Biomedical subjects

B Suki

Publications and source records attributed to B Suki.

77 records · Page 5Linked to original sources

PubMedDate not supplied

Mathematical modeling of the first inflation of degassed lungs.

The pressure-volume (P-V) relationship of degassed lungs during the first inflation is different from that in consecutive inflations. We developed a mathematical model of the P-V curve of the first inflation by assuming that (1) central airways are open leading to many subtrees of n generations that are initially closed; (2) an airway opens when inflation pressure reaches the opening threshold pressure of that segment; and (3) the opening threshold pressures do not depend on airway generation. In this model, airway opening occurs in cascades or avalanches. To test the model which contains only two parameters, n and a pressure, P(low), at which at least one subtree completely opens, we measured the first inflation P-V curves of 15 excised and degassed rabbit lungs. By fitting these data, we found that n=17+/-5, P(low)=23+/-4 cmH2O, and that there is a wide distribution of threshold pressures for airways with diameters <2 mm. Analysis of the P-V curve in a lung which was lavaged with a liquid of constant surface tension and in which airways are presumably open demonstrated that the distribution of threshold pressures is narrow, and hence no avalanches occur during inflation. We conclude that in normal lungs the first inflation is dominated by avalanche behavior of airway opening providing information on the global distribution of threshold pressures and the average site of airway closure.

Animals↗

PubMedDate not supplied

A frequency domain approach to nonlinear and structure identification for long memory systems: application to lung mechanics.

From the input-output point of view, many nonlinear biological systems display long memory characteristics which can become a critical issue using nonparametric time-domain kernel identification due to inevitable truncation of memory length. To avoid these limitations, we present an alternative approach in the frequency domain with application to lung mechanics. Generally, if the system is excited with a periodic wave form, the response will approach a steady state which dominates the long memory transients. Thus, we hypothesized that the kernels at discrete frequencies will not be significantly affected by memory truncation. To test this, we extended the frequency kernel analysis of Victor and Shapley (Biophys. J. 29:459-484, 1980) to a nonwhite input spectrum and developed a new structure test in the frequency domain to differentiate between Wiener and Hammerstein models. These techniques were applied to measured pressure-flow data of isolated lung lobes. The results showed that (1) the important nonlinearities in the pressure-flow relation are of second order, (2) the frequency kernels of the lobes were similar for flat and ventilatory-like input spectra, and (3) the structure test strongly suggested that the pressure-flow relationship during tidal-like excursions is consistent with a Wiener structure.

Animals↗

PubMedDate not supplied

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.

Algorithms↗

PubMedDate not supplied

Sensitivity analysis of kernel estimates: implications in nonlinear physiological system identification.

Many techniques have been developed for the estimation of the Volterra/Wiener kernels of nonlinear systems, and have found extensive application in the study of various physiological systems. To date, however, we are not aware of methods for estimating the reliability of these kernels from single data records. In this study, we develop a formal analysis of variance for least-squares based nonlinear system identification algorithms. Expressions are developed for the variance of the estimated kernel coefficients and are used to place confidence bounds around both kernel estimates and output predictions. Specific bounds are developed for two such identification algorithms: Korenberg's fast orthogonal algorithm and the Laguerre expansion technique. Simulations, employing a model representative of the peripheral auditory system, are used to validate the theoretical derivations, and to explore their sensitivity to assumptions regarding the system and data. The simulations show excellent agreement between the variances of kernel coefficients and output predictions as estimated from the results of a single trial compared to the same quantities computed from an ensemble of 1000 Monte Carlo runs. These techniques were validated with white and nonwhite Gaussian inputs and with white Gaussian and nonwhite non-Gaussian measurement noise on the output, provided that the output noise source was independent of the test input.

Algorithms↗

PubMedDate not supplied

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.

Algorithms↗