ECG Signal Analysis Using Modified Matching Pursuit for Cardiovascular Tissue Characterization
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Solution Overview
Problem
Current ECG signal processing algorithms have limited diagnostic accuracy, particularly in detecting myocardial infarctions and characterizing cardiovascular tissues, due to their inability to effectively handle aperiodic random noise and non-linear dynamics.
Innovation Solution
The use of modified Matching Pursuit (MMP) algorithm and space-time analysis to derive a noiseless model of ECG data, which is then divided into regions to compute dynamical density and extract complex sub-harmonic frequencies, allowing for the characterization and imaging of cardiovascular tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG signal processing algorithms are used, then the system is simple and easy to operate, but the diagnostic accuracy is limited
Solution Approach 1:
The patent segments the ECG signal processing into multiple stages: preprocessing to remove noise, phase space reconstruction to transform the signal, and multi-scale entropy analysis to extract features. This segmentation allows complex analysis to be broken down into manageable steps, improving diagnostic accuracy while maintaining operational feasibility
Solution Approach 2:
The patent transforms the traditional one-dimensional time-domain ECG signal into a multi-dimensional phase space representation. By reconstructing the phase space and analyzing dynamics in multiple dimensions, the system extracts more information from the same signal, significantly improving diagnostic accuracy without requiring additional hardware
2Measurement precision
If Fourier transform is used for frequency-domain analysis, then the analysis is straightforward, but it cannot detect deterministic chaos in the presence of strong periodicity
Solution Approach 1:
The patent replaces the traditional Fourier transform (linear analysis method) with non-linear dynamic analysis methods including phase space reconstruction and multi-scale entropy analysis. This substitution enables the detection of deterministic chaos and non-linear structures that are obscured by strong periodicity in traditional frequency-domain analysis
Solution Approach 2:
The patent changes the analysis parameters by examining the ECG signal at multiple time scales and in phase space rather than only in the time domain. This parameter transformation allows the detection of subtle non-linear dynamics and chaotic patterns that remain hidden in conventional single-scale time-domain or frequency-domain analyses
3Measurement precision
If model-based analysis is used, then periodicity is assumed which simplifies processing, but it cannot capture aperiodic random noise characteristics
Solution Approach 1:
The patent applies dynamic analysis methods that do not assume periodicity. By using phase space reconstruction and analyzing the dynamic evolution of the signal, the system can characterize aperiodic random noise and non-periodic physiological variations, providing more accurate representation of real ECG signals that inherently contain both periodic and aperiodic components
Data Source
AI summary
The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.