Automated mechanical ventilator design and analysis using neural network

Area of Science:

• Biomedical Engineering
• Respiratory Medicine
• Computational Modeling

Background:

• Mechanical ventilation is crucial for patients with respiratory distress, with demand surging during pandemics.
• Proper ventilator parameter selection is vital for effective patient care and requires trained professionals.
• Pneumonia and Chronic Obstructive Pulmonary Disease (COPD) are significant respiratory conditions necessitating advanced ventilatory support.

Purpose of the Study:

• To design and simulate a computer-aided mechanical ventilator system.
• To validate the ventilator's performance for clinical complications like pneumonia and COPD.
• To ensure stable and optimal respiratory support through precise parameter control.

Main Methods:

• Computer-aided simulation of a novel ventilator design.
• Validation against normal ventilatory parameters for pneumonia and COPD.
• Control of tidal volume, respiratory rate, and I:E ratio using check valves.
• Hyperparameter tuning via a feed-forward neural network (FFNN).
• Integration of trained FFNN features with a mimicked lung model.

Main Results:

• The FFNN demonstrated optimal performance during training and testing phases.
• Simulation and validation confirmed the ventilator system's stability and effectiveness.
• The designed system provides optimal respiratory support for patients with pneumonia and COPD.

Conclusions:

• The developed mechanical ventilator system is stable and effective for clinical application.
• The simulation approach provides a reliable method for ventilator design and validation.
• This technology offers improved respiratory support for patients suffering from severe lung conditions.