Factors affecting Volterra kernel estimation: emphasis on lung tissue viscoelasticity.
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.
Zhang
Q
Q
Department of Biomedical Engineering, Boston University, MA 02215, USA.
Suki
B
B
Westwick
D T
DT
Lutchen
K R
KR
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D015982:Bias; D004285:Dogs; D004548:Elasticity; D016018:Least-Squares Analysis; D008170:Lung Compliance / Q000502:physiology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D015656:Respiratory Mechanics / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric; D014783:Viscosity