Work

Complex Network Analysis of Cardiovascular Time Series: Sex Differences in Visibility Graphs

Year: 2024

Type: conference-paper

Abstract: Nonlinear time series analysis is widely used to study physiological signals under pathological conditions or specific external stimuli, however there are few large-scale researches on how complex dynamics can change based on population characteristics, e.g. sex. In this study, we investigate sex differences in cardiovascular signals from 147 women and 129 men, by using the HYPOL database. We applied the visibility graph (VG) approach, a complex network analysis method, to the interbeat interval (RR) series, and systolic and diastolic blood pressure (SBP and DBP) series. Results on the Average Degree ($D_{A}$) quantifier extracted from the VGs of SBP and RR series showed significantly higher values in males when compared to females ($p=\mathbf{3. 1 1 8} e^{-06}$ and $p=\mathbf{0. 0 4 6}$, respectively). These findings support the hypothesis of the existence of sex-specific factors regulating complex dynamics of healthy cardiovascular activity.

Source: Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)

DOI: 10.1109/esgco63003.2024.10766996

Authors: Laura Lavezzo, Enzo Pasquale Scilingo, Mimma Nardelli

Institution: University of Pisa

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: Ministry of Education