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Cardiovascular regulation: a modelling approach.

A Asteroth1, K Moller, F Baisch

  • 1Bonn University, Department of Computer Science I, FRG. aster@cs.uni-bonn.de

Journal of Gravitational Physiology : a Journal of the International Society for Gravitational Physiology
|July 1, 1997
PubMed
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Artificial neural networks (ANNs) model autonomic cardiovascular control (ACVC) to understand postflight orthostatic hypotension. This research approximates human data, aiding space research into ACVC mechanisms.

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Space Medicine

Background:

  • Postflight orthostatic hypotension is a significant concern for astronauts.
  • Autonomic cardiovascular control (ACVC) mechanisms are not fully understood, especially in microgravity.
  • Direct measurement of the central ACVC substrate is challenging in space research.

Purpose of the Study:

  • To evaluate artificial neural networks (ANNs) as a model for the central substrate of ACVC.
  • To investigate the involvement of ACVC in postflight orthostatic hypotension.
  • To refine modeling techniques for physiological data analysis in spaceflight.

Main Methods:

  • Utilizing artificial neural networks (ANNs) to model ACVC.
  • Conducting experiments with simulated tilt test data to optimize ANN input coding and architecture.

Related Experiment Videos

  • Approximating physiological data from human subjects during pre-mission preparations for spaceflights (MIR'97 and D-2).

    • Main Results:

    • Demonstrated the suitability of ANNs for modeling complex physiological control systems.
    • Identified key parameters for ANN training related to tilt test data.
    • Successfully approximated human physiological data using the developed ANN models.

    Conclusions:

    • ANNs are a valuable tool for modeling the central substrate of ACVC.
    • This modeling approach can enhance understanding of postflight orthostatic hypotension.
    • The findings support the use of computational models in space physiology research.