Leak Detection in Patient Gas Modules Using CO2 Curve Analysis
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Solution Overview
Problem
Existing ventilation systems face challenges in accurately detecting leaks in patient gas modules, particularly due to dilution effects from ambient air, which can lead to measurement errors and potential patient injury, especially in closed-loop anesthesia environments.
Innovation Solution
A process that analyzes the time curves of carbon dioxide and another gas concentration in the breathing gas mixture to determine a statistical similarity indicator, allowing for robust leak detection, including phase shifts and covariance analysis, with optional pressure measurements to confirm and locate leaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a continuous gas stream is suctioned from the patient for measurement, then gas concentration values can be monitored, but leaks in the suction section cause dilution effects and measurement errors
Solution Approach 1:
The system continuously monitors gas concentrations (CO2, O2, N2O, anesthetic gases) and uses this feedback to detect leaks by comparing expected vs. actual concentration changes. When a leak is detected, the system can alert operators and potentially adjust ventilation parameters to maintain patient safety.
Solution Approach 2:
The patent introduces an intermediary detection mechanism that monitors multiple gas concentrations simultaneously to identify leaks. Instead of directly detecting the leak itself, the system uses gas concentration changes as an intermediary indicator to infer the presence and location of leaks in the suction section.
2Difficulty of detecting and measuring
If monitoring measures relative concentration changes between breathing phases, then abrupt leaks can be detected, but slowly developing leaks are detected with difficulty
Solution Approach 1:
The system performs preliminary analysis by continuously tracking gas concentration trends and comparing them against expected patterns. By establishing a baseline of normal concentration changes and continuously comparing actual measurements against this baseline, the system can detect gradual deviations that indicate slowly developing leaks before they become critical.
Solution Approach 2:
The monitoring system dynamically adapts its detection thresholds and analysis methods based on the current breathing phase and observed concentration patterns. It transitions between different detection modes to optimize sensitivity for both abrupt and gradual leaks, adjusting the expected concentration change rates based on real-time observations.
3Measurement precision
If pressure measurements are used to detect leaks, then leak detection is possible, but pressure changes are highly attenuated when the pump is running and monitoring is only meaningful for sufficiently high airway pressure set values
Solution Approach 1:
The system changes the measurement parameter from pressure to gas concentration. By monitoring the concentrations of multiple gases (CO2, O2, N2O, anesthetic gases) instead of relying on pressure changes, the system overcomes the attenuation problem associated with pump operation and can effectively detect leaks across a wider range of airway pressure conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enables reliable and cost-effective leak detection with up to 20% measurement error tolerance, effectively identifying leaks even in slowly developing cases and reducing the risk of patient injury by ensuring accurate gas dispensing.
Implementation Method 1
a measurement error of up to 20% in the gas concentration measurement is caused due to the leak
Data Source
AI summary
A process (10), with a computer program, a device (30) and a ventilation system (40) detect a leak in a patient gas module, which suctions and analyzes a continuous sample gas stream from a ventilated patient (20), in a ventilation system for ventilating a patient (20). The process includes a determination (12) of a first time curve of a carbon dioxide concentration in a breathing gas mixture of the patient (20) and the determination (14) of a second time curve of a concentration of another gas in the breathing gas mixture, which gas is different from carbon dioxide. The process (10) further includes a determination (16) of a statistical similarity indicator between the first time curve and the second time curve and the detection (18) of the leak based on the similarity indicator.