Wheezing Detection Using Peak Height and Width Ratio
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Solution Overview
Problem
Existing wheezing detection systems lack accuracy in identifying wheezing sounds due to reliance on peak magnitude comparisons alone, without considering peak width and background noise, leading to suboptimal detection performance.
Innovation Solution
A wheezing detection apparatus that converts breathing sound signals into frequency spectra, analyzes peak heights and widths, and uses a ratio of height to width to determine wheezing presence, focusing on dominant peaks within a specific frequency range (200 Hz to 1500 Hz) to enhance detection accuracy, and generates warnings based on prolonged wheezing periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only peak magnitude comparison is used for wheezing detection, then the detection system is simple, but the detection accuracy is poor
Solution Approach 1:
The patent changes the detection parameters from simple peak magnitude comparison to a multi-parameter analysis including peak height, peak width, and height-to-width ratio. This allows the system to distinguish wheezing sounds more accurately by considering the characteristic narrow width of wheezing peaks in the frequency spectrum, thereby improving detection accuracy without requiring complex additional hardware
Solution Approach 2:
The patent transitions from one-dimensional peak magnitude analysis to two-dimensional analysis by incorporating both peak height and peak width (or height-to-width ratio). This dimensional expansion enables the system to differentiate wheezing from other breathing sounds based on the distinctive shape characteristics of wheezing peaks in the frequency domain
2Measurement precision
If peak width analysis is incorporated into wheezing detection, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent simplifies the measurement process by using the height-to-width ratio as a composite parameter that inherently captures the characteristic narrow shape of wheezing peaks. This ratio metric is straightforward to calculate from the frequency spectrum data and provides a robust indicator of wheezing without requiring complex pattern recognition or additional processing steps
Data Source
AI summary
A wheezing detection apparatus includes a breathing sound detection unit that detects a breathing sound of a measurement subject and acquires a breathing sound signal in a time series expressing the breathing sound. The wheezing detection apparatus includes a determination processing unit that, in each pre-determined processing unit period, converts the breathing sound signal into a frequency space to acquire a frequency spectrum of the breathing sound, and based on a height and a width of a peak in the frequency spectrum, determines whether or not the peak indicates wheezing.