Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy

Herbert F. Jelinek; Rohisha Tuladhar; Garland Culbreth; Gyanendra Bohara; David Cornforth; Bruce. J. West; Paolo Grigolini

doi:10.3389/fphys.2020.607324

journal article Open Access Jan 14, 2021

Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy

Herbert F. Jelinek

Rohisha Tuladhar Garland Culbreth Gyanendra Bohara David Cornforth Bruce. J. West Paolo Grigolini

Frontiers in Physiology Vol. 11 · Frontiers Media SA

View at Publisher Save 10.3389/fphys.2020.607324

Abstract

We review the literature to argue the importance of the occurrence of crucial events in the dynamics of physiological processes. Crucial events are interpreted as short time intervals of turbulence, and the time distance between two consecutive crucial events is a waiting time distribution density with an inverse power law (IPL) index μ, with μ &lt; 3 generating non-stationary behavior. The non-stationary condition is characterized by two regimes of the IPL index: (a) perennial non-stationarity, with 1 &lt; μ &lt; 2 and (b) slow evolution toward the stationary regime, with 2 &lt; μ &lt; 3. Human heartbeats and brain dynamics belong to the latter regime, with healthy physiological processes tending to be closer to the border with the perennial non-stationary regime with μ = 2. The complexity of cognitive tasks is associated with the mental effort required to address a difficult task, which leads to an increase of μ with increasing task difficulty. On this basis we explore the conjecture that disease evolution leads the IPL index μ moving from the healthy condition μ = 2 toward the border with Gaussian statistics with μ = 3, as the disease progresses. Examining heart rate time series of patients affected by diabetes-induced autonomic neuropathy of varying severity, we find that the progression of cardiac autonomic neuropathy (CAN) indeed shifts μ from the border with perennial variability, μ = 2, to the border with Gaussian statistics, μ = 3 and provides a novel, sensitive index for assessing disease progression. We find that at the Gaussian border, the dynamical complexity of crucial events is replaced by Gaussian fluctuation with long-time memory.

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Details

Published: Jan 14, 2021
Vol/Issue: 11
License: View

Authors

H

Herbert F. Jelinek

Biomedical Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates

Medical Artificial Intelligence and Automation (MAIA) Laboratory Department of Radiation Oncology UT Southwestern Medical Center Dallas TX 75390 USA

Funding

Army Research Office Award: W911NF1901

Cite This Article

Herbert F. Jelinek, Rohisha Tuladhar, Garland Culbreth, et al. (2021). Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy. Frontiers in Physiology, 11. https://doi.org/10.3389/fphys.2020.607324

Diffusion Entropy vs. Multiscale and Rényi Entropy to Detect Progression of Autonomic Neuropathy

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