Patient Interface Identification via Exhaust Flow Gradient
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Solution Overview
Problem
Existing pressure support systems for non-invasive ventilation and CPAP therapies face challenges in accurately identifying patient interface devices, particularly in distinguishing between intentional and unintentional leaks, which affects the precision of pressure delivery and patient comfort.
Innovation Solution
A pressure support system comprising a pressure generator, flow sensor, and controller that automatically identifies patient interface devices by detecting changes in exhaust flow across a predetermined pressure gradient, enabling precise determination of mask type and adjusting operating parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses multiple different patient interface devices with varying intentional leak rates, then the adaptability of the system improves, but the difficulty of detecting and measuring the correct device type increases
Solution Approach 1:
The system measures exhaust flow at multiple different pressure points and uses the changes in flow parameters across these pressure gradients to identify the patient interface device type. By analyzing how flow parameters change with pressure, the system can distinguish between different mask types despite their varying intentional leak rates.
Solution Approach 2:
The system employs a feedback mechanism where the measured exhaust flow at different pressure points is compared against expected flow patterns for various device types. This feedback loop enables automatic identification and selection of the correct device-specific flow curve, resolving the identification difficulty while maintaining adaptability.
2Measurement precision
If the system subtracts intentional leak from total leak to determine unintentional leak, then the measurement precision of unintentional leak improves, but the reliability of the measurement deteriorates due to accumulation of errors
Solution Approach 1:
The system performs preliminary identification of the patient interface device type by analyzing exhaust flow patterns at multiple pressure points before conducting leak measurements. This preliminary action ensures that the correct device-specific intentional leak values are used, preventing error accumulation from using incorrect subtraction values.
Solution Approach 2:
The system dynamically selects the appropriate intentional leak value based on the identified device type rather than using a fixed or estimated value. This dynamic approach ensures that the most accurate device-specific parameters are used in the unintentional leak calculation, improving both precision and reliability.
3Device complexity
If the system manually identifies patient interface device type, then the device complexity is reduced, but the productivity of the system deteriorates due to time-consuming manual input
Solution Approach 1:
The system automatically identifies the patient interface device type by measuring exhaust flow at multiple pressure points and comparing the results against stored flow patterns for different device types. This self-service capability eliminates the need for manual device identification, significantly improving therapy setup speed while maintaining manageable system complexity through automated algorithms.
4Ease of operation
If the system measures exhaust flow at a single pressure point, then the ease of operation improves, but the measurement precision of device identification deteriorates
Solution Approach 1:
The system segments the exhaust flow measurement process into multiple discrete pressure point measurements. By measuring flow at several different pressure points rather than a single point, the system gathers sufficient data to accurately identify the patient interface device type while maintaining ease of operation through automated sequential measurement.
Solution Approach 2:
The system performs periodic measurements of exhaust flow at multiple pressure points in a systematic sequence. This periodic measurement approach ensures accurate device identification by collecting comprehensive flow data across the pressure range, while the automated sequencing maintains operational simplicity.
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 solution enhances the accuracy of pressure computation and patient comfort by automatically identifying the patient interface device in use, minimizing unintentional leaks, and optimizing therapy delivery.
Implementation Method 1
detecting a change of exhaust flow of up to a predetermined amount across a predetermined pressure gradient of a pressure range
Data Source
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AI summary
A pressure support system includes a pressure generator, a pressure sensor, a flow sensor, and a controller cooperating with the pressure sensor and the flow sensor to control operation of the pressure generator. The controller is structured to automatically identify a patient interface device in use with the pressure support system by detecting a change of exhaust flow of up to a predetermined amount across a predetermined pressure gradient of a pressure range of the pressure support system.