Publications by authors named "Y Antonacci"
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.
View Article and Find Full Text PDFThe objective was to compare the symptom networks of long-COVID and chronic fatigue syndrome (CFS) in conjunction with other theoretically relevant diagnoses in order to provide insight into the etiology of medically unexplained symptoms (MUS). This was a cross-sectional comparison of questionnaire items between six groups identified by clinical diagnosis. All participants completed a 65-item psychological and somatic symptom questionnaire (GSQ065).
View Article and Find Full Text PDFThe increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system.
View Article and Find Full Text PDFArticle Synopsis
- The concept of self-predictability is essential for analyzing the self-driven dynamics of physiological processes that exhibit complex oscillatory rhythms.
- The authors propose a new method to characterize linear self-predictability in the frequency domain, linking it to specific oscillatory components through spectral decomposition.
- The approach was demonstrated through simulations and applied to real-time data, revealing critical information about how physiological responses vary with frequency, particularly in reaction to postural stress, which isn't easily seen in traditional time-domain analyses.
Decomposing the Mutual Information Rate of Heart Period and Respiration Variability Series to Assess Cardiorespiratory Interactions.
Annu Int Conf IEEE Eng Med Biol Soc
July 2023
Article Synopsis
- Heart rate variability is influenced by the interplay between cardiovascular and cardiorespiratory systems, which work together alongside the autonomic nervous system to maintain balance in the body.
- The study investigates these physiological interactions using a method called Mutual Information Rate (MIR), breaking it down into measures of complexity and causality through a non-parametric approach.
- Testing both simulated models and real data from healthy subjects, the findings reveal that MIR decomposition effectively reveals how short-term physiological mechanisms respond to changes in body position, highlighting the cardiorespiratory interactions under stress.