Fractal physiology

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][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]

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. Bassingthwaighte, James B. (1994). Fractal physiology. New York: Published for the American Physiological Society by Oxford University Press. ISBN 0195080130.
  2. an der Heiden, U. (February 2006). "Schizophrenia as a Dynamical Disease". Pharmacopsychiatry. 39: 36–42. doi:10.1055/s-2006-931487. PMID 16508894.
  3. 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. PMID 21544742.
  4. Smits, Fenne Margreeth; Porcaro, Camillo; Cottone, Carlo; Cancelli, Andrea; Rossini, Paolo Maria; Tecchio, Franca (12 February 2016). "Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer's Disease". PLOS ONE. 11 (2): e0149587. Bibcode:2016PLoSO..1149587S. doi:10.1371/journal.pone.0149587. PMC 4752290. PMID 26872349.
  5. Pham, Tam; Lau, Zen Juen; Chen, S. H. Annabel; Makowski, Dominique (9 June 2021). "Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial". Sensors. 21 (12): 3998. Bibcode:2021Senso..21.3998P. doi:10.3390/s21123998. PMC 8230044. PMID 34207927.
  6. Makowski, Dominique; Pham, Tam; Lau, Zen J.; Brammer, Jan C.; Lespinasse, François; Pham, Hung; Schölzel, Christopher; Chen, S. H. Annabel (August 2021). "NeuroKit2: A Python toolbox for neurophysiological signal processing". Behavior Research Methods. 53 (4): 1689–1696. doi:10.3758/s13428-020-01516-y. PMID 33528817. S2CID 231757711.
  7. Vallat, Raphael (22 March 2022). "raphaelvallat/antropy". github.com. Retrieved 22 March 2022.
  8. Tomcala, Jiri (8 October 2018). "TSEntropies". CRAN. Retrieved 22 March 2022.
  9. Hasselman, Fred (6 March 2022). "casnet". github.com. Retrieved 22 March 2022.
  10. Li, Peng (December 2019). "EZ Entropy: a software application for the entropy analysis of physiological time-series". BioMedical Engineering OnLine. 18 (1): 30. doi:10.1186/s12938-019-0650-5. PMC 6425722. PMID 30894180.


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