Respiratory Signal Extraction via Empirical Wavelet Reconstruction
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Solution Overview
Problem
Existing methods for obtaining respiratory signals using non-contact piezoelectric sensing suffer from low signal-to-noise ratios due to noise interference, making it difficult to accurately capture human respiratory activity.
Innovation Solution
A method involving a piezoelectric sensor to obtain aliasing vital signs signals, followed by filtering and Fourier transform to generate an upper envelope, identifying key frequency points, and reconstructing the respiratory spectrum principal component interval using an empirical wavelet function to extract the respiratory signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-contact piezoelectric sensing method is used to obtain respiratory signal, then contactless measurement is achieved, but signal-to-noise ratio becomes very low due to noise interference
Solution Approach 1:
The patent segments the frequency spectrum into multiple bands and applies different processing strategies to each segment. By dividing the aliasing vital signs signals into distinct frequency components, the method can selectively enhance respiratory signal portions while suppressing noise in other segments, thereby improving signal-to-noise ratio while maintaining contactless measurement capability
Solution Approach 2:
The patent extracts the respiratory signal from the aliasing vital signs signals through Fourier transform and frequency domain analysis. By identifying and separating the respiratory frequency components from the mixed signals, the method isolates the useful respiratory information from noise interference, achieving both contactless measurement and improved signal quality
2Device complexity
If aliasing vital signs signals are directly processed, then processing complexity is reduced, but respiratory signal extraction accuracy becomes insufficient
Solution Approach 1:
The patent performs preliminary filtering and frequency domain transformation on the aliasing vital signs signals before extracting respiratory information. By pre-processing the signals to identify frequency characteristics and separate components, the method simplifies subsequent respiratory signal extraction while improving accuracy through systematic frequency domain analysis
Solution Approach 2:
The patent introduces Fourier transform as an intermediary step between signal acquisition and respiratory signal extraction. This transformation mediates the conversion from time-domain aliasing signals to frequency-domain representation, enabling accurate identification and extraction of respiratory components while maintaining manageable processing complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively filters out noise interference and reconstructs the respiratory signal with improved accuracy, addressing the limitations of direct contact methods and enhancing the precision of respiratory signal extraction.
Implementation Method 1
obtaining aliasing vital signs signals of a target human body through a piezoelectric sensor
Data Source
AI summary
Disclosed are a method, device, computer system for obtaining respiratory signal. The method includes: filtering aliasing vital signs signals obtained by piezoelectric sensor to obtain target vital signs signals; performing a Fourier transform to obtain first frequency response, and generating an upper envelope according to each frequency point of the first frequency response; determining main peak frequency point and main peak amplitude according to the frequency point corresponding to the maximum value of flat tops; identifying the flat top corresponding to the main peak amplitude as main peak flat top; determining minimum value frequency point according to the minimum value between the main peak flat top and an adjacent flat top or flat bottom; and determining respiratory spectrum principal component interval according to the minimum value frequency point; reconstructing the respiratory spectrum principal component interval through an empirical wavelet function to obtain reconstructed respiratory signal.