Electroanatomical Map Quality Scoring and Visualization
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Solution Overview
Problem
Existing electroanatomical (EA) map visualization methods lack a general method to estimate and visualize the overall quality of EA maps, leading to potential misinterpretation by physicians due to insufficient data points or excessive interpolation in certain regions.
Innovation Solution
An automated algorithm that scores the quality of EA analysis output at each location on the EA map, using a weighted sum of quality scores from multiple EA analysis algorithms, and visually indicates insufficient quality regions on the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EA maps are generated using multiple EA analysis algorithms, then the diagnostic information is enhanced, but the complexity of quality assessment increases and regions with insufficient data points or excessive interpolation cannot be easily identified
Solution Approach 1:
The patent segments the EA map into multiple regions and assigns a confidence level to each region independently. This allows the system to handle complex quality assessment by breaking it down into manageable regional evaluations, where each region can be assessed separately based on local data density and interpolation characteristics.
Solution Approach 2:
The patent uses visual color coding to represent different confidence levels in various regions of the EA map. High-confidence regions are displayed with one color scheme while low-confidence regions use another, enabling physicians to quickly identify areas with insufficient data or excessive interpolation without complex numerical analysis.
2Measurement precision
If data points are densely collected to improve map quality, then the analysis accuracy is improved, but the time and resources required for mapping increase
Solution Approach 1:
The patent applies partial action by performing exhaustive data collection only in regions where it is most needed. The confidence level assessment identifies specific regions with insufficient data, allowing the system to focus additional measurement efforts on those particular areas rather than uniformly increasing data collection across the entire map.
Solution Approach 2:
The patent implements a feedback mechanism where the confidence level calculation provides information about data sufficiency in each region. This feedback allows the mapping system to adaptively determine where additional data points are needed and where existing data is sufficient, optimizing the balance between measurement precision and time investment.
Data Source
AI summary
A method includes receiving an electroanatomical (EA) map of at least a portion of a cardiac chamber. A quality is scored of an EA analysis of the EA map. Based on the scoring, one or more regions are identified in the mapped portion as failing to meet a defined quality criterion. The one or more regions are indicted on an EA map to be displayed to a user.