Clustering Wavefront Signals in Electrophysiological Maps
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Solution Overview
Problem
Conventional electrophysiological (EP) cardiac maps present a large amount of graphical information in the form of vectors, making it difficult for physicians, especially inexperienced ones, to interpret and understand the direction and velocity of wavefront signals in cardiac tissue.
Innovation Solution
The method involves clustering wavefront signals in an electrophysiological map by discretizing the map into sections, grouping velocity vectors based on predefined criteria, and generating trend lines representative of each group, thereby simplifying the visualization of wavefront propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If velocity vectors are displayed for each measurement point to show wavefront propagation direction and velocity, then the completeness of wavefront information is improved, but the complexity of interpretation and ease of operation deteriorates
Solution Approach 1:
The electrophysiological map is divided into multiple discrete regions or zones based on spatial coordinates. Each region contains a subset of velocity vectors that are processed independently. This segmentation allows the system to manage large volumes of vector data by breaking them into smaller, more manageable groups that can be represented by regional trend lines rather than displaying every individual vector, thus maintaining information completeness while improving interpretability.
Solution Approach 2:
Trend lines are introduced as intermediary elements that represent groups of velocity vectors. Instead of directly displaying numerous individual vectors that overwhelm the physician, the system computes trend lines that capture the essential propagation characteristics of each vector group. These trend lines serve as simplified mediators that convey the same diagnostic information in a more comprehensible visual format.
2Measurement precision
If the number of velocity vectors is increased to cover more measurement points, then the measurement precision is improved, but the device complexity and difficulty of detecting and measuring increases
Solution Approach 1:
Multiple velocity vectors from different measurement points within the same region are merged into a single trend line representation. The system combines the directional and velocity information from multiple vectors by computing a representative trend line that captures the collective propagation characteristics. This merging reduces the visual complexity of displaying numerous individual vectors while preserving the essential measurement precision through the aggregated trend line data.
Data Source
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AI summary
A system and method for clustering wavefront signals in an electrophysiological map of a cardiac tissue are provided. The system and method include receiving an electrophysiological map of the cardiac tissue, displaying propagation of the wavefront signals as a plurality of velocity vectors, discretizing the received electrophysiological map into a plurality of sections, clustering the velocity vectors into at least one group within each section based on predefined criteria, and generating a trend line representative of each group of clustered velocity vectors within each section in the electrophysiological map.