Paper Info
Title
Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems
Abstract
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.
Year
DOI
Venue
2022
10.3390/e24010026
ENTROPY
Keywords
DocType
Volume
variational embedding entropy, sample entropy, multi-channel system, physical signal analysis, complexity science, multivariate data
Journal
24
Issue
ISSN
Citations 
1
1099-4300
1
PageRank 
References 
Authors
0.40
18
2
Name
Order
Citations
PageRank
Hongjian Xiao110.73
Danilo Mandic21641173.32