Research on the Identification Method of Respiratory Characteristic Parameters during Mechanical Ventilation

In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In this method, the patient’s respiratory sample set is obtained through non-invasive measurements. Experimental results demonstrate that the mean relative error of pulmonary elastance identification was 14.25%, and the mean relative error of intrapulmonary pressure identification was 12.33% using the Romberg integral algorithm. For chronic patients using non-invasive ventilators, the fault-tolerant space for ventilator parameter setting is large; this method meets the requirement of auxiliary setting of non-invasive ventilator parameters. For invasive ventilators, a respiratory characteristic parameter identification method based on the AVOV–BP neural network is established. In this method, the patient’s respiratory sample set is obtained through real-time invasive measurements. Even with small sample datasets, experimental results show that the mean relative error of pulmonary elastance identification and intrapulmonary pressure identification were both 0.22%. For critically ill patients using invasive ventilators, the fault-tolerant space for ventilator parameter setting is small; this method meets the requirement of auxiliary setting of invasive ventilator parameters.