Complexity quantification of signals from the heart, the macrocirculation and the microcirculation through a multiscale entropy analysis

Abstract Quantifying and modelling the cardiovascular system (CVS) represent a challenge to improve our understanding of the CVS. To describe and quantify the CVS, several physiological signals have been analyzed through various signal processing methods. Recently, a quantitative descriptor – the multiscale entropy (MSE) – has been proposed to quantify time series complexity (i.e. the degree of regularity of signal fluctuations) over multiple time scales. Heart rate variability (HRV) signals (i.e. data from the heart) have largely been studied through MSE. By contrast, complexities of signals from the macrocirculation (i.e. elastic and muscular arteries) and the microcirculation (i.e. arterioles and capillaries), two other main components of the CVS, have rarely been investigated simultaneously with MSE. We therefore propose to quantify and compare complexity of signals from these three CVS subsystems: the heart, the macrocirculation and the microcirculation, using MSE. Electrocardiograms, electrical bio-impedance signals (macrocirculation), as well as laser Doppler flowmetry (LDF) signals from finger and forearm (microcirculation) have been recorded simultaneously in nine healthy subjects. The MSE values from these data have been computed and compared. We note a significant lower complexity on scales τ = 1, 2 and 3 (i.e. around 1.08 Hz, 0.54 Hz and 0.36 Hz respectively) for LDF signals from the finger compared to the ones of signals from the heart and the macrocirculation. On scale τ = 5 (i.e. 0.21 Hz), complexity value of signals from the macrocirculation is significantly lower than the ones of HRV and data from the microvascular blood flow in forearm. The three CVS subsystems present different complexity values depending on scales. It could now be of interest to investigate if these complexity differences are due to physiological activities. Moreover, our results could be compared with those obtained from data recorded on patients with vascular diseases.