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Fractal physiology refers to the study of physiological systems using complexity science methods, such as chaos measure, entropy, and fractal dimensions. The underlying assumption is that biological systems are complex and exhibit non-linear patterns of activity, and that characterizing that complexity (using dedicated mathematical approaches) is useful to understand, and make inferences and predictions about the system.[1]
Quantifications of the complexity of brain activity is used in the context of neuropsychiatric diseases and mental states characterization, such as schizophrenia,[2] affective disorders,[3] or neurodegenerative disorders.[4] Particularly, diminished EEG complexity is typically associated with increased symptomatology.
In Python, NeuroKit provides a comprehensive set of functions for complexity analysis of physiological data.[6][5]AntroPy implements several measures to quantify the complexity of time-series.[7]
In R, TSEntropies provides methods to quantify the entropy.[8]casnet implements a collection of analytic tools for studying signals recorded from complex adaptive systems.[9]
^Bassingthwaighte, James B. (1994). Fractal physiology. New York: Published for the American Physiological Society by Oxford University Press. ISBN0-19-508013-0.
^Tretter, F.; Gebicke-Haerter, P. J.; an der Heiden, U.; Rujescu, D.; Mewes, H. W.; Turck, C. W. (May 2011). "Affective Disorders as Complex Dynamic Diseases – a Perspective from Systems Biology". Pharmacopsychiatry. 44 (S 01): S2 –S8. doi:10.1055/s-0031-1275278. PMID21544742.