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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Matamis, D., Lemaire, F., Harf, A., Brun-Buisson, C., Ansquer, J.C., Atlan, G.: Total Respiratory Pressure-Volume Curves in the Adult Respiratory Distress Syndrome. Chest 86, 58–66 (1984)
Ranieri, V.M., Giuliani, R., Fiore, T., Damhrosio, M., Milic-Emili, J.: Volume-Pressure Curve of the Respiratory System Predicts Effects of PEEP in ARDS: “Occlusion” Versus “Constant Flow” Technique. Am. J. Respir. Crit. Care Med. 149, 19–27 (1994)
Uhl, R.R., Lewis, F.J.: Digital Computer Calculation of Human Pulmonary Mechanics Using a Least Squares Fit Technique. Comput. Biomed. Res. 7, 489–495 (1974)
Muramatsu, K., Yukitake, K., Nakamura, M., Matsumoto, I., Motohiro, Y.: Monitoring of Nonlinear Respiratory Elastance Using a Multiple Linear Regression Analysis. European Respiratory Journal 17, 1158–1166 (2001)
Kanae, S., Muramatsu, K., Yang, Z.J., Wada, K.: Modeling of Respiration and Estimation of Pulmonary Elastance. In: The Proceedings of the 5th Asian Control Conference (ASCC2004), Melbourne, Australia, pp. 648–651 (2004)
Kanae, S., Yang, Z.-J., Wada, K.: Estimation of Pulmonary Elastance Based on RBF Expression. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 507–512. Springer, Heidelberg (2004)
Kanae, S., Maeda, K., Yang, Z.J., Wada, K.: Parameter Estimation of Nonlinear Differential Equation Models of Respiratory System by Using Numerical Integration Technique. Preprints of the 14th IFAC Symposium on System Identification, Newcastle, Australia (2006)
Sagara, S., Zhao, Z.: Numerical Integration Approach to On-Line Identification of Continuous-Time Systems. Automatica 26, 63–74 (1990)
Ljung, L.: System Identification Theory for the User. Prentice-Hall, Englewood Cliffs (1987)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Kanae, S., Yang, ZJ., Wada, K. (2007). A Decision Method for Air-Pressure Limit Value Based on the Respiratory Model with RBF Expression of Elastance. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_141
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)