Adaptive Wheeze Detection for Variable Respiratory Sound Volumes
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Solution Overview
Problem
Existing wheeze detection technologies face challenges in accurately identifying wheezes during severe asthma attacks or in individuals with large body weights or body mass indices, where respiratory sound volumes are either too high or too low, making it difficult to distinguish wheezes from background noise.
Innovation Solution
A wheeze detection apparatus that adjusts detection sensitivity based on respiratory sound volume, increasing sensitivity when sound volumes are outside a predetermined range to enhance detection in challenging conditions, and reducing sensitivity when within the range to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detection sensitivity is increased to detect wheezes in severe asthma attacks or individuals with large body weight, then wheeze detection capability is improved, but false positives increase due to high respiratory sound volume
Solution Approach 1:
The patent applies dynamics by making the detection sensitivity adjustable rather than fixed. The system dynamically changes the detection threshold based on the measured respiratory sound volume, increasing sensitivity when respiratory sound volume is low (severe asthma, large body weight) and decreasing sensitivity when respiratory sound volume is high (normal conditions), thereby resolving the contradiction between detection capability and false positive rate
Solution Approach 2:
The patent changes the detection parameter (sensitivity threshold) based on the respiratory sound volume parameter. When respiratory sound volume exceeds a predetermined threshold, the system adjusts the detection sensitivity parameter to a lower value, and when respiratory sound volume is below the threshold, it adjusts sensitivity to a higher value, thus adapting to different measurement conditions
2Reliability
If detection sensitivity is decreased to maintain accuracy in normal conditions, then false positives are reduced, but wheezes in severe asthma attacks or individuals with large body weight become undetectable
Solution Approach 1:
The system dynamically adjusts detection sensitivity based on real-time respiratory sound volume measurement, switching between high sensitivity mode (for severe asthma/large body weight) and low sensitivity mode (for normal conditions), ensuring both detection capability and accuracy are maintained across different scenarios
Solution Approach 2:
The detection sensitivity parameter is changed according to the respiratory sound volume parameter, creating an adaptive detection system that optimizes the balance between sensitivity and specificity for each measurement condition
Data Source
AI summary
There is provided a wheeze detection apparatus including: a sound measurer configured to measure a pulmonary sound of a measurement subject; a respiratory sound volume deriver configured to derive a respiratory sound volume of the measurement subject based on the sound measured by the sound measurer; and a wheeze detector configured to extract a maximum point from an intensity distribution for each frequency of the sound and to detect wheeze based on information on the maximum point. The wheeze detector sets, in a case where the respiratory sound volume is outside a predetermined range, detection sensitivity of the wheeze to a higher value than the detection sensitivity in a case where the respiratory sound volume is within the range.