A RESPIRATORY MECHANICAL PARAMETERS ESTIMATION TECHNOLOGY BASED ON EXTENDED LEAST SQUARES

Respiratory mechanical parameters of ventilated patients are usually referred in the respiratory diagnosis and treatment. However, the effectiveness of the modern estimation methods is limited. To estimate the overall breathing resistance, overall respiratory compliance, and residual volume simultaneously, a new mathematical model of mechanical ventilation system was proposed. Furthermore, to improve the estimation accuracy, the noise model of mechanical ventilation system was taken into consideration. Based on the mathematical model, a respiratory mechanical parameters estimation method based on extended least squares (ELS) algorithm was derived. Finally, to test the respiratory mechanical parameters estimation method, it was studied experimentally and numerically, and it was approved that the proposed method is effective to estimate the three respiratory mechanical parameters simultaneously and precisely. The estimated values of the parameters can be adopted in the clinical practice. The study provides a new method to estimate respiratory mechanical parameters.

[1]  Esra Saatci,et al.  Respiratory parameter estimation in non-invasive ventilation based on generalized Gaussian noise models , 2010, Signal Process..

[2]  Yan Shi,et al.  Pressure Dynamic Characteristics of Pressure Controlled Ventilation System of a Lung Simulator , 2014, Comput. Math. Methods Medicine.

[3]  Adam Seiver,et al.  Real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients: A feasibility study , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Maolin Cai,et al.  Working characteristics of two kinds of air-driven boosters , 2011 .

[5]  Kenji Kawashima,et al.  Power Assessment of Flowing Compressed Air , 2006 .

[6]  John G. Eyles,et al.  Estimating Respiratory Mechanical Parameters in Parallel Compartment Models , 1981, IEEE Transactions on Biomedical Engineering.

[7]  J. X. Brunner,et al.  History and principles of closed-loop control applied to mechanical ventilation , 2002 .

[8]  Bill Diong,et al.  Respiratory Impedance Values in Adults Are Relatively Insensitive to Mead Model Lung Compliance and Chest Wall Compliance Parameters , 2010 .

[9]  M. Borrello Modeling and control of systems for critical care ventilation , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Robert L Chatburn,et al.  Computer control of mechanical ventilation. , 2004, Respiratory care.

[11]  G. Nucci,et al.  On-line estimation of respiratory parameters of lung mechanics in different pathologies , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[12]  Janusz Mroczka,et al.  Nonlinear model for mechanical ventilation of human lungs , 2006, Comput. Biol. Medicine.