In recent years, the assessment of systolic acceleration in lower-extremity peripheral artery disease (PAD) has been brought back into the spotlight, whatever measure is used: time (in s) or acceleration (in cm.s⁻²). Acceleration time (also called systolic rise time) and maximal acceleration are two different but very useful measurements of growing interest in PAD. A background of the historical development, physics rationale, semantics, and methods of measurement, as well as their strengths and weaknesses, are discussed herein. Acceleration time is a powerful tool for predicting significant arterial stenosis or for estimating the overall impact of PAD as it is highly correlated to the ankle or toe pressure indexes. It could even become a new diagnostic criterion for critical limb ischemia. Similarly, maximal systolic acceleration ratios are highly predictive of carotid or renal stenosis. However, the literature lacks reference standards or guidelines for the assessment of such variables, and their measurement techniques seem to differ between authors. We propose herein a semantic and measurement statement order to clarify and help standardize future research.

Microvascular perfusion is commonly used to study the peripheral cardiovascular system. Microvascular blood flow can be continuously and non-invasively monitored with laser speckle contrast imaging (LSCI) or with laser Doppler flowmetry (LDF). These two optical-based techniques give perfusion values in arbitrary units. Our goal is to better understand the perfusion time series given by each technique. For this purpose, we propose a nonlinear complexity analysis of LSCI and LDF time series recorded simultaneously in nine healthy subjects. This is performed through the computation of their multiscale compression entropy. The results obtained with LSCI time series computed from different regions of interest (ROI) sizes are examined. Our findings show that, for LSCI and LDF time series, compression entropy values are less than one for all of the scales analyzed. This suggests that, for all scales, there are repetitive structures within the data fluctuations. Moreover, at the largest scales studied, LDF signals seem to have structures that are different from those Entropy 2014, 16 5778 of Gaussian white noise. By opposition, this is not observed for LSCI time series computed from small ROI sizes

Time irreversibility can be qualitatively defined as the degree of a signal for temporal asymmetry. Recently, a time irreversibility characterization method based on entropies of positive and negative increments has been proposed for experimental signals and applied to heart rate variability (HRV) data (central cardiovascular system (CVS)). The results led to interesting information as a time asymmetry index was found different for young subjects and elderly people or heart disease patients. Nevertheless, similar analyses have not yet been conducted on laser Doppler flowmetry (LDF) signals (peripheral CVS). We first propose to further investigate the above-mentioned characterization method. Then, LDF signals, LDF signals reduced to samples acquired during ECG R peaks (LDF_R(ECG) signals) and HRV recorded simultaneously in healthy subjects are processed. Entropies of positive and negative increments for LDF signals show a nonmonotonic pattern: oscillations--more or less pronounced, depending on subjects--are found with a period matching the one of cardiac activity. However, such oscillations are not found with LDF_R(ECG) nor with HRV. Moreover, the asymmetry index for LDF is markedly different from the ones of LDF_R(ECG) and HRV. The cardiac activity may therefore play a dominant role in the time irreversibility properties of LDF signals.