Fractal physiology explained

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]

Main Findings

Neurophysiology

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.

Cardiovascular systems

The complexity of Heart Rate Variability is a useful predictor of cardiovascular health.[5]

Software

In Python, NeuroKit provides a comprehensive set of functions for complexity analysis of physiological data.[6] 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]

In MATLAB, The Neurophysiological Biomarker Toolbox (NBT) allows the computation of Detrended fluctuation analysis. EZ Entropy implements the entropy analysis of physiological time-series.[10]

See also

References

  1. Book: Bassingthwaighte . James B. . Fractal physiology . 1994 . Published for the American Physiological Society by Oxford University Press . New York . 0195080130.
  2. an der Heiden . U. . Schizophrenia as a Dynamical Disease . Pharmacopsychiatry . February 2006 . 39 . 36–42 . 10.1055/s-2006-931487. 16508894 .
  3. Tretter . F. . Gebicke-Haerter . P. J. . an der Heiden . U. . Rujescu . D. . Mewes . H. W. . Turck . C. W. . Affective Disorders as Complex Dynamic Diseases – a Perspective from Systems Biology . Pharmacopsychiatry . May 2011 . 44 . S 01 . S2–S8 . 10.1055/s-0031-1275278. 21544742 .
  4. Smits . Fenne Margreeth . Porcaro . Camillo . Cottone . Carlo . Cancelli . Andrea . Rossini . Paolo Maria . Tecchio . Franca . Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer's Disease . PLOS ONE . 12 February 2016 . 11 . 2 . e0149587 . 10.1371/journal.pone.0149587. 26872349 . 4752290 . 2016PLoSO..1149587S . free .
  5. Pham . Tam . Lau . Zen Juen . Chen . S. H. Annabel . Makowski . Dominique . Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial . Sensors . 9 June 2021 . 21 . 12 . 3998 . 10.3390/s21123998. 34207927 . 8230044 . 2021Senso..21.3998P . free .
  6. Makowski . Dominique . Pham . Tam . Lau . Zen J. . Brammer . Jan C. . Lespinasse . François . Pham . Hung . Schölzel . Christopher . Chen . S. H. Annabel . NeuroKit2: A Python toolbox for neurophysiological signal processing . Behavior Research Methods . August 2021 . 53 . 4 . 1689–1696 . 10.3758/s13428-020-01516-y. 33528817 . 231757711 . free .
  7. Web site: Vallat . Raphael . raphaelvallat/antropy . github.com . 22 March 2022 . 22 March 2022.
  8. Web site: Tomcala . Jiri . TSEntropies . CRAN . 22 March 2022 . 8 October 2018.
  9. Web site: Hasselman . Fred . casnet . github.com . 22 March 2022 . 6 March 2022.
  10. Li . Peng . EZ Entropy: a software application for the entropy analysis of physiological time-series . BioMedical Engineering OnLine . December 2019 . 18 . 1 . 30 . 10.1186/s12938-019-0650-5. 30894180 . 6425722 . free .

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