Cardiac Activation Wave Velocity Clustering for Block Line Detection
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Solution Overview
Problem
Existing methods for analyzing cardiac electrophysiological maps struggle to accurately identify a blocking line of activation waves due to noise in wave velocity measurements, making it difficult to assess the efficacy of ablation treatments for cardiac arrhythmia.
Innovation Solution
Employing k-means clustering algorithm to partition activation wave velocities into two clusters, identifying a discontinuity line as a blocking line by minimizing variance, which is then overlaid on the map to assist in evaluating the accuracy and efficacy of ablation procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze cardiac electrophysiological maps, then the analysis process is simple, but the accuracy of identifying blocking lines is poor due to noise in wave velocity measurements
Solution Approach 1:
The patent introduces an intermediary statistical model (Gaussian distribution) that mediates between the noisy wave velocity measurements and the blocking line identification. The model acts as a filter that processes the noisy data through probabilistic reasoning, separating the signal from noise and enabling accurate blocking line detection without requiring direct thresholding of raw measurements
Solution Approach 2:
The patent replaces traditional mechanical/threshold-based analysis methods with a statistical computing approach. Instead of using fixed thresholds or simple signal processing techniques, the invention employs probabilistic models and statistical inference to identify blocking lines, substituting deterministic mechanical methods with stochastic computational methods that are more robust to noise
2Measurement precision
If statistical analysis methods are applied to reduce noise, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The patent changes the parameters of the statistical model adaptively based on the local characteristics of the wave velocity data. By adjusting model parameters such as standard deviation and mean values according to local data patterns, the system achieves high measurement precision without requiring excessively complex computational models, thus balancing accuracy with computational efficiency
3Measurement precision
If k-means clustering is used to partition activation wave velocities, then the blocking line identification becomes more accurate, but the algorithm complexity increases
Solution Approach 1:
The patent applies segmentation by partitioning the cardiac tissue region into distinct clusters based on wave velocity characteristics. The k-means algorithm divides the continuous space into discrete regions separated by blocking lines, creating segmented zones that represent different conduction properties. This segmentation approach transforms the complex problem of blocking line detection into a simpler cluster partitioning task
Data Source
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AI summary
A method includes receiving a set of data points including positions and respective velocities of an activation wave in a tissue region of a cardiac chamber. The set is partitioned into at least two velocity clusters, each velocity cluster characterized by a respective velocity of the activation wave. One or more border curves are estimated, between the at least two clusters. The one or more border curves are indicated to a user as possible lines of block of the activation wave.