the relative power of linear and nonlinear parameters of beat-to-beat BPV in detecting early and subtle differences in various states. The practical advantage and utility of beat-to-beat BPV monitoring support its incorporation into routine clinical practices.

Complexity and Nonlinearities of Short-Term Cardiovascular and Cerebrovascular Controls after Surgical Aortic Valve Replacement

We assessed the effect of surgical aortic valve replacement (SAVR) on cardiovascular and cerebrovascular controls with particular attention to their complexity and presence of nonlinear behaviors via the analysis of spontaneous variability of heart period (HP), systolic and diastolic arterial pressure (SAP and DAP) and mean cerebral blood flow (MCBF). Variability series were acquired before (PRE) and after (POST) SAVR in 12 patients (age: 76±4.7 yrs, 7 males) at rest in supine position and during active standing. Complexity was assessed via a local nonlinear prediction approach exploiting the k-nearest neighbor strategy. The presence of nonlinear dynamics was checked by comparing the complexity marker computed over the original series with the distribution of values assessed over 100 surrogates preserving distribution and power spectral density of the original series but with random phases. We found that: i) HP variance was significantly reduced in POST; ii) the complexity of SAP and DAP variabilities increased in POST with a greater likelihood of observing nonlinear dynamics over SAP compared to PRE at supine rest; iii) the amplitude of MCBF fluctuations and its complexity in POST remained similar to PRE. SAVR induces important changes of the cardiac and vascular autonomic controls, while cerebrovascular regulation seems to be less affected.

Are Strategies Favoring Pattern Matching a Viable Way to Improve Complexity Estimation Based on Sample Entropy?

It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.

Probing age-related changes in cardio-respiratory dynamics by multimodal coupling assessment

Quantifying respiratory sinus arrhythmia (RSA) can provide an index of parasympathetic function. Fourier spectral analysis, the most widely used approach, estimates the power of the heart rate variability in the frequency band of breathing. However, it neglects the time-varying characteristics of the transitions as well as the nonlinear properties of the cardio-respiratory coupling. Here, we propose a novel approach based on Hilbert-Huang transform, called the multimodal coupling analysis (MMCA) method, to assess cardio-respiratory dynamics by examining the instantaneous nonlinear phase interactions between two interconnected signals (i.e., heart rate and respiration) and compare with the counterparts derived from the wavelet-based method. We used an online database. The corresponding RSA components of the 90-min ECG and respiratory signals of 20 young and 20 elderly healthy subjects were extracted and quantified. A cycle-based analysis and a synchro-squeezed wavelet transform were also introduced to assess the amplitude or phase changes of each respiratory cycle. Our results demonstrated that the diminished mean and standard deviation of the derived dynamical RSA activities can better discriminate between elderly and young subjects. Moreover, the degree of nonlinearity of the cycle-by-cycle RSA waveform derived from the differences between the instantaneous frequency and the mean frequency of each respiratory cycle was significantly decreased in the elderly subjects by the MMCA method. The MMCA method in combination with the cycle-based analysis can potentially be a useful tool to depict the aging changes of the parasympathetic function as well as the waveform nonlinearity of RSA compared to the Fourier-based high-frequency power and the wavelet-based method.

A Transfer Entropy Approach for the Assessment of the Impact of Inspiratory Muscle Training on the Cardiorespiratory Coupling of Amateur Cyclists

