基于PRN强迫振荡数据参数估计的线性呼吸系统模型比较。
A comparison of linear respiratory system models based on parameter estimates from PRN forced oscillation data.
作者信息
Diong B, Grainger J, Goldman M, Nazeran H
机构信息
Department of Engineering, Texas Christian University, Fort Worth, TX 76129, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2879-82. doi: 10.1109/IEMBS.2009.5333109.
The forced oscillation technique offers some advantages over spirometry for assessing pulmonary function. It requires only passive patient cooperation; it also provides data in a form, frequency-dependent impedance, which is very amenable to engineering analysis. In particular, the data can be used to obtain parameter estimates for electric circuit-based models of the respiratory system, which can in turn aid the detection and diagnosis of various diseases/pathologies. In this study, we compare the least-squares error performance of the RIC, extended RIC, augmented RIC, augmented RIC+I(p), DuBois, Nagels and Mead models in fitting 3 sets of impedance data. These data were obtained by pseudorandom noise forced oscillation of healthy subjects, mild asthmatics and more severe asthmatics. We found that the aRIC+I(p) and DuBois models yielded the lowest fitting errors (for the healthy subjects group and the 2 asthmatic patient groups, respectively) without also producing unphysiologically large component estimates.
强迫振荡技术在评估肺功能方面比肺量计具有一些优势。它仅需要患者被动配合；还能以与频率相关的阻抗这种形式提供数据，这非常便于进行工程分析。特别是，这些数据可用于获取基于电路的呼吸系统模型的参数估计值，进而有助于各种疾病/病理状况的检测和诊断。在本研究中，我们比较了RIC、扩展RIC、增强RIC、增强RIC + I(p)、杜波依斯（DuBois）、纳吉尔斯（Nagels）和米德（Mead）模型在拟合3组阻抗数据时的最小二乘误差性能。这些数据是通过对健康受试者、轻度哮喘患者和重度哮喘患者进行伪随机噪声强迫振荡获得的。我们发现，aRIC + I(p)和杜波依斯模型分别在健康受试者组和两个哮喘患者组中产生了最低的拟合误差，同时也没有产生非生理性的大分量估计值。
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