摘要: Comment [1] raise a point that was not discussed in our paper [2] due to page limitations. The multiscale entropy (MSE)[2] method is aimed at evaluating the complexity of time series. It is based on the analysis of the entropy values assigned not only to the original time series but also to coarse-grained time series, each of which represents the system’s dynamics on a different scale. The entropy assigned to a time series takes into account both its standard deviation (SD) and its correlation properties. However, there is no universal relationship between entropy and SD. Signals with higher variance may or may not have higher entropy, depending on the correlation properties. We illustrate both cases:(1) Consider stochastic variables that are completely independent (without correlations). The variables with larger variances will have higher entropy.(2) Consider a periodic signal with variance jxj and a random signal …