Preejection period as a sympathetic activity index: a role of confounding factors

In previous studies, one of the systolic time intervals - preejection period (PEP) - was used as an index of sympathetic activity reflecting the cardiac contractility. However, PEP could be also influenced by several other cardiovascular variables including preload, afterload and diastolic blood pressure (DBP). The aim of this study was to assess the behavior of the PEP together with other potentially confounding cardiovascular system characteristics in healthy humans during mental and orthostatic stress (head-up tilt test - HUT). Forty-nine healthy volunteers (28 females, 21 males, mean age 18.6 years (SD=1.8 years)) participated in the study. We recorded finger arterial blood pressure by volume-clamp method (Finometer Pro, FMS, Netherlands), PEP, thoracic fluid content (TFC) - a measure of preload, and cardiac output (CO) by impedance cardiography (CardioScreen (R) 2000, Medis, Germany). Systemic vascular resistance (SVR) - a measure of afterload - was calculated as a ratio of mean arterial pressure and CO. We observed that during HUT, an expected decrease in TFC was accompanied by an increase of PEP, an increase of SVR and no significant change in DBP. During mental stress, we observed a decrease of PEP and an increase of TFC, SVR and DBP. Correlating a change in assessed measures (delta values) between mental stress and previous supine rest, we found that Delta PEP correlated negatively with Delta CO and positively with Delta SVR. In orthostasis, no significant correlation between Delta PEP and Delta DBP, Delta TFC, Delta CO, Delta MBP or Delta SVR was found. We conclude that despite an expected increase of sympathetic activity during both challenges, PEP behaved differently indicating an effect of other confounding factors. To interpret PEP values properly, we recommend simultaneously to measure other variables influencing this cardiovascular measure.Web of Science66suppl. 2S275S26

Spectral analysis of the beat-to-beat variability of arterial compliance

Arterial compliance is an important parameter influencing ventricular-arterial coupling, depending on structural and functional mechanics of arteries. In this study, the spontaneous beat-to-beat variability of arterial compliance was investigated in time and frequency domains in thirty-nine young and healthy subjects monitored in the supine resting state and during head-up tilt. Spectral decomposition was applied to retrieve the spectral content of the time series associated to low (LF) and high frequency (HF) oscillatory components. Our results highlight: (i) a decrease of arterial compliance with tilt, in agreement with previous studies; (ii) an increase of the LF power content concurrent with a decrease of the HF power, potentially reflecting changes in vasomotor tone, blood pressure and heart rate variability associated with higher sympathetic activity and vagal withdrawal occurring with tilt

Comparison of Linear Model-Based and Nonlinear Model-Free Directional Coupling Measures: Analysis of Cardiovascular and Cardiorespiratory Interactions at Rest and During Physiological Stress

In this work, we present an investigation of the cardiovascular and cardiorespiratory regulatory mechanisms involved during stress responses using the information-theoretic measure of transfer entropy (TE). Specifically, the aim of the study is to compare different estimation approaches for the evaluation of the information transferred among different physiological systems. The analysis was carried out on the series of heart period, systolic arterial pressure and respiration measured from 61 young healthy subjects, at rest and during orthostatic and mental stress states, by using both a linear model-based and a nonlinear modelfree approaches to compute TE. The results reveal mostly significant correlations for the measures of TE estimated with the two approaches, particularly when assessing the influence of respiration on cardiovascular activity during mental stress and the influence of vascular dynamics on cardiac activity during tilt. Therefore, our findings suggest that the simpler linear parametric approach is suitable in conditions predominantly regulated by sympathetic nervous system or by the withdrawal of the parasympathetic system

Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures

Goal: The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate to infer the underlying network topology. To address these limitations, here we introduce a framework which combines the assessment of high-order interactions with statistical inference for the characterization of the functional links sustaining physiological networks. Methods: The framework develops information-theoretic measures quantifying how two nodes interact in a redundant or synergistic way with the rest of the network, and employs these measures for reconstructing the functional structure of the network. The measures are implemented for both static and dynamic networks mapped respectively by random variables and random processes using plug-in and model-based entropy estimators. Results: The validation on theoretical and numerical simulated networks documents the ability of the framework to represent high-order interactions as networks and to detect statistical structures associated to cascade, common drive and common target effects. The application to cardiovascular networks mapped by the beat-to-beat variability of heart rate, respiration, arterial pressure, cardiac output and vascular resistance allowed noninvasive characterization of several mechanisms of cardiovascular control operating in resting state and during orthostatic stress. Conclusion: Our approach brings to new comprehensive assessment of physiological interactions and complements existing strategies for the classification of pathophysiological states

Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

This study investigates the feasibility of classifying physiological stress states usingMachine Learning (ML) algorithms on short-term (ST,∼5min) and ultra-short-term (UST, < 5 min, down to 10 heartbeats) heart rate (HRV) or pulse rate variability (PRV) features computed from inter-beat interval time series. Three widely employed ML algorithms were used, i.e. Naive Bayes Classifier, Support Vector Machines, and Neural Networks, on various time-, frequency and information domain HRV/PRV indices on a single-feature basis. Data were collected from healthy individuals during different physiological states including rest, postural and mental stress. Results highlighted comparable values using either HRV or PRV indices, and higher accuracy (>65% for most features and all classifiers) when classifying postural than mental stress. While decreasing the time series length, time-domain indices resulted still reliable down to ∼10 s, contrary to UST frequency-domain features which reported lower accuracy below 60 heartbeats

Respiratory Sinus Arrhythmia Mechanisms in Young Obese Subjects

Autonomic nervous system (ANS) activity and imbalance between its sympathetic and parasympathetic components are important factors contributing to the initiation and progression of many cardiovascular disorders related to obesity. The results on respiratory sinus arrhythmia (RSA) magnitude changes as a parasympathetic index were not straightforward in previous studies on young obese subjects. Considering the potentially unbalanced ANS regulation with impaired parasympathetic control in obese patients, the aim of this study was to compare the relative contribution of baroreflex and non-baroreflex (central) mechanisms to the origin of RSA in obese vs. control subjects. To this end, we applied a recently proposed information-theoretic methodology – partial information decomposition (PID) – to the time series of heart rate variability (HRV, computed from RR intervals in the ECG), systolic blood pressure (SBP) variability, and respiration (RESP) pattern measured in 29 obese and 29 ageand gender-matched non-obese adolescents and young adults monitored in the resting supine position and during postural and cognitive stress evoked by head-up tilt and mental arithmetic. PID was used to quantify the so-called unique information transferred from RESP to HRV and from SBP to HRV, reflecting, respectively, non-baroreflex and RESP-unrelated baroreflex HRV mechanisms, and the redundant information transferred from (RESP, SBP) to HRV, reflecting RESP-related baroreflex RSA mechanisms. Our results suggest that obesity is associated: (i) with blunted involvement of non-baroreflex RSA mechanisms, documented by the lower unique information transferred from RESP to HRV at rest; and (ii) with a reduced response to postural stress (but not to mental stress), documented by the lack of changes in the unique information transferred from RESP and SBP to HRV in obese subjects moving from supine to upright, and by a decreased redundant information transfer in obese compared to controls in the upright position. These findings were observed in the presence of an unchanged RSA magnitude measured as the high frequency (HF) power of HRV, thus suggesting that the changes in ANS imbalance related to obesity in adolescents and young adults are subtle and can be revealed by dissecting RSA mechanisms into its components during various challenges

Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures

Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time–domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices