4.6 Article

Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems

期刊

ENTROPY
卷 24, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/e24010026

关键词

variational embedding entropy; sample entropy; multi-channel system; physical signal analysis; complexity science; multivariate data

向作者/读者索取更多资源

Entropy-based methods are important in quantifying the complexity of real-world systems. We propose a new method called veMSE, which can robustly evaluate structural complexity even with shorter data. veMSE also exhibits desirable properties such as robustness to embedding dimension and noise resilience.
Entropy-based methods have received considerable attention in the quantification of structural complexity of real-world systems. Among numerous empirical entropy algorithms, conditional entropy-based methods such as sample entropy, which are associated with amplitude distance calculation, are quite intuitive to interpret but require excessive data lengths for meaningful evaluation at large scales. To address this issue, we propose the variational embedding multiscale sample entropy (veMSE) method and conclusively demonstrate its ability to operate robustly, even with several times shorter data than the existing conditional entropy-based methods. The analysis reveals that veMSE also exhibits other desirable properties, such as the robustness to the variation in embedding dimension and noise resilience. For rigor, unlike the existing multivariate methods, the proposed veMSE assigns a different embedding dimension to every data channel, which makes its operation independent of channel permutation. The veMSE is tested on both stimulated and real world signals, and its performance is evaluated against the existing multivariate multiscale sample entropy methods. The proposed veMSE is also shown to exhibit computational advantages over the existing amplitude distance-based entropy methods.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据