Nguyen T-U, Diong B, Nazeran H, Goldman M
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:1078-81. doi: 10.1109/IEMBS.2007.4352482.
This paper introduces two new respiratory system models, the Mead-Cw model and the Mead-Cl model, which are 6-component models that are intermediate in complexity between the well-known 7-component Mead model and the recently proposed 5-component augmented RIC model (derived from the Mead model by eliminating both Cw and Cl). Their modeling errors were compared to the RIC, extended RIC, augmented RIC and Mead models, for component values estimated from IOS data. The two new models yielded lower errors than all the other models, except for the Mead model. However, the Mead-Cl model and the Mead-Cw model also yielded unreasonably large values for Cw and Cl, respectively, which are known disadvantages of the Mead model. Hence the augmented RIC model appears to be the most useful at present for IOS-based computer-aided detection and diagnosis of respiratory disorders.
本文介绍了两种新的呼吸系统模型，即米德 - 克模型（Mead-Cw模型）和米德 - 氯模型（Mead-Cl模型），它们是六组分模型，其复杂度介于著名的七组分米德模型和最近提出的五组分增强RIC模型（通过消除Cw和Cl从米德模型推导而来）之间。对于从IOS数据估计的组分值，将它们的建模误差与RIC模型、扩展RIC模型、增强RIC模型和米德模型进行了比较。除米德模型外，这两种新模型产生的误差比所有其他模型都低。然而，米德 - 氯模型和米德 - 克模型分别还产生了不合理的Cw和Cl大值，这是米德模型已知的缺点。因此，目前增强RIC模型似乎对于基于IOS的呼吸系统疾病计算机辅助检测和诊断最为有用。
