Parametric and Nonparametric Nonlinear System Identification of Lung Tissue Strip Mechanics View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1999-07

AUTHORS

Huichin Yuan, David T. Westwick, Edward P. Ingenito, Kenneth R. Lutchen, Béla Suki

ABSTRACT

Lung parenchyma is a soft biological material composed of many interacting elements such as the interstitial cells, extracellular collagen-elastin fiber network, and proteoglycan ground substance. The mechanical behavior of this delicate structure is complex showing several mild but distinct types of nonlinearities and a fractal-like long memory stress relaxation characterized by a power-law function. To characterize tissue nonlinearity in the presence of such long memory, we investigated the robustness and predictive ability of several nonlinear system identification techniques on stress-strain data obtained from lung tissue strips with various input wave forms. We found that in general, for a mildly nonlinear system with long memory, a nonparametric nonlinear system identification in the frequency domain is preferred over time-domain techniques. More importantly, if a suitable parametric nonlinear model is available that captures the long memory of the system with only a few parameters, high predictive ability with substantially increased robustness can be achieved. The results provide evidence that the first-order kernel of the stress-strain relationship is consistent with a fractal-type long memory stress relaxation and the nonlinearity can be described as a Wiener-type nonlinear structure for displacements mimicking tidal breathing. More... »

PAGES

548-562

Identifiers

URI

http://scigraph.springernature.com/pub.10.1114/1.217

DOI

http://dx.doi.org/10.1114/1.217

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015267497

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10468239


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