Key result
Refined Generalized Multivariate Multiscale Fuzzy Entropy (ReGeM-MFE) consistently outperformed Refined Composite Multivariate Multiscale Fuzzy Entropy in discerning resting and stand-up conditions.
The proposed ReGeM-MFE metric provides an improved computational method for analyzing multivariate physiological complexity, such as heart rate and blood pressure variability, during postural changes.
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ReGeM-MFE may refine postural autonomic analysis in research; leaves open clinical validation before any practice change.
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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Nardelli et al. (2018) studied Autonomic Nervous System (ANS) dynamics during postural changes. Refined Generalized Multivariate Multiscale Fuzzy Entropy (ReGeM-MFE) vs. Refined Composite Multivariate Multiscale Fuzzy Entropy was evaluated on Discerning different levels of complexity in ANS dynamics during active stand-up. Refined Generalized Multivariate Multiscale Fuzzy Entropy (ReGeM-MFE) consistently outperformed Refined Composite Multivariate Multiscale Fuzzy Entropy in discerning resting and stand-up conditions.
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