Lightly Filtered Photoplethysmograph Signal Processing for Respiratory Effort Detection
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Solution Overview
Problem
Current signal processing techniques, such as pulse oximetry, struggle to accurately measure respiratory effort in patients due to noise interference and limited frequency range filtering, which can obscure clinically useful information in photoplethysmograph (PPG) signals.
Innovation Solution
The use of continuous wavelet transforms to represent PPG signals in a scalogram or spectrogram domain allows for the extraction of respiratory effort by analyzing changes in signal features, such as breathing bands, and provides a more comprehensive view of physiological data by lightly filtering PPG signals to include a wider frequency range while removing high-frequency noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If heavy filtering is applied to PPG signals to remove noise, then noise interference is reduced, but clinically useful respiratory effort information is lost
Solution Approach 1:
The patent segments the PPG signal processing into multiple stages: initial light filtering to remove high-frequency noise, followed by continuous wavelet transform to separate respiratory effort components from remaining noise in the time-frequency domain. This multi-stage approach allows selective removal of harmful noise while preserving clinically useful respiratory information.
Solution Approach 2:
The patent transforms the PPG signal from the time domain to the time-frequency domain using continuous wavelet transform. This dimensional change allows respiratory effort information to be visualized and extracted as distinct patterns in the scalogram, separating it from noise that appears at different frequency scales.
2Device complexity
If traditional filtering methods are used to process PPG signals, then signal processing is simple, but respiratory effort detection accuracy is limited
Solution Approach 1:
The patent introduces continuous wavelet transform as an intermediary processing step between signal acquisition and respiratory effort detection. This intermediary transformation enables accurate extraction of respiratory effort by creating a time-frequency representation where respiratory patterns are clearly distinguishable from other signal components.
3Device complexity
If the frequency range of PPG signals is limited, then processing is simpler, but hidden physiological information is obscured
Solution Approach 1:
The patent extends the effective frequency range analysis by transforming the signal into the time-frequency domain. This allows the system to analyze and extract information across a broader spectrum of frequencies that would be difficult to process in the time domain, revealing hidden physiological patterns without proportionally increasing processing complexity.
Data Source
AI summary
One or more physiological conditions of a patient can be observed by obtaining a photoplethysmograph (“PPG”) signal from the patient and by only lightly filtering that signal. The light filtering of the PPG may be such as to only remove (for example) high frequency noise from that signal, while leaving in the signal most or all frequency components that are due to physiological events in the patient. In this way, such physiological events can be observed via a visual display of the lightly filtered PPG signal and/or via other signal processing of the lightly filtered PPG signal to automatically extract certain physiological parameters or characteristics from that signal.