To contribute to the understanding of the complex dynamics in the cardiovascular system (CVS), the central CVS has previously been analyzed through multifractal analyses of heart rate variability (HRV) signals that were shown to bring useful contributions. Similar approaches for the peripheral CVS through the analysis of laser Doppler flowmetry (LDF) signals are comparatively very recent. In this direction, we propose here a study of the peripheral CVS through a multifractal analysis of LDF fluctuations, together with a comparison of the results with those obtained on HRV fluctuations simultaneously recorded. To perform these investigations concerning the biophysics of the CVS, first we have to address the problem of selecting a suitable methodology for multifractal analysis, allowing us to extract meaningful interpretations on biophysical signals. For this purpose, we test four existing methodologies of multifractal analysis. We also present a comparison of their applicability and interpretability when implemented on both simulated multifractal signals of reference and on experimental signals from the CVS. One essential outcome of the study is that the multifractal properties observed from both the LDF fluctuations (peripheral CVS) and the HRV fluctuations (central CVS) appear very close and similar over the studied range of scales relevant to physiology.

Analysis of the cardiovascular system (CVS) activity is important for several purposes, including better understanding of heart physiology, diagnosis and forecast of cardiac events. The central CVS, through the study of heart rate variability (HRV), has been shown to exhibit multifractal properties, possibly evolving with physiologic or pathologic states of the organism. An additional viewpoint on the CVS is provided at the peripheral level by laser Doppler flowmetry (LDF), which enables local blood perfusion monitoring. We report here for the first time a multifractal analysis of LDF signals through the computation of their multifractal spectra. The method for estimation of the multifractal spectra, based on the box method, is first described and tested on a priori known synthetic multifractal signals, before application to LDF data. Moreover, simultaneous recordings of both central HRV and peripheral LDF signals, and corresponding multifractal analyses, are performed to confront their properties. With the scales chosen on the partition functions to compute Renyi exponents, LDF signals appear to have broader multifractal spectra compared to HRV. Various conditions for LDF acquisitions are tested showing larger multifractal spectra for signals recorded on fingers than on forearms. The results uncover complex interactions at central and peripheral CVS levels.

Multiscale entropy of LDF signals in healthy subjects shows variation with scales. Moreover, as the variation pattern observed appears similar for all the tested signals, multiscale entropy could potentially be a useful stationary signature for LDF signals, which otherwise are probe-position and subject dependent. Further work could now be conducted to evaluate possible diagnostic purposes of the multiscale entropy of LDF signals.

Laser speckle contrast imaging (LSCI) is a recent clinical powerful tool to obtain full-field images of microvascular blood perfusion. The technique relies on laser speckle obtained by the interactions between coherent monochromatic radiations and the tissues under study. From these speckle images, contrast values are determined and instantaneous map of the perfusion are computed. LSCI has gained increased attention in the last years and is now additional to laser Doppler flowmetry (LDF). In spite of the growing interest for LSCI in skin clinical research, very few LSCI perfusion data processing have been published from now to extract physiologically-linked indices. By opposition, numerous signal processing works have been dedicated to the processing of LDF signals. The latter works proposed methodological processing procedures to extract information reflecting underlying microvascular mechanisms such as myogenic, neurogenic and endothelial activities. Our goal herein is to report on the potentialities of studies dedicated to the processing of LSCI perfusion data. Linear and nonlinear analyses could be of interest to improve the understanding of LSCI images.Keywords Laser speckle contrast imaging Á Microcirculation Á Signal processing Á Image processing Á Laser Doppler flowmetryIn clinical research, the real-time monitoring of skin microvascular blood perfusion can be performed, among others, with laser Doppler flowmetry (LDF) and laser speckle contrast imaging (LSCI) (see, e.g., [7, 13, 18, 26,43,46]). LDF has been proposed in the 1970's [45] to monitor the microvascular blood perfusion in a small volume of tissue (approximately 1 mm 3 in skin when a 780 nm laser wavelength is used). Since that time, many works have led to the improvements of the technique (see, e.g., [1-6, 10, 29, 38, 39, 47]). LSCI is a more recent technique developed in the mid 1990s. It relies on the speckle phenomenon generated by the interactions between coherent monochromatic radiations and a scattering medium [7]. The first speckle imagers were commercialized very recently and from this time LSCI has been the subject of an increasing number of papers (see, e.g., [7,30,31,40]). LSCI and LDF have found widespread skin vascular application among which we can cite the reperfusion monitoring of skin flaps, the quantification of peripheral vascular diseases, the perfusion monitoring of burns, wounds and foot ulcers. LSCI has the advantage, over LDF, of being a noncontact method and giving a twodimensional image of the perfusion, thus reducing the spatial variability of the measure [42].In the last years, several LDF models and simulations [9, 16, 22-24, 27, 35-37] and many LDF signal processing studies have been published. A number of these signal processing works use wavelets to extract data from the heart rate, respiration, myogenic, neurogenic and endothelial activities (see, e.g., [12, 17,44]