We propose refined composite multivariate multiscale fuzzy entropy (RCmvMFE).

The coarse-graining step of RCmvMFE uses variance (RCmvMFE_σ²${}_{{\sigma }^2}$) or mean (RCmvMFEaf4a0d15d0" title="Click to view the MathML source">_μ${}_{\mu }$).

The introduced fuzzy membership function significantly decreases the running time.

Our simulations demonstrate that RCmvMFE_σ²${}_{{\sigma }^2}$ and RCmvMFEaf4a0d15d0" title="Click to view the MathML source">_μ${}_{\mu }$ lead to more stable results.

RCmvMFE_σ²${}_{{\sigma }^2}$ and RCmvMFEaf4a0d15d0" title="Click to view the MathML source">_μ${}_{\mu }$ are less sensitive to the length of signals.