Dynamic Characteristics of a Mechanical Ventilation System With Spontaneous Breathing

Abstract

Mechanical ventilation is an important and effective method for the treatment of pulmonary diseases patients with spontaneous breathing. Spontaneous breathing refers to the physiological breathing activity caused by the respiratory muscle. These patients retain some ability to breathe spontaneously, but do not reach the level of normal breathing. Mathematical simulation and modeling of the mechanical ventilation system are crucial for research on mechanical ventilation. In this paper, a novel pneumatic model of a mechanical ventilation system considering patients' spontaneous breathing is presented. Mathematical equations are accurately derived to explain the principles of the respiratory system and mechanical ventilation system. An experimental prototype is designed to confirm the correctness and validity of the pneumatic model. The goodness of fit shows that the mathematical simulation curve fits well with the experimental curve, thus confirming the accuracy of the pneumatic model. For patients with a certain degree of spontaneous breathing, the mechanical ventilation mode is set to the pressure support ventilation (PSV) mode, and variations in the flow, pressure and tidal volume curves are observed by changing specific respiratory mechanics parameters such as the compliance (C), the effective area of the throttle in the pneumatic model (A), and the muscle pressure difference (P mus). From the results, it can be concluded that the resistance of the mechanical ventilation system can be equivalent to A. The dynamic characteristics (mainly flow characteristics, tidal volume characteristics and pressure characteristics) of the mechanical ventilation system are directly influenced by variations in C, A and P mus. This study is an important reference for setting ventilation levels and ventilator control parameters. The results of this research are valuable for the diagnosis and treatment of respiratory diseases.

Spirometry-Based Airways Disease Simulation and Recognition Using Machine Learning Approaches

“…However, for this study, the model used is slightly more sophisticated to get a better representation of the distribution of the ventilation, see figure 1b. The profile of the pressure used to mimic the muscular action is taken from L. Hao et al [9] and represents a standard for tidal breathing. It is necessary to highlight that the hypotheses of linearity used in this model are respected in the regime of tidal breathing [17].…”

“…It is possible to mimic the behavior of healthy subjects by setting physiological values of R eq = R t + R1R2 R1+R2 and E eq = E1E2 E1+E2 . In the literature, they are set to: R eq = 3 cmH 2 O/L/s and E eq = 10 cmH 2 O/L [9]. In this work, we mimic cystic fibrosis by doubling the healthy elastance (doubling the rigidity of the balloons): E eq = 20 cmH 2 O/L, and asthmatic subjects by setting R eq = 5 cmH 2 O/L/s.…”

“…Normally, ML models are trained and tested on data and labels are provided by medical doctors. However, the originality of this study consists in using mathematical equations to simulate the ventilation following the directives of IEEE [9], then this data will be used to train the ML models. The obtained volume flows respect the expectations for both healthy and not healthy subjects.…”

Differential Pressure Spirometry for Mechanical Ventilation Using Dichotomic Search

“…The three primary variables monitored by the mechanical ventilator during the gas injection are airway pressure, flow, and the tidal volume (numerical integration of the flow) [10] . Processing these variables, as well as other critical ventilatory parameters such as positive end-expiratory pressure (PEEP), FiO 2 , and plateau pressure allow the clinical treatment evaluation [11] – [16] . Airway pressure can be measured easily by applying the Pascal principle.…”