A mathematical model of the human respiratory system

Area of Science:

• Physiology
• Biomedical Engineering
• Control Systems

Background:

• Understanding the human respiratory system's complex control mechanisms is crucial for diagnosing and treating respiratory disorders.
• Existing models often struggle to accurately represent dynamic physiological conditions and reflexes.

Purpose of the Study:

• To develop and validate a comprehensive computational model of the human respiratory system.
• To simulate respiratory responses under various physiological conditions, including hypercapnia, hypoxia, and exercise.

Main Methods:

• A hybrid model combining a continuous plant with a discrete controller simulating the Hering-Breuer reflex.
• Inclusion of arterial and central medullary sensors with time-varying parameters (lung volume, dead space, cardiac output, cerebral blood flow).
• Optimization to minimize respiratory work.

Main Results:

• The model exhibited satisfactory performance under diverse physiological conditions.
• Simulations accurately reproduced transient responses seen in Cheyne-Stokes breathing and carbon dioxide inhalation.
• The model successfully predicted responses during hypercapnia, hypoxia, periodic breathing, and moderate exercise.

Conclusions:

• The proposed model provides a robust framework for studying human respiratory control.
• It accurately simulates complex respiratory dynamics and responses to various physiological challenges.
• This model can be a valuable tool for research and clinical applications in respiratory medicine.