Refined Generalized Multivariate Multiscale Fuzzy Entropy: A Preliminary Study on Multichannel Physiological Complexity During Postural Changes

We propose a novel approach to characterize the complexity of multivariate physiological processes over multiple time scales, which hereinafter we call Refined Generalized Multivariate Multiscale Fuzzy Entropy (ReGeM-MFE). In this preliminary study, we evaluate the effectiveness of this methodology in discerning different levels of complexity in Autonomic Nervous System (ANS) dynamics during active stand-up, considering a bivariate process comprising heart rate variability and blood pressure variability series. Results show that, using mean-and variance-based ReGeM-MFE throughout different coarse-graining steps, it is possible to statistically discern the resting and stand-up conditions. Compared with the previously proposed Refined Composite Multivariate Multiscale Fuzzy Entropy, we demonstrate that the proposed ReGeM-MFE consistently out-performs this metrics.

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