Pressure dynamic characteristics of pressure controlled ventilation system of a lung simulator

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection

BACKGROUND AND OBJECTIVE

Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data.

METHODS

In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy.

RESULTS

VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01).

CONCLUSIONS

Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

Numerical modeling and development of a dual lung simulator using partitioned fluid-structure interaction approach for ventilator testing

New designs of mechanical ventilators require extensive testing before utilizing the ventilator on a patient. Test lungs are commonly used to understand the behavior of new designs of ventilators and the lung mechanics. The current study aims to develop a numerical model of dual test lungs utilizing the partitioned fluid-structure interaction (FSI) approach and test it against the available experimental data of volume-controlled ventilation. Two breathing rates of 12 and 18 bpm were studied at two different tidal volumes of 500 and 600 ml for spontaneous breathing. It is found that with an increase in the compliance (tidal volume/pressure rise) of the lung, the peak pressure rise inside the test lung decreases irrespective of the breathing rate. The maximum average pressure of 44.73, 27.45, and 14 cm H₂ O is observed for static lung compliances of 10, 21 , and 39 ml/cm H₂ O, respectively at a tidal volume of 600 ml. Similarly, the maximum von-misses stress was higher (498 kPa) for the lung with lower compliance (10 ml/cm H₂ O) as compared to the lung with higher compliance (39 ml/cm H₂ O) at the end of inspiration. This study forms a basis for using computational methods to model simple lung simulators that can effectively investigate the lung mechanics for both spontaneous and ventilated breathing. Thus, it can be utilized as a reference to test novel designs of mechanical ventilators.

Advanced mechanical ventilation modes: design and computer simulations

In this paper, three different advanced mechanical ventilation modes, pressure regulated volume control ventilation (PRVC), proportional assist ventilation (PAV), and adaptive support ventilation (ASV) are designed and simulated on the computer via MATLAB/Simulink. In the algorithms of advanced ventilation modes, a closed-loop control structure is used and recursive least squares method is considered for the estimation of respiratory mechanics. The designed algorithms are then applied to the human respiratory system model for the active and/or passive patient cases. Simulation results show that such algorithms can be designed and simulated on the computer successfully. In addition, the simulation environment helps us to understand the working principles of the advanced modes and to see the results such as ventilator waveforms, the effect of the parameter changes. Moreover, it also allows us to improve and test the algorithms and strategies quickly and efficiently.

Coupling Effect of Double Lungs on a VCV Ventilator with Automatic Secretion Clearance Function

For patients with mechanical ventilation, secretions in airway are harmful and sometimes even mortal, it's of great significance to clear secretion timely and efficiently. In this paper, a new secretion clearance method for VCV (volume-controlled ventilation) ventilator is put forward, and a secretion clearance system with a VCV ventilator and double lungs is designed. Furthermore, the mathematical model of the secretion clearance system is built and verified via experimental study. Finally, to illustrate the influence of key parameters of respiratory system and secretion clearance system on the secretion clearance characteristics, coupling effects of two lungs on VCV secretion clearance system are studied by an orthogonal experiment, it can be obtained that rise of tidal volume adds to efficiency of secretion clearance while effect of area, compliance, and suction pressure on efficiency of secretion clearance needs further study. Rise of compliance improves bottom pressure of secretion clearance while rise of area, tidal volume, and suction pressure decreases bottom pressure of secretion clearance. This paper can be referred to in researches of secretion clearance for VCV.

ANSYS-MATLAB co-simulation of mucus flow distribution and clearance effectiveness of a new simulated cough device

Coughing is an irritable reaction that protects the respiratory system from infection and improves mucus clearance. However, for the patients who cannot cough autonomously, an assisted cough device is essential for mucus clearance. Considering the low efficiency of current assisted cough devices, a new simulated cough device based on the pneumatic system is proposed in this paper. Given the uncertainty of airflow rates necessary to clear mucus from airways, the computational fluid dynamics Eulerian wall film model and cough efficiency (CE) were used in this study to simulate the cough process and evaluate cough effectiveness. The Ansys-Matlab co-simulation model was set up and verified through experimental studies using Newtonian fluids. Next, model simulations were performed using non-Newtonian fluids, and peak cough flow (PCF) and PCF duration time were analyzed to determine their influence on mucus clearance. CE growth rate (λ) was calculated to reflect the CE variation trend. From the numerical simulation results, we find that CE rises as PCF increases while the growth rate trends to slow as PCF increases; when PCF changes from 60 to 360 L/min, CE changes from 3.2% to 51.5% which is approximately 16 times the initial value. Meanwhile, keeping a long PCF duration time could greatly improve CE under the same cough expired volume and PCF. The results indicated that increasing the PCF and PCF duration time can improve the efficiency of mucus clearance. This paper provides a new approach and a research direction for control strategy in simulated cough devices for airway mucus clearance.

Pneumatic Rotary Actuator Position Servo System Based on ADE-PD Control

In order to accurately control the rotation position of a pneumatic rotary actuator, the flow state of the gas and the motion state of the pneumatic rotary actuator in the pneumatic rotary actuator position servo system are analyzed in this paper. The mathematical model of the system and the experiment platform are established after that. An Adaptive Differential Evolution (ADE) algorithm which adaptively ameliorates the scaling factor and crossover probability in the process of individual evolution is proposed and applied to the parameter optimization of PD controller. The experimental platform is used to compare the controller with Differential Evolution (DE) algorithm and NCD-PID controller. Finally, the characteristics of the system are tested by increasing the inertial load. The experimental results illustrate that system using ADE-PD control strategy has greater position precision and faster response than using DE-PD and NCD-PID strategies, and shows great robustness.

Dynamic Characteristics of Mechanical Ventilation System of Double Lungs with Bi-Level Positive Airway Pressure Model

In recent studies on the dynamic characteristics of ventilation system, it was considered that human had only one lung, and the coupling effect of double lungs on the air flow can not be illustrated, which has been in regard to be vital to life support of patients. In this article, to illustrate coupling effect of double lungs on flow dynamics of mechanical ventilation system, a mathematical model of a mechanical ventilation system, which consists of double lungs and a bi-level positive airway pressure (BIPAP) controlled ventilator, was proposed. To verify the mathematical model, a prototype of BIPAP system with a double-lung simulators and a BIPAP ventilator was set up for experimental study. Lastly, the study on the influences of key parameters of BIPAP system on dynamic characteristics was carried out. The study can be referred to in the development of research on BIPAP ventilation treatment and real respiratory diagnostics.

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.