  To quantify the complexity of a system, entropy-based methods have received
considerable critical attentions in real-world data analysis. Among numerous
entropy algorithms, amplitude-based formulas, represented by Sample Entropy,
suffer from a limitation of data length especially when it comes to practical
scenarios. And this shortcoming is further highlighted by involving coarse
graining procedure in multi-scale process. The unbalance between embedding
dimension and data size will undoubtedly result in inaccurate and undefined
estimation. To that cause, Variational Embedding Multiscale Sample Entropy is
proposed in this paper, which assigns signals from various channels with
distinct embedding dimensions. And this algorithm is tested by both stimulated
and real signals. Furthermore, the performance of the new entropy is
investigated and compared with Multivariate Multiscale Sample Entropy and
Variational Embedding Multiscale Diversity Entropy. Two real-world database,
wind data sets with varying regimes and physiological database recorded from
young and elderly people, were utilized. As a result, the proposed algorithm
gives an improved separation for both situations.
