Diagnostic Signal Processing via Irregular Frequency Spectrum
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Solution Overview
Problem
Current signal processing methods, particularly Fourier methods, face limitations in accurately analyzing signals with continuous or closely spaced frequencies, leading to spectral broadening and inadequate frequency resolution, especially in medical applications involving nonstationary signals and digitization errors.
Innovation Solution
A time-to-frequency domain transform is employed to generate a spectrum with non-zero values at irregularly spaced frequency intervals, allowing for a more accurate representation of real spectra, improved frequency resolution, and reduced digitization errors, enabling analysis of signals with changing spectral content and those affected by digitization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Fourier methods are used for spectral analysis, then the analysis process is simple and well-established, but spectral broadening occurs and frequency resolution is inadequate for continuous or closely spaced frequencies
Solution Approach 1:
The patent changes the fundamental parameter of frequency spacing from uniform (Fourier) to non-uniform (logarithmic or linear spacing). This allows the spectrum to be represented at arbitrarily spaced frequency points, resolving the spectral broadening issue while maintaining computational feasibility through iterative optimization methods.
Solution Approach 2:
The patent replaces the traditional Fourier mechanical transform approach with an iterative optimization-based spectral estimation method. Instead of directly transforming the signal, the system iteratively adjusts spectral parameters to minimize the difference between the synthesized and actual signals, achieving higher frequency resolution without spectral broadening.
2Loss of time
If the signal duration is limited to less than a period of a signal component, then real-time analysis is enabled, but traditional Fourier methods cannot provide accurate spectral information
Solution Approach 1:
The patent performs preliminary spectral estimation using the available signal portion, then iteratively refines the spectral parameters. This allows meaningful spectral information to be extracted from incomplete signal periods, enabling real-time analysis while maintaining spectral accuracy through subsequent optimization iterations.
Solution Approach 2:
The patent changes the approach from requiring complete signal periods to working with partial periods by using iterative optimization. The system estimates spectral parameters from the available data and refines them iteratively, eliminating the constraint that the signal must contain complete periods for accurate spectral analysis.
3Ease of manufacture
If digitization is performed with limited precision, then the system is practical for real-world applications, but digitization errors are introduced that degrade spectral accuracy
Solution Approach 1:
The patent introduces feedback through iterative optimization where the synthesized signal from the estimated spectrum is continuously compared with the actual digitized signal. This feedback loop allows the system to compensate for digitization errors by adjusting spectral parameters to minimize the difference between synthesized and actual signals, thereby recovering spectral accuracy despite limited digitization precision.
4Productivity
If conventional spectral analysis methods are used, then the processing is computationally efficient, but they cannot handle nonstationary signals with changing spectral content
Solution Approach 1:
The patent applies dynamics by using iterative optimization that can adapt to changing spectral content. The spectral parameters are not fixed but are continuously refined through iteration, allowing the system to track and analyze nonstationary signals with time-varying spectral characteristics while maintaining computational efficiency through the structured optimization approach.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides enhanced spectral analysis capabilities, reducing spectral broadening and improving frequency resolution, enabling more accurate characterization of signals with continuous frequency ranges and handling nonstationary and digitization errors effectively.
Implementation Method 1
performing a transform on the time-based information to obtain a frequency spectrum including non-zero values at irregularly spaced frequency intervals
Data Source
AI summary
A signal processing utility is disclosed involving time-to-frequency domain transforms for applications including medical diagnostic signal processing. Such transforms can be used to define a continuous spectral density function or other spectral density function including nonzero values at irregularly spaced frequency intervals. The invention thereby enables more accurate representation of certain real spectra and reduced spectral broadening. The utility also accounts for digitization errors associated with analog-to-digital conversion. The invention has particular advantages with respect to medical contexts where the received signal has a changing spectral content as a result of interaction of an interrogating signal with moving physiological material such as blood flowing through an artery.