 Multiscale entropy, MSE, is a technique used to measure the complexity of a time series by quantifying its entropy over a range of temporal scales. It has been widely used in various fields such as neuroscience, finance, and engineering. Over the years, several modifications and refinements have been proposed to increase the accuracy of the entropy estimates, or explore alternative course-graining procedures. This paper reviews these developments and provides a comprehensive summary of the current state of the art. This article was authored by Inhumohurdier.