Hybrid physiological modeling of subjects undergoing cyclic physical loading

A. Nassef, M. Mahfouf, C. H. Ting, E. Elsamahy, D. A. Linkens, M. Denai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper investigates the influence of physical stress on the physiological parameters of the cardiovascular system (CVS). The work aims at estimating the physiological variables such as the Heart Rate (HR), Blood Pressure (BP), Total Peripheral Resistance (TPR) and respiration in a subject underging physical workload. The core of the model was based on the model architecture previously developed by Luczak and his co-workers. Luczak's model was first reconstructed and the original published figure plots were used to identify some of the missing parameters via Genetic Algorithms (GA). The model was then modified using real experimental data extracted from healthy subjects who underwent two-session experiments of cyclic-loading based physical stress. Neuro-Fuzzy models were elicited via the data in order to describe the non-linear components of the model. The model response has also been significantly improved by including a dynamics-based component represented by 'time' as an extra input. The final model, as well as being of a 'hybrid' nature, was found to generalize better, to be more amenable to expansions and to also lead to better predictions.

Original languageEnglish
Title of host publicationBIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing
Pages252-257
Number of pages6
StatePublished - 2010
Externally publishedYes
Event3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 - Valencia, Spain
Duration: 20 Jan 201023 Jan 2010

Publication series

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

Conference

Conference3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010
Country/TerritorySpain
CityValencia
Period20/01/1023/01/10

Keywords

  • Biomedical systems
  • Fuzzy systems
  • Genetic algorithms
  • Modeling
  • Signal processing

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