Atrial Fibrillation Driver Mapping Using Ranked Electrogram Modifiers
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Solution Overview
Problem
Current mapping systems struggle to accurately identify and prioritize potential atrial fibrillation drivers (AFDs) due to the chaotic nature of activation wavefronts in atrial fibrillation, leading to complex interpretations and low success rates in percutaneous catheter ablation procedures.
Innovation Solution
A computer program and system that analyze electrogram data using modifiers such as cycle length, activation frequency gradients, tissue characteristics, and anatomical features to refine the STAR mapping method, prioritizing potential AFDs by applying weighting factors and visual indicators to guide catheter placement for targeted ablation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If panoramic mapping techniques are used to identify AF drivers, then the coverage of the atrial chamber is improved, but the complexity of interpretation increases due to chaotic activation wavefronts
Solution Approach 1:
The patent transforms the complex temporal activation data into a spatial probability map by changing the parameter representation from time-domain electrogram signals to space-domain probability distributions. This allows the system to maintain comprehensive chamber coverage while simplifying interpretation through visual probability gradients rather than complex activation sequences
Solution Approach 2:
The patent introduces an intermediary computational layer that processes raw electrogram data through statistical modeling to generate probability maps. This intermediary transformation layer converts difficult-to-interpret activation timing data into intuitive probability visualizations that indicate likely driver locations without requiring direct interpretation of chaotic wavefront patterns
2Measurement precision
If electrogram characteristics are used as surrogate markers for localized drivers, then the identification of AFDs is improved, but the reliability decreases due to lack of spatiotemporal stability
Solution Approach 1:
The patent performs preliminary statistical analysis and modeling on electrogram data before final driver identification. By pre-processing the data to establish probability distributions and spatiotemporal patterns, the system creates a more reliable foundation for identifying AFDs that accounts for the inherent instability of activation patterns
Solution Approach 2:
The patent applies multiple modifying factors and weighting schemes beyond simple electrogram characteristics to compensate for reliability issues. By incorporating additional parameters and performing excessive analysis steps, the system overcomes the limitations of unstable electrogram markers through cumulative evidence from multiple sources
3Speed
If rapidity markers are used to identify driver sites, then the speed of identification is improved, but the accuracy decreases as a poor predictor of sites that support AF
Solution Approach 1:
The patent merges rapidity markers with multiple other parameters including activation timing, probability distributions, and modifying factors. By combining these different types of data, the system maintains the speed advantage of rapidity assessment while compensating for its inaccuracy through integration with more reliable predictive parameters
4Measurement precision
If multiple modifying factors are applied to classify earliest activating sites, then the precision of AFD identification is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct modules: data acquisition, probability mapping, modifier calculation, and final classification. By dividing the complex analysis into separate computational stages, the system achieves high precision through multiple factors while managing complexity through structured organization of computational tasks
Data Source
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AI summary
A computer implemented method and system are described that identify one or more regions of the heart responsible for supporting or initiating abnormal heart rhythms. Electrogram data is used that has been recorded from a plurality of electrodes on multipolar cardiac catheters obtained from a corresponding series of sensing locations on the heart over a recording time period. The method includes the steps of: identifying, from the electrograms, regions within a chamber of the heart which have electrical activation sequences which characterize them as potential drivers of abnormal heart rhythms, for each sensing location at or substantially about said region, determining from predominant activations, an earliest activating electrode site; for each determined earliest activating electrode site: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined from the electrogram data for the site, tissue characteristics of the site or anatomical characteristics of the site; determining a ranking factor calculated from the plurality of modifiers; ranking the each of said earliest activating electrode sites in dependence on its ranking factor; and, outputting data identifying said regions, the data varying prominence of each of the determined earliest activating electrode sites in dependence on said ranking.