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一种用于描述健康受试者呼吸阻抗高频范围的重复参数模型。

A recurrent parameter model to characterize the high-frequency range of respiratory impedance in healthy subjects.

出版信息

IEEE Trans Biomed Circuits Syst. 2013 Dec;7(6):882-92. doi: 10.1109/TBCAS.2013.2243837.

Abstract

In this work, a re-visited model of the respiratory system is proposed. Identification of a recurrent electrical ladder network model of the lungs, which incorporates their specific morphology and anatomical structure, is performed on 31 healthy subjects. The data for identification has been gathered using the forced oscillation lung function test, which delivers a non-parametric model of the impedance. On the measured frequency response, the ladder network parameters have been identified and a fractional order has been calculated from the recurrent ratios of the respiratory mechanics (resistance and compliance). The paper includes also a comparison of our recurrent parameter model with another parametric model for high frequency range. The results suggest that the two models can equally well characterize the respiratory impedance over a long range of frequencies. Additionally, we have shown that the fractional order resulting from the recurrent properties of resistance and compliance in the ladder network model is independent of frequency and is not biased by the nose clip wore by the patients during measurements. An illustrative example shows that our re-visited model is sensitive to changes in respiratory mechanics and the fractional order value is a reliable parameter to capture these changes.

摘要

在这项工作中,提出了一种经过重新审视的呼吸系统模型。对 31 名健康受试者进行了肺部递归电梯网络模型的识别,该模型包含了其特定的形态和解剖结构。识别所使用的数据是通过强迫振荡肺功能测试获得的,该测试提供了阻抗的非参数模型。在测量的频率响应上,已经识别了梯网络参数,并从呼吸力学(阻力和顺应性)的递归比计算了分数阶。本文还包括将我们的递归参数模型与高频范围内的另一个参数模型进行比较。结果表明,这两个模型可以在很宽的频率范围内同样很好地描述呼吸阻抗。此外,我们已经表明,来自梯网络模型中阻力和顺应性的递归特性的分数阶与频率无关,并且不受患者在测量过程中佩戴鼻夹的影响。一个说明性的例子表明,我们重新审视的模型对呼吸力学的变化敏感,并且分数阶值是捕获这些变化的可靠参数。

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