Oscillometry Measurement Evaluation Using Objective Functions
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Solution Overview
Problem
Current oscillometry systems in clinical pulmonary function testing require human operator intervention for assessing variability and selecting measurements, leading to operator dependence and variability in results due to differences in breath segmentation and potential artifacts, which complicates the acquisition of comparable data for averaging.
Innovation Solution
A method and system that utilize a processor to receive, isolate, and evaluate oscillometry measurements by calculating objective functions such as coefficient of variation, allowing for automated acceptance or rejection of measurements based on predetermined thresholds, ensuring standardized and reproducible data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual operator assessment and selection of oscillometry measurements is used, then flexibility in evaluating different criteria (CV, R, X, Z) is improved, but operator dependence and variability in results worsen
Solution Approach 1:
The system performs self-assessment of measurement quality by automatically calculating objective functions (coefficient of variation, signal-to-noise ratio, coherence) and comparing them against pre-defined thresholds to determine acceptance or rejection of measurements, eliminating the need for manual operator evaluation while maintaining consistent criteria application
Solution Approach 2:
The system changes the evaluation approach from subjective manual assessment to objective automated calculation by computing specific parameters (coefficient of variation, signal-to-noise ratio, coherence) and using these quantified metrics to automatically accept or reject measurements based on predetermined thresholds
2Adaptability or versatility
If varying degrees of individual quality control and breath segmentation are applied to each measurement, then customization of analysis is improved, but comparability of measurements for averaging worsens
Solution Approach 1:
The system segments measurements into individual breaths and evaluates each breath independently using consistent quality criteria, then selects only those breaths that meet the predetermined thresholds for inclusion in the average, ensuring that all contributing data points are comparable
Solution Approach 2:
The system performs preliminary quality assessment and breath segmentation before averaging by calculating objective functions for each measurement and pre-selecting only those that meet the quality thresholds, ensuring that only comparable measurements are combined
3Reliability
If automated processing of oscillometry measurements is implemented, then operator independence and consistency are improved, but complexity of the system worsens
Solution Approach 1:
The system replaces the mechanical process of manual operator assessment with automated computational processing by using algorithms to calculate objective functions (coefficient of variation, signal-to-noise ratio, coherence) and automatically make acceptance decisions based on predetermined thresholds
Solution Approach 2:
The system introduces an intermediary automated evaluation layer between data acquisition and final results by computing objective functions as intermediate metrics that mediate between raw measurements and the final averaged output, enabling consistent automated decision-making
4Productivity
If multiple measurements with different numbers of complete breaths are averaged, then data utilization is improved, but accuracy of the average worsens
Solution Approach 1:
The system performs preliminary breath segmentation and counting for each measurement before averaging, identifying the number of complete breaths in each measurement and using this information to weight or select measurements appropriately to ensure accurate representation in the final average
Solution Approach 2:
The system changes from simple averaging of measurements with different breath counts to a parameter-based evaluation approach where objective functions (coefficient of variation, signal-to-noise ratio, coherence) are calculated and used to select or weight measurements, ensuring that only measurements with sufficient and comparable data quality contribute to the final average
Data Source
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AI summary
A method for acquiring oscillometry measurements with an oscillometry measuring system comprises receiving oscillometry measurements, using at least one processor of the oscillometry measuring system, the oscillometry measurements being from at least one oscillometry recording. Parameters are identified in the oscillometry measurements. An objective function(s) is calculated from the parameters of the oscillometry measurements. The objective function(s) is evaluated as a function of at least one predetermined threshold. The oscillometry measurements are accepted or rejected from the evaluating. Oscillometry data is output using the oscillometry measurements if accepted from the evaluating. A system for acquiring oscillometry measurements is also provided.