Capnography Device Dynamic Averaging for False Alarm Reduction
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Solution Overview
Problem
Medical monitoring devices face challenges in accurately determining clinically significant alerts for breath-related parameters like respiratory rate and CO2 levels, often triggering false alarms due to varying measurement periods and thresholds, which can lead to neglect of genuine clinical changes.
Innovation Solution
A method and device that dynamically adjust the averaging time for breath-related parameters based on stability and consistency, using algorithms to calculate respiratory rate and CO2 waveform patterns, reducing non-clinically significant alarms by adapting measurement periods in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed averaging time is used for breath-related parameters, then the measurement is simple and consistent, but false alarms increase due to inability to adapt to patient stability changes
Solution Approach 1:
The patent implements dynamic adjustment of the measurement period (averaging time) based on patient stability status. The system transitions from a fixed averaging time to a variable one that adapts in real-time. When patient stability changes, the system modifies the measurement period length to optimize between responsiveness and false alarm reduction, thereby resolving the contradiction between reliability and device complexity.
Solution Approach 2:
The system employs feedback mechanisms where the assessment of patient stability directly influences the measurement period selection. The stability assessment unit continuously monitors patient conditions and feeds this information back to the measurement period adjustment unit, which then modifies the averaging time accordingly. This closed-loop feedback system enables the device to adapt to changing clinical conditions while maintaining appropriate measurement sensitivity.
2Reliability
If a longer averaging time is used, then false alarms are reduced, but timely detection of clinically significant changes is delayed
Solution Approach 1:
The measurement period is dynamically adjusted based on real-time stability assessment. During stable periods, longer averaging times reduce false alarms. When instability is detected, the system automatically shortens the measurement period to enable timely detection of clinically significant changes. This dynamic adaptation resolves the contradiction between reducing false alarms and maintaining rapid response capability.
Solution Approach 2:
The system changes the parameter of measurement period length based on patient stability status. By varying this critical parameter in response to clinical conditions, the system optimizes the balance between false alarm reduction and detection speed. The parameter adjustment enables the system to achieve both long-term reliability and short-term responsiveness as needed.
3Adaptability or versatility
If the measurement period is continuously adjusted, then adaptability to patient conditions improves, but computational complexity and processing requirements increase
Solution Approach 1:
The system implements dynamic measurement period adjustment driven by stability assessment. The algorithm continuously evaluates patient stability and modifies the measurement period accordingly, enabling high adaptability to changing conditions. This dynamic approach allows the system to respond flexibly to different clinical scenarios while maintaining manageable computational complexity through structured decision logic.
Data Source
AI summary
There is provided a method for dynamically determining a breath related parameter, which includes averaging a breath related parameter over a first period of time, calculating a breath related value over a second period of time and adapting the duration of the first period of time according to the calculated breath related value.