Capnogram Curve Approximation for Serial Dead Space
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Solution Overview
Problem
Existing methods for automatically evaluating volume capnograms are not flexible enough to identify capnograms that significantly differ from the norm, requiring human interaction and are thus unusable without manual intervention.
Innovation Solution
A method using a numerical optimization algorithm, such as the Levenberg-Marquardt algorithm, to automatically approximate the capnogram curve with three straight lines, determining six parameters that define these lines and enable the calculation of serial dead space and gas exchange quality, allowing for the identification of unusable capnograms and improving the estimation of serial dead space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing automatic evaluation methods are used, then automation is achieved, but flexibility and ability to identify non-norm capnograms deteriorates
Solution Approach 1:
The patent implements a dynamic evaluation system that adapts to different capnogram types by automatically detecting the number of phases present in the measured data. The system transitions from static, pre-programmed evaluation criteria to dynamic, data-driven phase detection, allowing the same device to handle both norm and non-norm capnograms effectively
Solution Approach 2:
The system changes evaluation parameters based on the detected capnogram characteristics. By identifying which phases (1-4) are present in the measured data, the system automatically adjusts its evaluation criteria and determines serial dead space using appropriate phase transitions, enabling flexible handling of various capnogram patterns without manual intervention
2Measurement precision
If manual intervention is required for non-norm capnograms, then evaluation accuracy improves, but productivity and ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically detecting capnogram phases and selecting appropriate evaluation methods without requiring manual intervention. The device independently identifies non-norm capnograms, determines which phases are present in the data, and calculates serial dead space using the appropriate phase transitions, maintaining both accuracy and productivity
3Adaptability or versatility
If complex algorithms are used to handle all capnogram variations, then versatility improves, but device complexity increases
Solution Approach 1:
The patent segments the capnogram evaluation into distinct phases (1-4) with characteristic features. By dividing the continuous capnogram data into discrete phase segments based on CO2 concentration patterns, the system simplifies the evaluation process while maintaining versatility across different capnogram types
Solution Approach 2:
The system uses dynamic phase detection to identify which of the four phases are present in each measured capnogram. This dynamic segmentation approach allows the same simplified algorithm structure to handle both norm and non-norm capnograms by adapting to the actual data patterns rather than requiring complex pre-programmed scenarios
Data Source
AI summary
A method implemented, e.g., as software and a device operating according to the method for the automatic evaluation and analysis of a capnogram are provided. Measured values for an expired volume—volume measured values—and measured values for a carbon dioxide concentration—concentration measured values—are recorded for the breathing gas of a test subject. An automatic approximation of at least one part of the curve of the concentration measured values over the volume measured values is performed, by using three mutually adjacent straight lines for the approximation. The area is determined using the third straight line according to Fowler for the determination of the serial dead space Vds.