The purpose of this study is to investigate the feasibility of nonlinear methods for differentiating between haemodynamic steady states as a potential method of identifying microvascular dysfunction. As conventional nonlinear measures do not take into account the multiple time scales of the processes modulating microvascular function, here we evaluate the efficacy of multiscale analysis as a better discriminator of changes in microvascular health. We describe the basis and the implementation of the multiscale analysis of the microvascular blood flux (BF) and tissue oxygenation (OXY: oxyHb) signals recorded from the skin of 15 healthy male volunteers, age 29.2 ± 8.1y (mean ± SD), in two haemodynamic steady states at 33 °C and during warming at 43 °C to generate a local thermal hyperaemia (LTH). To investigate the influence of varying process time scales, multiscale analysis is employed on Sample entropy (MSE), to quantify signal regularity and Lempel and Ziv (MSLZ) and effort to compress (METC) complexity, to measure the randomness of the time series. Our findings show that there was a good discrimination in the multiscale indexes of both the BF (p = 0.001) and oxyHb (MSE, p = 0.002; METC and MSLZ, p < 0.001) signals between the two haemodynamic steady states, having the highest classification accuracy in oxyHb signals (MSE: 86.67%, MSLZ: 90.00% and METC: 93.33%). This study shows that “multiscale-based” analysis of blood flow and tissue oxygenation signals can identify different microvascular functional states and thus has potential for the clinical assessment and diagnosis of pathophysiological conditions.
