Cardiac Activation Velocity Clustering for Line-of-Block Detection
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Solution Overview
Problem
Existing methods for analyzing cardiac electrophysiological maps struggle to accurately identify blocking lines of activation waves due to noise and complexity in wave velocity measurements, making it difficult to assess the efficacy of ablation treatments for cardiac arrhythmias.
Innovation Solution
Applying k-means clustering to partition activation wave velocities into clusters and estimating border curves to identify discontinuities, which are indicated as possible blocking lines on a surface representation of the cardiac chamber, using a processor and interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis and clustering methods are applied to velocity data, then the precision of blocking line identification is improved, but the complexity of data processing increases
Solution Approach 1:
The patent introduces clustering algorithms as an intermediary processing layer between raw velocity measurements and blocking line identification. The k-means clustering method acts as a mediator that groups velocity data points into distinct clusters, making the discontinuities more apparent and easier to identify as blocking lines, thereby improving identification accuracy while managing processing complexity through automated statistical methods
Solution Approach 2:
The patent replaces manual visual inspection and subjective interpretation of velocity maps with automated statistical analysis and clustering algorithms. This substitution of mechanical/manual analysis with computational methods objectively identifies blocking lines based on velocity discontinuities, significantly improving measurement precision and consistency while the automation handles the processing complexity
2Reliability
If k-means clustering is applied to partition velocity data, then the reliability of blocking line detection is improved, but the computational time increases
Solution Approach 1:
The patent applies k-means clustering with a predetermined number of clusters (typically 2) rather than exhaustively analyzing all possible cluster configurations. This partial action approach focuses computational resources on the most relevant discontinuities in velocity data, improving detection reliability for actual blocking lines while avoiding unnecessary computational overhead from excessive clustering iterations
Solution Approach 2:
The clustering algorithm performs self-organization of velocity data points into clusters based on their velocity characteristics, automatically identifying discontinuities without requiring manual intervention or complex preprocessing. The algorithm serves itself by iteratively assigning data points to clusters and updating cluster centers, thereby improving detection reliability while minimizing the need for external computational control
Data Source
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.