Simulation of Respiratory Mechanics

Jodat, Ronald W.; Horgan, James D.; Lange, Ramon L.

doi:10.1016/s0006-3495(66)86694-8

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Several active lung models have been proposed in past studies for the simulation of mechanical ventilation considering the patient's spontaneous breathing [10], [12]- [18]. Jodat, Ronald W. et al conducted a comprehensive analysis of the respiratory system and obtained the mutual mechanical relationship among the various parts of the respiratory system [13]. Lutchen et al proposed a viscoelastic model, considering airway resistance, static compliance, viscous resistance and compliance, but the estimation error was large [14].…”

Section: With the Development Of Medical Technology Mechanicalmentioning

confidence: 99%

Dynamic Characteristics of a Mechanical Ventilation System With Spontaneous Breathing

et al. 2019

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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.

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Section: With the Development Of Medical Technology Mechanicalmentioning

confidence: 99%

Dynamic Characteristics of a Mechanical Ventilation System With Spontaneous Breathing

et al. 2019

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“…Para probar el esquema de control se buscó un modelo dinámico matemático del sistema respiratorio humano con ciertas características fisiológicas relevantes [3], que incluye la vía respiratoria superior, pulmones, tórax y abdomen, se le adicionaron características para simular el colapso de la vía respiratoria, resistor Starling y no linealidades del flujo de aire. También se modificó un modelo discreto para simular el ventilador BPAP [4], transformándolo a tiempo continúo en variables de estado.…”

Section: Palabras Clave -Modos Deslizantes Síndrome Apnea Obstructivunclassified

Sliding mode control for bilevel positive airway pressure: Advantages over PID control

Gallardo¹,

Leder

2013

2013 Pan American Health Care Exchanges (PAHCE)

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A bilevel positive airway pressure ventilator (BPAP) is employed as a pneumatic splint for the upper airway in obstructive sleep apnea syndrome (OSAS) treatment. In this research we developed a cascade control scheme for the BPAP ventilator using a sliding mode control, generalized super twisting algorithm (GST). The gain design and the GST algorithm analysis are done using Lyapunov functions. A mathematical model of the human respiratory system and the BPAP ventilator were developed to test the GST control, analyze, and simulate the response of a person in normal conditions, during apnea, and treated with BPAP. El ventilador por presión positiva binivel en las vías respiratorias (BPAP, por sus siglas en inglés) es usado en el tratamiento del síndrome de apnea obstructiva del sueño (SAOS). En el presente trabajo se desarrolló un esquema de control en cascada para el ventilador BPAP utilizando el control por modos deslizantes algoritmo Super Twisting Generalizado (STG). El diseño de las ganancias y análisis del algoritmo STG se realizapor medio de funciones de Lyapunov. Para probar el controlador STG, analizar y simular la respuesta de una persona en condiciones normales, durante la apnea y sometida al tratamiento BPAP se hacen uso de un modelo matemático del sistema respiratorio del ser humano y del ventilador BPAP.

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“…The parameters of R and C in model can be selected as normal human; while the respiratory muscle pressure can be acquired precisely from the data of Jodat et al(1966) image processing, as shown in figure 5. In Simulink the muscle pressure used as input, the breath mechanical parameters (R and C) is the linear response system, then the spontaneous respiratory waveform can be computed.…”

Section: Simplest Model Simulationmentioning

confidence: 99%

“…The respiratory muscle pressure data from Jodat (1966) is used as input of model. We construct the nonlinear model of breath system using simulink, the following block in Fig.…”

Section: Simulation Of Spontaneous Respiration Nonlinear Modelmentioning

confidence: 99%