Modeling complexity of physiological time series in-silico

Jesse Berwald, Tomáš Gedeon, Konstantin Mischaikow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationBIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing
Pages61-67
Number of pages7
StatePublished - 2010
Event3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 - Valencia, Spain
Duration: Jan 20 2010Jan 23 2010

Publication series

NameBIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings

Other

Other3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010
Country/TerritorySpain
CityValencia
Period1/20/101/23/10

All Science Journal Classification (ASJC) codes

  • Signal Processing

Keywords

  • Complexity
  • Neural networks
  • Physiological time series

Fingerprint

Dive into the research topics of 'Modeling complexity of physiological time series in-silico'. Together they form a unique fingerprint.

Cite this