The strength of cardiorespiratory interactions diminishes with age. Physical exercise can reduce the rate of this trend. Inspiratory muscle training (IMT) is a technique capable of improving cardiorespiratory interactions. This study evaluates the effect of IMT on cardiorespiratory coupling in amateur cyclists. Thirty male young healthy cyclists underwent a sham IMT of very low intensity (SHAM, n = 9), an IMT of moderate intensity at 60% of the maximal inspiratory pressure (MIP60, n = 10) and an IMT of high intensity at the critical inspiratory pressure (CIP, n = 11). Electrocardiogram, non-invasive arterial pressure, and thoracic respiratory movement (RM) were recorded before (PRE) and after (POST) training at rest in supine position (REST) and during active standing (STAND). The beat-to-beat series of heart period (HP) and systolic arterial pressure (SAP) were analyzed with the RM signal via a traditional non-causal approach, such as squared coherence function, and via a causal model-based transfer entropy (TE) approach. Cardiorespiratory coupling was quantified via the HP-RM squared coherence at the respiratory rate (K 2 HP-R M), the unconditioned TE from RM to HP (TER M → HP) and the TE from RM to HP conditioned on SAP (TER M → HP| SAP). In PRE condition we found that STAND led to a decrease of TER M → HP| SAP. After SHAM and CIP training this tendency was confirmed, while MIP60 inverted it by empowering cardiorespiratory coupling. This behavior was observed in presence of unvaried SAP mean and with usual responses of the baroreflex control and HP mean to STAND. TER M → HP and K 2 HP- RM were not able to detect the post-training increase of cardiorespiratory coupling strength during STAND, thus suggesting that conditioning out SAP is important for the assessment of cardiorespiratory interactions. Since the usual response of HP mean, SAP mean and baroreflex sensitivity to postural stressor were observed after MIP60 training, we conclude that the post-training increase of cardiorespiratory coupling during STAND in MIP60 group might be the genuine effect of some rearrangements at the level of central respiratory network and its interactions with sympathetic drive and vagal activity.

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

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

Presence of age- and sex-related differences in heart rate variability despite the maintenance of a suitable level of accelerometer-based physical activity

There is currently a lack of information adjacent on the influence of sex and age in heart rate variability (HRV), adjusted according to accelerometer-based physical activity (PADL). We hypothesized that the effect of sex and age on the HRV should be reduced or absent in individuals with a suitable PADL level. We aim to evaluate the influence of sex and age on HRV, adjusted for the confounding effects of the PADL level. A total of 485 age-stratified subjects (18–39, 40–59, and ≥60 years) underwent HRV analyses at rest and 7-day assessments of accelerometer-based PADL. Multivariate analyses of covariance were done using log-transformed HRV indices as outcomes, age and sex as fixed factors, and PADL, cardiovascular risk, fat body mass, and heart rate (HR) at rest as covariates. Despite the adjustment for directly measured PADL, women had better indices of vagal tone, whereas men had higher sympathetic influence. Also, compared to middle-aged and older adults, younger individuals (ages 18–39 years) presented better HRV. Multiple regression analyses confirmed that age and sex were the main predictors of HRV indices, even after adjusting for PADL directly assessed by triaxial accelerometer and HR. We also observed that the correlation between some HRV indexes and the different indexes of physical activity directly evaluated was significant, but not very consistent. Thus, HRV indices are influenced by age and sex, regardless of accelerometer-based physical activity. Interventions with physical activity and exercise aimed at improving the autonomic modulation of asymptomatic adults should take such differences into account.

Cardiac baroreflex hysteresis is one of the determinants of the heart period variability asymmetry

In heart period (HP) variability (HPV) recordings the percentage of negative HP variations tends to be greater than that of positive ones and this pattern is referred to as HPV asymmetry (HPVA). HPVA has been studied in several experimental conditions in healthy and pathological populations, but its origin is unclear. The baroreflex (BR) exhibits an asymmetric behavior as well given that it reacts more importantly to positive than negative arterial pressure (AP) variations. We tested the hypothesis that the BR asymmetry (BRA) is a HPVA determinant over spontaneous fluctuations of HP and systolic AP (SAP). We studied 100 healthy subjects (age from 21 to 70 yr, 54 men) comprising 20 subjects in each age decade. Electrocardiogram and noninvasive AP were recorded for 15 min at rest in supine position (REST) and during active standing (STAND). The HPVA was evaluated via Porta's index and Guzik's index, while the BRA was assessed as the difference, and normalized difference, between BR sensitivities computed over positive and negative SAP variations via the sequence method applied to HP and SAP variability. HPVA significantly increased during STAND and decreased progressively with age. BRA was not significantly detected both at REST and during STAND. However, we found a significant positive association between BRA and HPVA markers during STAND persisting even within the age groups. This study supports the use of HPVA indexes as descriptors of BRA and identified a challenge soliciting the BR response like STAND to maximize the association between HPVA and BRA markers.

Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDC_HR) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDC_LR) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDC_HR patients presented lowered HRV and increased BPV compared to both IDC_LR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDC_LR vs. IDC_HR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

Cardiovascular autonomic modulation and baroreflex control in the second trimester of pregnancy: A cross sectional study

Purpose

The aim is to evaluate and characterize cardiovascular autonomic control and baroreflex function and their response to an orthostatic stressor in the second trimester of pregnancy via time, frequency, information and symbolic analyses.

Methods

We evaluated 22 women at 18 weeks of pregnancy, labeled as pregnant group (PG) (30.8±4.4 years), and 22 non-pregnant women (29.8±5.4 years), labeled as control group (CG). Electrocardiogram, non-invasive photoplethysmographic arterial pressure (AP) and respiratory signals were recorded at rest at left lateral decubitus (REST) and during active standing (STAND) for 10 minutes. The heart period (HP) variability and systolic AP (SAP) variability were assessed in the frequency domain. High frequency (HF) and low frequency (LF) spectral indexes were computed. Nonlinear indexes such as symbolic markers (0V%, 1V%, 2LV% and 2UV% indexes), Shannon entropy (SE) and normalized complexity index (NCI) were calculated as well. Baroreflex control was assessed by cross-spectral HP-SAP analysis. We computed baroreflex sensitivity (BRS), HP-SAP squared coherence (K²) and phase in LF and HF bands.

Results

At REST, the PG had lower mean, variance and HF power of HP series and lower K²(LF), BRS(LF) and BRS(HF) than the CG. During STAND, CG and PG decreased the mean, CI, NCI and 2UV% and increased 0V% of the HP series and augmented the SAP variance. LFabs of SAP series increased during STAND solely in CG. BRS(HF) was reduced during in both PG and CG, while HFabs of HP series did not diminish during STAND either in PG or CG. Complexity of the autonomic control was similar in PG and CG regardless of the experimental condition.

Conclusion

We conclude that the second trimester of pregnancy was characterized by a lower parasympathetic modulation and reduced BRS at REST, preserved complexity of cardiac and vascular controls, limited sympathetic response to STAND and general conservation of the baroreflex responses to posture changes.

Trial registration

Begistro Brasileiro de Ensaios clínicos, Number: RBR-9s8t88.

Baroreflex sensitivity in frailty syndrome

Frailty is related to a decrease in the physiological reserves, which causes difficulties in maintaining homeostasis. An example of physiological mechanisms for cardiovascular homeostasis is the baroreflex. The aim of this study was to compare baroreflex among frail, prefrail, and nonfrail individuals, in supine and orthostatic positions. Community-dwelling older adults were evaluated and categorized into frail, prefrail, or nonfrail groups, according to frailty phenotype. The RR interval (RRi) and systolic blood pressure (SBP) series were recorded for 15 min in the supine and 15 min in the orthostatic positions. Mean and variance of RRi and SBP, and baroreflex evaluated by phase, gain (α), and coherence (K2) were determined. A two-way repeated measures ANOVA, with Tukey's post hoc, was applied for group, position, and their interaction effects. The significance level established was 5%. Prefrail and frail participants did not present a significant decrease in mean values of RRi after postural challenge (893.43 to 834.20 ms and 925.99 to 857.98 ms, respectively). Frail participants showed a reduction in RRi variance in supine to orthostatic (852.04 to 232.37 ms2). Prefrail and frail participants showed a decrease in K2 after postural change (0.69 to 0.52 and 0.54 to 0.34, respectively). Frail participants exhibited lower values of K2 (0.34) compared to nonfrail and prefrail participants (0.61 and 0.52, respectively). Baroreflex indicated the presence of decoupling between heart period and SBP in frail and prefrail. Thus, reduced K2 might be a marker of the frailty process.

