Estimation of pulmonary elastance fuzzy model by data combination of two respiration phases

Pulmonary characteristics differ in patients, and the suitable setting of ventilation condition is needed for every patient in the artificial respiration. The pulmonary elastance is one of the important features of lung, and it is a basis for deciding the airway pressure limit value. To get the pulmonary elastance of the of the patient from measurement data of the artificial respiration, the fuzzy logic technique has been proposed for estimating the pulmonary elastance and the static P - V curve in our previous works. In this paper, a new technique of fuzzy modeling based on data combination of two respiration phases is proposed to improve the estimation precision, and some estimation examples using real patient data are given to illustrate the superiority of the proposed method over the previous algorithm in the precision.