Parametric and nonparametric nonlinear system identification of lung tissue strip mechanics.
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.
Yuan
H
H
Department of Biomedical Engineering, Boston University, MA 02215, USA.
Westwick
D T
DT
Ingenito
E P
EP
Lutchen
K R
KR
Suki
B
B
D000465:Algorithms; D000818:Animals; D003094:Collagen / Q000502:physiology; D003198:Computer Simulation; D004549:Elastin / Q000502:physiology; D017709:Fractals; D006168:Guinea Pigs; D066298:In Vitro Techniques; D016014:Linear Models; D008168:Lung / Q000033:anatomy & histology / Q000502:physiology*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D013314:Stress, Mechanical
D003094:Collagen; D004549:Elastin