Factors Affecting Volterra Kernel Estimation: Emphasis on Lung Tissue Viscoelasticity View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-01

AUTHORS

Qin Zhang, Béla Suki, David T. Westwick, Kenneth R. Lutchen

ABSTRACT

The goal of this study is to quantitatively investigate how the memory length, order of nonlinearity, type of input, and measurement noise can affect the identification of the Volterra kernels of a nonlinear viscoelastic system, and hence the inference on system structure. We explored these aspects with emphasis on nonlinear lung tissue mechanics around breathing frequencies, where the memory length issue can be critical and a ventilatory input is clinically demanded. We adopted and examined Korenberg's fast orthogonal algorithm since it is a least-squares technique that does not demand white Gaussian noise input and makes no presumptions on the kernel shape and system structure. We then propose a memory autosearch method, which incorporates Akaike's final production error criterion into Korenberg's fast orthogonal algorithm to identify the memory length simultaneously with the kernels. Finally, we designed a special ventilatory flow input and evaluated its potential for the kernel identification of the nonlinear systems requiring oscillatory forcing. We found that the long memory associated with soft tissue viscoelasticity may prohibit correct identification of the higher-order kernels of the lung. However, the key characteristics of the first-order kernel may be revealed through averaging over multiple experiments and estimations. More... »

PAGES

103-116

References to SciGraph publications

  • 1969-12. Fitting autoregressive models for prediction in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • 1996-05. Detection of nonlinear dynamics in short, noisy time series in NATURE
  • 1991-07. Interpretation of functional series expansions in ANNALS OF BIOMEDICAL ENGINEERING
  • 1991-07. The interpretation of kernels — An overview in ANNALS OF BIOMEDICAL ENGINEERING
  • 1989. Wiener Analysis of the Hodgkin-Huxley Equations in ADVANCED METHODS OF PHYSIOLOGICAL SYSTEM MODELING
  • 1987-12. The fractal dimension of a test signal: Implications for system identification procedures in BIOLOGICAL CYBERNETICS
  • 1991-07. Parallel cascade identification and kernel estimation for nonlinear systems in ANNALS OF BIOMEDICAL ENGINEERING
  • 1994. Identification of Multiple-Input Nonlinear Systems Using Non-White Test Signals in ADVANCED METHODS OF PHYSIOLOGICAL SYSTEM MODELING
  • 1996-03. The identification of nonlinear biological systems: Volterra kernel approaches in ANNALS OF BIOMEDICAL ENGINEERING
  • 1993-11. Identification of nonlinear biological systems using laguerre expansions of kernels in ANNALS OF BIOMEDICAL ENGINEERING
  • 1989. Fast Orthogonal Algorithms for Nonlinear System Identification and Time-Series Analysis in ADVANCED METHODS OF PHYSIOLOGICAL SYSTEM MODELING
  • 1995-10. Nonlinear neuronal mode analysis of action potential encoding in the cockroach tactile spine neuron in BIOLOGICAL CYBERNETICS
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    http://scigraph.springernature.com/pub.10.1114/1.82

    DOI

    http://dx.doi.org/10.1114/1.82

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/10355555


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