Acta Physiol (Oxf). 2021 Apr 29:e13670. doi: 10.1111/apha.13670. Online ahead of print.
ABSTRACT
Physiological outputs are characterised by constant fluctuations, even under resting conditions.1 Quantifying this variability represents an important methodological challenge. Variability in physiological outputs has traditionally been quantified according to its magnitude, using measures such as the standard deviation (SD).2 Such magnitude-based measures have provided substantial insight into the analysis of physiological outputs, with changes in the magnitude of variability associated with adverse events in a number of systems.2 However, physiological outputs are characterised by irregular self-similar fluctuations (“complexity”) over multiple orders of temporal magnitude (i.e. seconds, minutes, hours), a property magnitude-based measures cannot quantify.1 Complexity measures derive from non-linear dynamics, and include metrics related to information theory (e.g. entropy statistics), which measure the apparent regularity or randomness of an output, and metrics drawn from fractal geometry, which identify long-range correlations present in an output.3 It has been suggested that neither magnitude- nor complexity-based metrics should be used as the sole indicator of system characteristics; rather, they should be used in conjunction, in order to provide a more complete understanding of variability.2,4.
PMID:33915024 | DOI:10.1111/apha.13670
