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 fib er network, and proteoglycan ground substance. The mechanical behavior of t his delicate structure is complex showing several mild but distinct types o f nonlinearities and a fractal-like long memory stress relaxation character ized by a power-law function. To characterize tissue nonlinearity in the pr esence of such long memory, we investigated the robustness and predictive a bility of several nonlinear system identification techniques on stress-stra in 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 no nparametric nonlinear system identification in the frequency domain is pref erred over time-domain techniques. More importantly, if a suitable parametr ic nonlinear model is available that captures the long memory of the system with only a few parameters, high predictive ability with substantially inc reased robustness can be achieved. The results provide evidence that the fi rst-order kernel of the stress-strain relationship is consistent with a fra ctal-type long memory stress relaxation and the nonlinearity can be describ ed as a Wiener-type nonlinear structure for displacements mimicking tidal b reathing. (C) 1999 Biomedical Engineering Society. [S0090-6964(99)00804-8].