Physiological Signal Denoising Using Multi-Domain Decomposition
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Solution Overview
Problem
Current physiological signal processing techniques face challenges in accurately extracting information from ambulatory subjects due to noise, particularly in-band noise, which complicates the separation of signal sources and leads to false positives, excessive data transmission, and high telecommunications costs.
Innovation Solution
The implementation of Multi-Domain Signal Processing (MDSP) techniques, which decompose signals into higher-dimensional subcomponents, apply spatially selective filtering or principal component analysis to identify and remove noise, and reconstruct denoised signals, while also computing a dynamic signal-to-noise ratio for improved accuracy and feature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current physiological signal processing techniques are used, then device complexity is reduced, but measurement precision deteriorates due to noise and false positives
Solution Approach 1:
The patent segments the physiological signal into multiple frequency bands using filter banks, allowing separate processing of different signal components. This segmentation enables targeted noise removal while preserving important physiological information, thereby improving measurement precision without requiring complete redesign of the entire processing system.
Solution Approach 2:
The patent transforms the signal from the time domain to the frequency domain using wavelet transforms and filter banks. This dimensional change allows noise and signal components to be separated more effectively in the frequency domain, improving measurement precision while the modular transformation algorithms keep implementation complexity manageable.
2Measurement precision
If noise removal techniques are applied, then measurement precision is improved, but data volume increases due to additional processing
Solution Approach 1:
The patent extracts and removes noise components from the physiological signal using wavelet thresholding and frequency-based filtering. By selectively removing only the noise portions while preserving the essential signal data, the technique improves measurement precision without requiring transmission or storage of excessive additional data for correction purposes.
Solution Approach 2:
The patent changes the representation parameters of the signal by applying wavelet transforms and frequency domain conversions. These parameter changes enable more efficient compression of the processed signal, as the transformed data can be stored or transmitted with fewer bits while maintaining improved measurement precision.
3Productivity
If automated processing is used, then productivity is improved, but reliability deteriorates due to false positives from noise
Solution Approach 1:
The patent implements feedback mechanisms where the processed signal is continuously monitored and compared against expected physiological patterns. The noise reduction algorithms use feedback from the signal characteristics to adaptively adjust processing parameters, improving detection reliability while maintaining automated processing efficiency through iterative refinement rather than requiring manual intervention.
Solution Approach 2:
The patent employs dynamic processing where the noise reduction parameters and filter characteristics are continuously adapted based on the incoming signal properties. This dynamic approach allows the automated system to maintain high reliability across varying physiological conditions and noise levels, improving detection accuracy without sacrificing processing speed or requiring manual recalibration.
Data Source
AI summary
Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.