Key result
A novel method mapping the coupling strength of multiple signals into a single time series using Voronoi decomposition of the copula domain yielded results consistent with classical studies in rats.
Why the study?
Multiscale entropy is unable to jointly analyze more than two signals.
A novel mathematical method using Voronoi decomposition successfully maps the coupling strength of multiple physiological signals into a single time series in a rat model.
Enables joint multiscale coupling analysis of >2 signals; leaves open validation in cardiovascular datasets.
This paper proposes a method that maps the coupling strength of an arbitrary number of signals D, D ≥ 2, into a single time series. It is motivated by the inability of multiscale entropy to jointly analyze more than two signals. The coupling strength is determined using the copula density defined over a [0 1]D copula domain. The copula domain is decomposed into the Voronoi regions, with volumes inversely proportional to the dependency level (coupling strength) of the observed joint signals. A stream of dependency levels, ordered in time, creates a new time series that shows the fluctuation of the signals’ coupling strength along the time axis. The composite multiscale entropy (CMSE) is then applied to three signals, systolic blood pressure (SBP), pulse interval (PI), and body temperature (tB), simultaneously recorded from rats exposed to different ambient temperatures (tA). The obtained results are consistent with the results from the classical studies, and the method itself offers more levels of freedom than the classical analysis.
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Bajić et al. (2019) studied this question. Voronoi decomposition of copula domain for composite multiscale entropy vs. Classical analysis was evaluated on Fluctuation of signals' coupling strength along the time axis. A novel method mapping the coupling strength of multiple signals into a single time series using Voronoi decomposition of the copula domain yielded results consistent with classical studies in rats.
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