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Related Experiment Videos

Factors affecting Volterra kernel estimation: emphasis on lung tissue viscoelasticity.

Q Zhang1, B Suki, D T Westwick

  • 1Department of Biomedical Engineering, Boston University, MA 02215, USA.

Annals of Biomedical Engineering
|June 4, 1999
PubMed
Summary

Identifying Volterra kernels in nonlinear viscoelastic systems, like lung tissue, is challenging due to long memory effects. Averaging experiments can help reveal key first-order kernel characteristics.

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Area of Science:

  • Biomedical Engineering
  • Nonlinear Dynamics
  • Systems Biology

Background:

  • Nonlinear viscoelastic systems, such as lung tissue, exhibit complex behaviors.
  • Accurate identification of system parameters is crucial for understanding and modeling these systems.
  • Traditional methods often struggle with long memory effects and non-Gaussian inputs.

Purpose of the Study:

  • To quantitatively assess the impact of memory length, nonlinearity order, input type, and noise on Volterra kernel identification.
  • To investigate the inference of system structure from identified kernels.
  • To explore challenges in modeling nonlinear lung tissue mechanics.

Main Methods:

  • Utilized Korenberg's fast orthogonal algorithm for Volterra kernel identification.

Related Experiment Videos

  • Developed a memory autosearch method incorporating Akaike's final prediction error.
  • Designed a specific ventilatory flow input for oscillatory systems.

    • Main Results:

    • Long memory in soft tissue viscoelasticity hinders the identification of higher-order Volterra kernels.
    • The proposed memory autosearch method aids in simultaneous kernel and memory length identification.
    • Averaging multiple experimental estimations can improve the reveal of first-order kernel characteristics.

    Conclusions:

    • Accurate Volterra kernel identification in nonlinear viscoelastic systems is sensitive to memory length and input characteristics.
    • The developed methods offer potential for improved system identification in complex biological tissues.
    • Further research is needed to fully address challenges posed by long memory in biological systems.