Machine Learning Respiratory Gas Control for End-Tidal Modulation
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Solution Overview
Problem
Current methods for controlling end-tidal carbon dioxide (CO2) and oxygen (O2) levels in respiratory studies are limited by their inability to independently manipulate these gases, require operator intervention, and are not suitable for unconscious patients or rapid, precise modulations, often leading to instability and variability in results.
Innovation Solution
A machine learning-based system that continuously adjusts the volume and concentration of respiratory gas mixtures in real-time, using feedback from current and previous breathing cycles to optimize gas flow without the need for reservoirs or aprioristic physiological models, allowing for precise and independent modulation of end-tidal CO2 and O2 levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional methods (apnea, hyperventilation, re-breathing) are used to alter PaO2 and PaCO2 levels, then gas levels can be changed without equipment or with simple equipment, but the levels of O2 and CO2 cannot be manipulated independently and the methods have low reproducibility
Solution Approach 1:
The system segments the control of respiratory gases by independently regulating O2 and CO2 flows through separate flow controllers (10), allowing precise independent manipulation of each gas's concentration and volume delivered to the subject
Solution Approach 2:
The system implements feedback control by continuously monitoring end-tidal gas levels (PetCO2 and PetO2) and using this information to adjust the inspired gas concentrations and volumes in real-time, achieving precise and reproducible gas level manipulation
2Ease of operation
If fixed concentrations of gases are administered for inspiration, then the method is simple to implement, but it is difficult to achieve specific levels of PetCO2 and PetO2 and control them independently
Solution Approach 1:
The system transitions from static fixed gas concentrations to dynamic adjustable concentrations by implementing flow controllers (10) that can vary O2 and CO2 flows in real-time, and a control unit (50) that continuously adjusts inspired gas composition based on monitored end-tidal levels
Solution Approach 2:
The system changes the parameters of inspired gas by independently adjusting the concentration and volume of O2 and CO2 through flow controllers (10), enabling precise control of end-tidal gas levels through real-time modification of gas delivery parameters
3Device complexity
If manual adjustment by operator is used to control gas levels, then the system is easier to implement, but it requires operator intervention and is not suitable for unconscious patients or rapid modulations
Solution Approach 1:
The system implements automated feedback control where the control unit (50) continuously receives end-tidal gas level data and automatically adjusts flow controllers (10) to maintain desired PetCO2 and PetO2 levels, eliminating the need for manual operator intervention
Solution Approach 2:
The system performs self-regulation by automatically monitoring end-tidal gas levels and adjusting inspired gas composition without external intervention, making it suitable for unconscious patients and rapid modulations where continuous automated control is required
4Loss of information
If aprioristic physiological models are used to calculate gas flows, then the system can predict gas requirements, but it requires accurate estimation of physiological parameters and is complex to calibrate
Solution Approach 1:
The system replaces complex aprioristic physiological models with a feedback-based approach where end-tidal gas level measurements directly guide adjustments to inspired gas composition, eliminating the need for complex physiological parameter estimation and model calibration
Data Source
AI summary
In accordance with the present disclosure, a system and method of intelligent control is provided that is based on machine learning, to modulate end-tidal concentration levels through continuous adjustments in the volume and concentration of a flow of incoming respiratory gases. The system and the method are able to continually estimate and adjust new gas flows that are administered for a user to inhale in immediate future moments of inspiration, based on the concentration and pressure signals collected at the actual moment of breathing and at previous moments of breathing, without the need for reservoirs to store inspired or expired gases, or for aprioristic physiological models.