The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct  +  indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR  →  DBP  →  SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP  →  RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP  →  RR interactions, as well as of weaker RR  →  DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.

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Detection of subclinical autonomic dysfunction in patients with diabetes mellitus (DM) is of vital importance for risk stratification and subsequent management. Heart rate variability (HRV) analysis is a sensitive tool for assessment of cardiovascular autonomic dysfunction. As the heart is controlled by non-linear deterministic system, the non-linear dynamics measures should be preferred. Recurrence plot (RP) is able to analyse recurrences within system dynamics. The aim of the study was to detect heart rate dysregulation in DM by RP and to ascertain which of the recurrence quantification analysis (RQA) measures are changed in patients with DM compared to control group. We analysed HRV recordings from 17 young patients with type 1 DM and 17 healthy matched control subjects. RQA was performed on RPs with a fixed value of recurrence points percentage. From RQA measures based on diagonal lines, we have found higher percentage of determinism in DM group (P=0.038). Trapping time measure was also higher in DM (P=0.022). RQA revealed changes in dynamics recurrences with reduced complexity of heart rate control in young diabetic patients. As RQA parameters are independent of overall HRV, parameters of RP should be used together with linear HRV parameters for better description of heart rate dysregulation in patients with diabetics.

Multiscale entropy (MSE) analysis provides information about complexity on various time scales. The aim of this study was to test whether MSE is able to detect autonomic dysregulation in young patients with diabetes mellitus (DM). We analyzed heart rate (HR) oscillations, systolic (SBP) and diastolic blood pressure (DBP) signals in 14 patients with DM type 1 and 14 age- and sex-matched healthy controls. SampEn values (scales 1-10) and linear measures were computed. HR: among the linear measures of heart rate variability significant differences between groups were only found for RMSSD (p = 0.043). MSE was significantly reduced on scales 2 and 3 in DM (p = 0.023 and 0.010, respectively). SBP and DBP: no significant differences were detected with linear measures. In contrast, MSE analysis revealed significantly lower SampEn values in DM on scale 3 (p = 0.039 for SBP; p = 0.015 for DBP). No significant correlations were found between MSE and linear measures. In conclusion, MSE analysis of HR, SBP and DBP oscillations is able to detect subtle abnormalities in cardiovascular control in young patients with DM and is independent of standard linear measures.