Heart Rate Variability and Performance of Commercial Airline Pilots during Flight Simulations

Pilots undergo a variety of stressors that may affect their performance during all phases of flight. Heart rate variability (HRV) has been considered as a reliable indicator of the parasympathetic and sympathetic activities of human autonomic nervous system, which can be used to characterize the sympathetic stress response of pilots during flight. In this study, thirty active commercial airline pilots were recruited to fly three flight segments in a Federal Aviation Administration (FAA)-certified A320 flight simulator with each segment at a different carbon dioxide (CO₂) concentration on the flight deck. The pilots performed a series of maneuvers of varying difficulty, and their performance was evaluated by FAA designated pilot examiners. The HRV metrics (SDNN, RMSSD and LF/HF ratio) of each pilot both before and during flight simulations were measured with a Movisens EcgMove3 sensor. The average SDNN, RMSSD and LF/HF ratio of the pilots during flight simulations were 34.1 ± 12.7 ms, 23.8 ± 10.2 ms and 5.7 ± 2.8 respectively. Decreased HRV was associated with aging, obesity and performing difficult maneuvers. Both CO₂ exposure and HRV had an independent effect on the pilot performance, while their interaction was not significant. The generalized additive mixed effect model results showed that a pilot performed better on a maneuver when his stress response was lower, as indicated by higher SDNN and RMSSD and lower LF/HF ratio. An interquartile range (IQR) increase in SDNN (21.97 ms) and RMSSD (16.00 ms) and an IQR decrease in LF/HF ratio (4.69) was associated with an increase in the odds of passing a maneuver by 37%, 22% and 20%, respectively.

Effect of Aging on Muscle-Pump Baroreflex of Individual Leg Muscles During Standing

Activation of leg muscles is an important component in the regulation of blood pressure during standing, failure of which could result in syncope and falls. Our previous work demonstrated baroreflex mediated activation of leg muscles (muscle-pump baroreflex) as an important factor in the regulation of blood pressure during standing; however, the effect of aging on the muscle-pump baroreflex of individual leg muscles during standing remains to be understood. Here, the interaction between systolic blood pressure (SBP) and the activation of lateral gastrocnemius (LG), medial gastrocnemius (MG), tibialis anterior (TA), and soleus (SOL) muscles during standing was quantified. Beat-to-beat heart period (RR interval), SBP, electromyography impulse (EMG_imp) were derived from continuously acquired electrocardiography, finger blood pressure, and calf-electromyography, respectively. The cardiac baroreflex (SBP→RR) causality (0.88 ± 0.08 vs. 0.94 ± 0.03, p = 0.01), percent time with significant coherence (%SC: 50.95 ± 23.31 vs. 76.75 ± 16.91, p = 0.001), and gain (4.39 ± 4.38 vs. 13.05 ± 8.11, p < 0.001) was lower in older (69 ± 4 years) compared to young (26 ± 2 years) persons. Muscle-pump baroreflex (SBP→EMG_imp) causality of LG (0.81 ± 0.08 vs. 0.88 ± 0.05, p = 0.01) and SOL (0.79 ± 0.11 vs. 0.88 ± 0.04, p = 0.01) muscles was lower in older compared to young persons. %SC was lower for all muscles in the older group (LG, p < 0.001; MG, p = 0.01; TA, p = 0.01; and SOL, p < 0.001) compared to young. The study outcomes highlighted impairment in muscle-pump baroreflex with age in addition to cardiac baroreflex. The findings of the study can assist in the development of an effective system for monitoring orthostatic tolerance via cardiac and muscle-pump baroreflexes to mitigate syncope and falls.

