Respiratory system modeling based on the physiological regulatory mechanism

• Main Authors: Pei-chia Huang, 黃沛嘉
• Other Authors: 吳炤民
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/55903051176983625069

碩士 === 國立中央大學 === 電機工程學系 === 103 === The purpose of this study is to build a respiratory system model with physiological regulatory mechanism by using equivalent analog circuits and mathematical functions. Based on the physiological mechanism and clinical experimental data, we can adjust the parameters to simulate the physiological phenomenon of normal condition, environmental changes and pathological conditions. We used the lumped parameter method to build the respiratory system model and divided it into four sections: upper airways, collapsible airways, small airways and alveolar regions. Furthermore, by adding the respiratory control mechanism into the system, we could simulate the effects of oxygen and carbon dioxide on the respiratory behavior, and show how pH value affects breathing frequency and tidal volume with this model. The respiratory system model in this thesis could be used to simulate three conditions: (1) normal condition, (2) environmental changes, and (3) disease situations. In normal condition, the simulation results could show physiologically normal air flow, tidal volume and respiratory rate. When simulating the situation of hypercapnia, we adjust the inspiratory carbon dioxide from normal level, which is 0.3%, to 3%、5%、6% and 7%. Under these conditions, excess carbon dioxide has been inhaled, causing a drop in pH values. Respiratory rate and tidal volume were significantly increased. Furthermore, total ventilation was increased to 1.7, 3.06, 4.3 and 6 times larger than that of the original, respectively. When simulating the situation of hypoxia, we adjust the inspiratory oxygen from normal level, which is 20%, to 9%、8% and 7%. Under these circumstances, respiratory rate and tidal volume were significantly increased. Total ventilation was increased to 2.3, 3.2 and 4.4 times larger than that of the original, respectively. Based on these results, it could be implied that oxygen is more sensitive to respiratory behaviors. When simulating disease situations, we simulated asthma, chronic bronchitis and emphysema. Our model could have the ability to simulate hypercapnia and hypoxia caused by airway obstruction. In this case, respiratory rate was increased, but the tidal volume was not changed, and the total ventilation was increased slightly. In summary, our results showed that this model was capable of simulating air flow, tidal volume, breathing frequency and other characteristics under normal conditions. By adding respiratory control mechanism, we could simulate respiratory rate and tidal volume responses caused by oxygen and carbon dioxide changes due to conditions of environmental changes and diseases. The model we built in this study provides a research tool for studying respiratory behavior changes under different conditions, and a prediction of respiratory behavior affected by environmental changes and diseases.