Work

Modeling Complexity of Physiological Time Series In-silico

Year: 2010

Type: conference-paper

Abstract: A free-running physiological system produces time series with complexity which has been correlated to the robustness and health of the system. The essential tool to study the link between the structure of the system and the complexity of the series it produces is a mathematical model that is capable of reproducing the statistical signatures of a physiological time series. We construct a model based on the neural structure of the hippocampus that reproduces detrended fluctuations and multiscale entropy complexity signatures of physiological time series. We study the dependence of these signatures on the length of the series and on the initial data.

Source: International Conference on Bio-inspired Systems and Signal Processing

DOI: 10.5220/0002710300610067

Authors: Jesse Berwald, Tomáš Gedeon, Konstantin Mischaikow

Institutions: Montana State University, Rutgers, The State University of New Jersey

Language: English

Topic: Complex Systems and Time Series Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W80730978

FWCI:

Cites: 10

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Related to: 10