Biology subjects

Javorka, M.

Publications and source records attributed to Javorka, M..

3 recordsLinked to original sources

bioRxiv2025

Direct causality measures unravel complex networks of cardiovascular oscillations and their modifications with postural stress

This study provides a comprehensive investigation of the spontaneous short-term regulatory mechanisms affecting cardiovascular and cardiorespiratory interactions during supine rest and in response to postural stress. The direct causality measure of conditional transfer entropy was applied to beat-to-beat heart period, arterial pressure, respiration, and arterial compliance variability series assessed in thirty-nine healthy subjects during the supine resting state and the orthostatic challenge. The inferred physiological networks behind these two conditions reveal well-known regulatory mechanisms, such as the tilt-induced decreased respiratory sinus arrhythmia (RSA) and increased baroreflex, as well as less explored interactions such as those involving compliance, which suggest striking physiological responses. Specifically, we found tight relationships between compliance and heart period, arterial pressure and respiration, which advocate the non negligible involvement of this cardio-vascular parameter into the intricate hank of the most studied physiological interconnections. Furthermore, the joint use of parametric and model-free estimation approaches allowed us to infer the prevalence of linear and nonlinear dynamics, as well as the effects on the inferred directed links of low- and high-frequency oscillations reflecting autonomic modulation. In conclusion, our study proves that direct causality measures are crucial to assess the characteristic links of complex cardiovascular networks and infer the many underlying short-term regulatory mechanisms. NEW & NOTEWORHTYWhile short-term regulatory mechanisms involving heart period, respiration and arterial pressure have been widely investigated, the way they produce and buffer cardiovascular oscillations in different physiological states is not fully understood. This study proposes a thorough investigation of a four-node physiological network, including the less explored arterial compliance variability, and provides insights into the linear vs. nonlinear characterization and spectral content of the causal dynamics representing each link within the network.

bioengineering↗

bioRxiv2022

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 [~]5 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 regularity and complexity of cardiovascular dynamics, on time series lasting up 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 evidence that shorter time series up to 120 samples still exhibit an acceptable agreement with the ST reference, and can be exploited to discriminate between stress and rest as well. Moreover, although neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting suitable to be implemented on wearable devices.

bioengineering↗

bioRxiv2020

Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

While cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest, reflect the mechanisms of short term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress.

bioengineering↗