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, facilitating accurate feature detection and data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing techniques are used to process physiological signals from ambulatory subjects, then the processing is simpler and faster, but the signal accuracy deteriorates due to noise, particularly in-band noise
Solution Approach 1:
The patent transforms the physiological signal from the time domain to the frequency domain using Fourier transform, and then to the time-frequency domain using wavelet transform. This dimensional transformation enables separation of signal and noise components that are overlapping in the time domain, particularly in-band noise, thereby improving signal accuracy without proportionally increasing processing complexity
Solution Approach 2:
The patent segments the physiological signal into multiple frequency bands using wavelet transform decomposition. By dividing the signal into different frequency components, the algorithm can selectively process and denoise specific bands while preserving others, improving overall signal accuracy through targeted processing rather than uniform processing of the entire signal
2Productivity
If automated analysis algorithms are used to extract information from noisy signals, then labor and costs are reduced, but the detection accuracy deteriorates with excessive false positives
Solution Approach 1:
The patent implements an iterative feedback mechanism where the denoised signal is continuously refined through multiple wavelet decomposition and reconstruction cycles. The algorithm compares the denoised signal with the original, identifies residual noise, and applies additional processing cycles to progressively improve detection accuracy while maintaining automation efficiency
Solution Approach 2:
The patent dynamically adjusts wavelet decomposition levels, threshold values, and reconstruction parameters based on the signal characteristics and noise levels detected in each processing cycle. This adaptive parameter adjustment enables the automated algorithm to maintain high detection accuracy across varying signal conditions without requiring manual intervention
3Loss of energy
If noise is not removed from physiological signals, then data transmission volume remains higher, but telecommunications costs increase due to excessive data transmission
Solution Approach 1:
The patent extracts and removes noise components from the physiological signal using wavelet thresholding and coefficient selection. By taking out the noise portions of the signal while preserving the essential physiological information, the algorithm reduces the data volume that needs to be transmitted, thereby lowering telecommunications costs without losing critical diagnostic information
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.