Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFE(σ)) and mean (RCMFE(μ)) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFE(σ) and RCMFE(μ), in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFE(σ) and RCMFE(μ) values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer’s disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFE(μ) cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFE(σ) may do so, and vice versa. The results showed that RCMFE(σ)-based features lead to higher classification accuracies in comparison with the RCMFE(μ)-based ones. We also made freely available all the Matlab codes used in this study at 10.7488/ds/1477.