English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Objective: Due to the non-linearity of numerous biomedical signals,non-linear analysis of multi-channel time series, notably multivariatemultiscale entropy (mvMSE), has been extensively used in biomedical signalprocessing. However, mvMSE has three drawbacks: 1) mvMSE values are eitherundefined or unreliable for short signals; 2) mvMSE is not fast enough forreal-time applications; and 3) the computation of mvMSE for signals with alarge number of channels requires the storage of a huge number of elements.Methods: To deal with these problems and improve the stability of mvMSE, weintroduce multivariate multiscale dispersion entropy (MDE - mvMDE) as anextension of our recently developed MDE, to quantify the complexity ofmultivariate time series. Results: We assess mvMDE, in comparison with mvMSEand multivariate multiscale fuzzy entropy (mvMFE), on correlated anduncorrelated multi-channel noise signals, bivariate autoregressive processes,and three biomedical datasets. The results show that mvMDE takes into accountdependencies in patterns across both the time and spatial domains. The mvMDE,mvMSE, and mvMFE methods are consistent in that they lead to similarconclusions about the underlying physiological conditions. However, theproposed mvMDE discriminates various physiological states of the biomedicalrecordings better than mvMSE and mvMFE. In addition, for both the short andlong time series, the mvMDE-based results are noticeably more stable than themvMSE- and mvMFE-based ones. Conclusion: For short multivariate time series,mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE isnoticeably faster than mvMFE and mvMSE and also needs to store a considerablysmaller number of elements. Significance: Due to its ability to detectdifferent kinds of dynamics of multivariate signals, mvMDE has great potentialto analyse various physiological signals.

Multivariate Multiscale Dispersion Entropy of Biomedical Times Series