Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEµ). We investigate the behaviour of these multivariate methods on multichannel white Gaussian and 1/f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEµ lead to more stable results and are less sensitive to the signals’ length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offer complexity profiles with complementary information for biomedical signal analysis. We made freely available all the Matlab codes used in this study, including mvSE, mvFE, mvMSEµ, RCmvMSEµ, mvMFEµ, RCmvMFEµ, mvMSEσ2, RCmvMSEσ2, mvMFEσ2 and RCmvMFEσ2.
Azami, Hamed; Escudero, Javier. (2016). Matlab codes for "Refined Composite Multivariate Generalized Multiscale Fuzzy Entropy: A Tool for Complexity Analysis of Multichannel Signals", [software]. University of Edinburgh, School of Engineering, Institute for Digital Communications. http://dx.doi.org/10.7488/ds/1432