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A Decision Method for Air-Pressure Limit Value Based on the Respiratory Model with RBF Expression of Elastance.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Rangan, C. Pandu
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Liu, Derong
Fei, Shumin
Hou, Zengguang
Zhang, Huaguang
Sun, Changyin
Source :
Advances in Neural Networks: ISNN 2007; 2007, p1194-1201, 8p
Publication Year :
2007

Abstract

Air-pressure limit value is an important conditional parameter of artificial respiration. The pulmonary characteristics are very different according to the person. For setting appropriate ventilation conditions fitting to each patient, it is necessary to establish a mathematical model describing the mechanism of human respiratory system, and to know the pulmonary characteristic of each patient via identification of the model. For this purpose, two types of respiratory system models have been proposed by the authors. These models are expressed as second order nonlinear differential equations with air-volume variant elastic coefficient and air-volume variant resistive coefficient. In the first type of model, elastic coefficient is expressed as polynomial function of air-volume, while in the second type of model, elastic coefficient is expressed by RBF network. The model with polynomial expression of elastance has the advantage that the structure is simple. On the other hand, the model with RBF expression of elastance has better numerical stability against to the model with polynomial expression of elastance. In this paper, a decision method of air-pressure limit value based on the respiration model with RBF expression of elastance is proposed. This method adopt a numerical technique to find the point of saturation starting point in the elastance curve, So direct calculation of radius of curvature can be avoided. The proposed method is validated by an example of application to practical clinical data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540723929
Database :
Complementary Index
Journal :
Advances in Neural Networks: ISNN 2007
Publication Type :
Book
Accession number :
33198903
Full Text :
https://doi.org/10.1007/978-3-540-72393-6_141