  Multivariate entropy quantification algorithms are becoming a prominent tool
for the extraction of information from multi-channel physiological time-series.
However, in the analysis of physiological signals from heterogeneous organ
systems, certain channels may overshadow the patterns of others, resulting in
information loss. Here, we introduce the framework of Stratified Entropy to
prioritize each channels' dynamics based on their allocation to respective
strata, leading to a richer description of the multi-channel time-series. As an
implementation of the framework, three algorithmic variations of the Stratified
Multivariate Multiscale Dispersion Entropy are introduced. These variations and
the original algorithm are applied to synthetic time-series, waveform
physiological time-series, and derivative physiological data. Based on the
synthetic time-series experiments, the variations successfully prioritize
channels following their strata allocation while maintaining the low
computation time of the original algorithm. In experiments on waveform
physiological time-series and derivative physiological data, increased
discrimination capacity was noted for multiple strata allocations in the
variations when benchmarked to the original algorithm. This suggests improved
physiological state monitoring by the variations. Furthermore, our variations
can be modified to utilize a priori knowledge for the stratification of
channels. Thus, our research provides a novel approach for the extraction of
previously inaccessible information from multi-channel time series acquired
from heterogeneous systems.
