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

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.