Paced Breathing Increases the Redundancy of Cardiorespiratory Control in Healthy Individuals and Chronic Heart Failure Patients

Synergy and redundancy are concepts that suggest, respectively, adaptability and fault tolerance of systems with complex behavior. This study computes redundancy/synergy in bivariate systems formed by a target X and a driver Y according to the predictive information decomposition approach and partial information decomposition framework based on the minimal mutual information principle. The two approaches assess the redundancy/synergy of past of X and Y in reducing the uncertainty of the current state of X. The methods were applied to evaluate the interactions between heart and respiration in healthy young subjects (n = 19) during controlled breathing at 10, 15 and 20 breaths/minute and in two groups of chronic heart failure patients during paced respiration at 6 (n = 9) and 15 (n = 20) breaths/minutes from spontaneous beat-to-beat fluctuations of heart period and respiratory signal. Both methods suggested that slowing respiratory rate below the spontaneous frequency increases redundancy of cardiorespiratory control in both healthy and pathological groups, thus possibly improving fault tolerance of the cardiorespiratory control. The two methods provide markers complementary to respiratory sinus arrhythmia and the strength of the linear coupling between heart period variability and respiration in describing the physiology of the cardiorespiratory reflex suitable to be exploited in various pathophysiological settings.

Multiscale network dynamics between heart rate and locomotor activity are altered in schizophrenia

OBJECTIVE

Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls.

APPROACH

HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls. Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy. A support vector machine was trained on these features to discriminate patients from controls. Additionally, time series were converted into a network with nodes comprised of HR and locomotor activity states, and edges representing state transitions. Graph properties were used as features. Leave-one-out cross validation was performed. To compare against non-psychiatric illness, the same approach was repeated in 41 patients with atrial fibrillation (AFib) and 53 controls.

MAIN RESULTS

Network features enabled perfect discrimination of schizophrenia patients from controls with an areas under the receiver operating characteristic curve (AUC) of 1.00 for training and test data. Other bivariate measures of interaction achieved lower AUCs (train 0.98, test 0.96), and univariate measures of complexity achieved the lowest performance. Conversely, interaction features did not improve discrimination of AFib patients from controls beyond univariate approaches.

SIGNIFICANCE

Interactions between HR and locomotor activity enabled perfect discrimination of subjects with schizophrenia from controls, but these features were less performant in a non-psychiatric illness. This is the first quantitative evaluation of interactions between physiology and behavior in patients with psychiatric illness.

Comparison between probabilistic and Wiener-Granger causality in assessing modifications of the cardiac baroreflex control with age

BACKGROUND

Probabilistic causality (PC) is a framework for checking that the occurrence of a cause raises the probability of the effect by comparing the probability of the effect conditioned and unconditioned to the cause. Even though it is less frequently utilized with respect to the more traditional model-based Wiener-Granger causality (WGC) that is based on the predictability improvement of an effect resulting from the inclusion of the presumed cause in the multivariate linear regression model, PC has the advantage of being model-free.

OBJECTIVE

The aim of the study is to apply the PC framework to assess the evolution of cardiac baroreflex control with age from spontaneous fluctuations of heart period (HP) and systolic arterial pressure (SAP) and to compare it to the more common WGC approach.

APPROACH

We studied 100 healthy humans (54 males, age: 21-70 years, 20 subjects for each 10 years bin). HP and SAP were extracted on a beat-to-beat basis from 5 min recordings of electrocardiogram and plethysmographic arterial pressure at rest in supine position (REST) and during active standing (STAND) under spontaneous breathing. The WGC ratio (WGCR) was computed as the log ratio of the prediction error variance of the autoregressive model on HP to that on HP with exogenous SAP. The PC ratio (PCR) was computed as the probability of observing an HP ramp given an associated parallel SAP variation divided by the probability of observing an HP ramp.

MAIN RESULTS

The WGCR and PCR suggested the gradual impairment of cardiac baroreflex with age, especially during STAND. Moreover, they were significantly associated both at REST and during STAND but the degree of the PCR-WGCR association was weak.

SIGNIFICANCE

PC can be effectively exploited to assess modification of the cardiovascular control during senescence even though a limited agreement was observed with WGC.