Respiratory Effort Detection Using Continuous Wavelet Transform Scalograms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current signal processing technologies face challenges in accurately determining respiratory effort from photoplethysmograph (PPG) signals, particularly in differentiating breathing effort and other physiological signals, which is crucial for monitoring patient health and detecting conditions like apneic events.
Innovation Solution
The use of continuous wavelet transforms to represent PPG signals in a scalogram or spectrogram domain, allowing for the extraction of respiratory effort by analyzing changes in signal features such as energy, amplitude, and phase, and correlating these with breathing frequency, enabling graphical and quantitative representation of effort and setting thresholds for alarm triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used to analyze PPG signals, then the analysis is simpler and faster, but the ability to accurately determine respiratory effort and differentiate breathing effort from other physiological signals deteriorates
Solution Approach 1:
The patent applies continuous wavelet transform to convert the PPG signal from the time domain to the time-frequency domain, creating a scalogram representation. This dimensional transformation enables simultaneous analysis of temporal and spectral characteristics, allowing accurate identification of respiratory effort while maintaining computational efficiency through the wavelet basis functions that adapt to signal features.
2Reliability
If the PPG signal is processed to extract respiratory effort features, then monitoring capability is enhanced, but the complexity of signal processing and feature extraction increases
Solution Approach 1:
The patent extracts specific features from the scalogram representation, including energy, amplitude, and phase characteristics at different frequency bands. By isolating these specific features rather than processing the entire signal, the system achieves reliable respiratory effort monitoring while reducing computational complexity through feature-selective analysis.
Solution Approach 2:
The patent segments the frequency spectrum into distinct bands (e.g., respiratory frequency band, heart rate band) and analyzes each band separately using wavelet transform. This segmentation allows targeted extraction of respiratory effort features without processing the entire signal spectrum, improving monitoring reliability while managing computational complexity through selective analysis of relevant frequency ranges.
3Measurement precision
If continuous wavelet transform is applied to analyze PPG signals, then respiratory effort can be accurately determined through scalogram analysis, but computational requirements and processing time increase
Solution Approach 1:
The patent applies wavelet transform selectively to specific frequency bands of interest (such as the respiratory frequency band) rather than processing the entire frequency spectrum. This partial action approach maintains measurement precision for respiratory effort while reducing computational time by avoiding unnecessary processing of irrelevant frequency components.
Data Source
AI summary
One or more respiratory characteristics of a patient are measured by coupling patient monitor apparatus (e.g., a photoplethysmograph (“PPG”)) to the patient in order to produce a patient monitor signal that includes signal indicia indicative of effort the patient is exerting to breathe. A breathing or respiratory effort signal for the patient is extracted from the patient monitor signal. A respiratory characteristic signal is extracted (at least in part) from the effort signal. This may be done, for example, on the basis of an amplitude feature of the effort signal and a relative time of occurrence of that amplitude feature. Alternatively, the respiratory characteristic signal may be based on a relationship between two amplitude features of the effort signal, with or without regard for specifics of the times of occurrence of those amplitude features. A breath air flow meter may also be coupled to the patient, if desired, in order to produce a flow signal. One or more of the respiratory characteristic measures may also be partly based on the flow signal.