Cardiac Potential Estimation Using Time-Dependent Regularization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electrocardiogram imaging (ECGI) methods face significant challenges due to the ill-conditioned nature of the inverse problem, leading to errors and unrealistic results in estimating cardiac potentials.
Innovation Solution
A computer-implemented method that uses a torso model and a cardiac model with time-dependent weighting regularization coefficients based on the cardiac activation sequence to estimate cardiac potentials, minimizing the difference between body surface potentials and estimated cardiac potentials while promoting high-frequency components in depolarized regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regularization techniques (e.g., Tikhonov regularization) are employed to mitigate the ill-posed nature of the inverse problem, then stability and reliability of the solution is improved, but spatial high-frequency information is lost and the solution becomes spatially smoothed
Solution Approach 1:
The patent applies different regularization strengths to different spatial locations based on the probability of depolarization. Regions with high depolarization probability receive weaker regularization to preserve high-frequency information, while regions with low probability receive stronger regularization for stability. This local differentiation resolves the contradiction by allowing high-frequency preservation where needed while maintaining stability where the inverse problem is most ill-posed.
Solution Approach 2:
The patent uses time-dependent regularization coefficients that dynamically adapt during the cardiac cycle. The regularization strength varies with the cardiac phase, being stronger during stable periods and weaker during high-frequency events. This dynamic adjustment allows the system to maintain reliability when needed while preserving measurement precision during critical cardiac moments.
2Measurement precision
If direct inversion of the forward solution is performed to obtain cardiac potentials, then measurement precision is improved, but errors are amplified and the solution becomes unstable
Solution Approach 1:
The patent changes the regularization parameter from a constant value to a time-dependent coefficient that varies with the cardiac cycle phase. This parameter change allows the system to achieve both precision and stability by adjusting the balance between fidelity to measurements and smoothness of the solution at different temporal moments during cardiac activity.
3Ease of operation
If a constant space-time scalar parameter is used for regularization, then ease of operation is improved, but spatially smoothed features are generated lacking high-frequency information
Solution Approach 1:
The patent transforms the static regularization parameter into a dynamic, time-dependent coefficient that adapts to the cardiac cycle phase. This dynamic approach maintains operational simplicity through a unified framework while significantly improving measurement precision by preserving high-frequency spatial information in regions and time periods where it is most relevant.
Data Source
AI summary
A method for determining the cardiac potential of a subject includes providing as input data a torso model of the subject comprising a plurality of nodes, a cardiac model of the subject comprising a plurality of nodes, a cardiac activation sequence at the plurality of nodes of the cardiac model, and body surface potentials recorded on the torso of the subject; assigning to each node of the cardiac model a time-dependent weighting regularization coefficient based on the cardiac activation sequence; obtaining estimated cardiac potentials; obtaining estimated body surface potentials; determining a correlation coefficient between the estimated body surface potentials and the body surface potentials provided as input data; and determining if a predefined stop condition is reached based on the determined correlation coefficient.