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Related Concept Videos

Regulation of Heart Rates01:31

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The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
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Related Experiment Video

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Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Modeling heart rate regulation--part II: parameter identification and analysis.

K R Fowler1, G A Gray, M S Olufsen

  • 1Department of Mathematics, Clarkson University, Potsdam, NY, USA.

Cardiovascular Engineering (Dordrecht, Netherlands)
|January 4, 2008
PubMed
Summary

This study compares optimization methods for a heart rate regulation model. Genetic algorithms and implicit filtering offer effective parameter estimation for predicting heart rate changes during postural transitions.

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Area of Science:

  • Physiology
  • Computational Biology
  • Biomedical Engineering

Background:

  • A 17-parameter model was previously developed to predict heart rate regulation during postural changes (sitting to standing).
  • Parameter estimation in Part I and prior work utilized the Nelder-Mead method to minimize least squares error.

Purpose of the Study:

  • To compare the Nelder-Mead optimization method with two sampling methods: implicit filtering and a genetic algorithm.
  • To assess the suitability of off-the-shelf optimization methods for the heart rate model.
  • To analyze the model's properties using data generated during optimization.

Main Methods:

  • Comparison of Nelder-Mead, implicit filtering, and genetic algorithm optimization techniques.
  • Application of these methods to estimate 17 parameters of a heart rate regulation model.
  • Analysis of the least-squares error landscape generated by the optimization process.

Main Results:

  • Implicit filtering and genetic algorithms provide reasonable parameter estimates with minimal tuning.
  • These sampling methods effectively work with the existing heart rate model.
  • The least-squares problem exhibits multiple local minima and significant error variation due to parameter interactions.

Conclusions:

  • Off-the-shelf sampling optimization methods are viable alternatives to Nelder-Mead for this heart rate model.
  • The model's parameter space is complex, with potential for multiple solutions and sensitivity to parameter interactions.
  • Further analysis of the model's robustness and parameter identifiability is warranted.