It is difficult to select the appropriate ventilation mode in clinical mechanical ventilation. This paper presents a nonlinear multi-compartment lung model to solve the difficulty. The purpose is to optimize respiratory airflow patterns and get the minimum of the work of inspiratory phrase and lung volume acceleration, minimum of the elastic potential energy and rapidity of airflow rate changes of expiratory phrase. Sigmoidal function is used to smooth the respiratory function of nonlinear equations. The equations are established to solve nonlinear boundary conditions BVP, and finally the problem was solved with gradient descent method. Experimental results showed that lung volume and the rate of airflow after optimization had good sensitivity and convergence speed. The results provide a theoretical basis for the development of multivariable controller monitoring critically ill mechanically ventilated patients.
Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in “respiratory signal quality index” to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects’ breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.