Yuan H, Westwick D T, Ingenito E P, Lutchen K R, Suki B
Department of Biomedical Engineering, Boston University, MA 02215, USA.
Ann Biomed Eng. 1999 Jul-Aug;27(4):548-62. doi: 10.1114/1.217.
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.
肺实质是一种柔软的生物材料，由许多相互作用的成分组成，如间质细胞、细胞外胶原 - 弹性纤维网络和蛋白聚糖基质。这种精细结构的力学行为很复杂，表现出几种轻微但不同类型的非线性以及一种以幂律函数为特征的类分形长记忆应力松弛。为了在存在这种长记忆的情况下表征组织非线性，我们研究了几种非线性系统识别技术对从具有各种输入波形的肺组织条带获得的应力 - 应变数据的鲁棒性和预测能力。我们发现，一般来说，对于具有长记忆的轻度非线性系统，频域中的非参数非线性系统识别比时域技术更受青睐。更重要的是，如果有一个合适的参数非线性模型，仅用几个参数就能捕捉系统的长记忆，那么就可以实现具有显著提高的鲁棒性的高预测能力。结果表明，应力 - 应变关系的一阶核与分形型长记忆应力松弛一致，并且非线性可以描述为模拟潮式呼吸的位移的维纳型非线性结构。
