The Multiscale Entropy Algorithm and Its Variants: A Review <s> Short Time Multiscale Entropy <s> Using 1000 successive points of a pulse wave velocity (PWV) series, we previously distinguished healthy from diabetic subjects with multi-scale entropy (MSE) using a scale factor of 10. One major limitation is the long time for data acquisition (i.e., 20 min). This study aimed at validating the sensitivity of a novel method, short time MSE (sMSE) that utilized a substantially smaller sample size (i.e., 600 consecutive points), in differentiating the complexity of PWV signals both in simulation and in human subjects that were divided into four groups: healthy young (Group 1; n = 24) and middle-aged (Group 2; n = 30) subjects without known cardiovascular disease and middle-aged individuals with well-controlled (Group 3; n = 18) and poorly-controlled (Group 4; n = 22) diabetes mellitus type 2. The results demonstrated that although conventional MSE could differentiate the subjects using 1000 consecutive PWV series points, sensitivity was lost using only 600 points. Simulation study revealed consistent results. By contrast, the novel sMSE method produced significant differences in entropy in both simulation and testing subjects. In conclusion, this study demonstrated that using a novel sMSE approach for PWV analysis, the time for data acquisition can be substantially reduced to that required for 600 cardiac cycles (~10 min) with remarkable preservation of sensitivity in differentiating among healthy, aged, and diabetic populations. <s> BIB001 </s> The Multiscale Entropy Algorithm and Its Variants: A Review <s> Short Time Multiscale Entropy <s> Laser speckle contrast imaging (LSCI) enables a noninvasive monitoring of microvascular perfusion. Some studies have proposed to extract information from LSCI data through their multiscale entropy (MSE). However, for reaching a large range of scales, the original MSE algorithm may require long recordings for reliability. Recently, a novel approach to compute MSE with shorter data sets has been proposed: the short-time MSE (sMSE). Our goal is to apply, for the first time, the sMSE algorithm in LSCI data and to compare results with those given by the original MSE. Moreover, we apply the original MSE algorithm on data of different lengths and compare results with those given by longer recordings. For this purpose, synthetic signals and 192 LSCI regions of interest (ROIs) of different sizes are processed. Our results show that the sMSE algorithm is valid to compute the MSE of LSCI data. Moreover, with time series shorter than those initially proposed, the sMSE and original MSE algorithms give results with no statistical difference from those of the original MSE algorithm with longer data sets. The minimal acceptable length depends on the ROI size. Comparisons of MSE from healthy and pathological subjects can be performed with shorter data sets than those proposed until now. <s> BIB002
In 2014, Chang et al. reported another algorithm, the short time MSE (sMSE), in order to be able to process data recorded during a short time BIB001 . Thus, for a discrete signal x = {x 1 , . . . , x i , . . . , x N }, the sMSE algorithm has the following steps BIB001 (1) construction of the coarse-grained time series y (p)(τ ) with 0 ≤ p ≤ τ − 1 as (2) the τ y (p)(τ) time series are subjected to sample entropy computation and averaged, giving sMSE of scale factor τ sM SE(x, τ, m, r) = 1 τ where S E (y (p)(τ) , m, r) corresponds to the sample entropy for the time series y (p)(τ) . The authors applied the sMSE algorithm on white and 1/f noises BIB001 . They noticed that, for white noise, MSE exhibits fluctuations whereas sMSE shows a relatively steady decrease BIB001 . Moreover, sMSE has also been applied on experimental data BIB001 BIB002 . Let us note that sMSE and CMSE present the same steps.