Basis Functions and Wavelet Transforms for PPG Signal Analysis
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Solution Overview
Problem
Current signal processing methods for photoplethysmogram (PPG) signals face challenges in accurately determining physiological information, such as oxygen saturation, due to noise and the need for precise filtering and feature identification, which existing technologies struggle to address effectively.
Innovation Solution
The use of basis functions and continuous wavelet transforms in conjunction, where basis functions refine areas of interest in frequency or time, and continuous wavelet transforms identify maxima ridges in the scalogram, allowing for optimized determination of physiological information by combining results and providing confidence metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basis functions are used to refine areas of interest in frequency or time, then measurement precision of physiological information is improved, but device complexity increases due to the need for iterative cancellation procedures and basis function generation
Solution Approach 1:
The signal processing is divided into distinct segments: basis function generation for frequency/time refinement and wavelet transform for feature identification. Each segment handles specific aspects of signal analysis, allowing complex processing to be broken down into manageable steps that can be executed systematically.
Solution Approach 2:
Basis functions are generated and prepared in advance before the main signal processing. The iterative cancellation procedure is pre-configured with appropriate basis function sets, allowing the actual signal analysis to proceed more efficiently without requiring complex real-time computations.
2Measurement precision
If continuous wavelet transforms are used to identify maxima ridges in the scalogram, then measurement precision of physiological information is improved, but device complexity increases due to the computational requirements of wavelet processing
Solution Approach 1:
The wavelet transform is combined with basis function processing in a unified signal processing framework. The maxima ridge identification from wavelet analysis is integrated with the basis function cancellation results, allowing both methods to complement each other and reduce overall computational complexity through synergistic processing.
Solution Approach 2:
The wavelet transform introduces an additional dimension of analysis by transforming the signal into a time-scale representation. This dimensional change allows for better identification of physiological features in the scalogram, providing enhanced measurement precision through multi-dimensional signal characterization.
3Reliability
If both basis function processing and continuous wavelet transforms are applied to received signals, then reliability of physiological information determination is improved, but device complexity and processing time increase
Solution Approach 1:
The system incorporates feedback mechanisms where the results of basis function processing and wavelet transform are compared and validated against each other. Confidence metrics are calculated based on the agreement between the two processing methods, allowing the system to verify reliability and filter out erroneous results through cross-validation.
Solution Approach 2:
The system dynamically adjusts processing parameters such as the number of basis functions, wavelet scales, and cancellation iterations based on signal quality and computational requirements. This adaptive parameter adjustment allows the system to maintain high reliability while optimizing processing complexity for different physiological conditions.
Data Source
AI summary
According to embodiments, systems and methods are provided that use continuous wavelet transforms and basis functions to provide an optimized system for the determination of physiological information. In an embodiment, the basis functions may be used to refine an area of interest in the signal in frequency or in time, and the continuous wavelet transform may be used to identify a maxima ridge in the scalogram at scales with characteristic frequencies proximal to the frequency or frequencies of interest. In another embodiment, a wavelet transform may be used to identify regions of a signal with the morphology of interest while basis functions may be used to focus on these regions to determine or filter information of interest. In yet another embodiment, basis functions and continuous wavelet transforms may be used concurrently and their results combined to form optimized information or a confidence metric for determined physiological information.