Dynamic Repolarization Substrate Mapping via Spatial Gradient Analysis
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Solution Overview
Problem
Current cardiac mapping technologies face limitations in accurately identifying arrhythmogenic tissue, as they rely on sequential point-by-point measurements and lack advanced visualization tools to differentiate between healthy and abnormal tissue regions.
Innovation Solution
The method involves acquiring and processing electrogram signals from multiple anatomical locations within the heart using a catheter with electrodes, calculating activation recovery intervals, and displaying spatial gradient data in three-dimensional graphical representations to highlight regions with significant rate changes or phase discrepancies, aiding in the identification of arrhythmogenic tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequential point-by-point measurements are used for cardiac mapping, then the measurement process is simplified and can be performed with basic equipment, but the time required for mapping increases significantly and measurement precision decreases
Solution Approach 1:
The mapping procedure is segmented into multiple pacing rates (e.g., 60 bpm, 90 bpm, 120 bpm), with ARIs measured at each rate. This segmentation allows comprehensive tissue characterization without requiring excessively long continuous measurements at a single rate, improving both precision and time efficiency.
Solution Approach 2:
The system uses periodic pacing at different rates to elicit reproducible ARIs at each anatomical location. By performing measurements at multiple periodic intervals (different pacing rates), the system captures dynamic repolarization characteristics that improve arrhythmogenic tissue identification accuracy while managing total procedure time.
2Measurement precision
If multiple pacing rates are used to measure activation recovery intervals, then the precision of arrhythmogenic tissue identification improves through dynamic repolarization assessment, but the complexity of the mapping procedure increases
Solution Approach 1:
The mapping system is designed to perform multiple functions: it can pace at different rates, measure ARIs at each rate, calculate spatial gradients, and generate visualizations. This multi-functionality consolidates what would otherwise require multiple separate devices or procedures into a single integrated system, managing complexity while improving precision.
Solution Approach 2:
The system changes the pacing rate parameter systematically (e.g., 60, 90, 120 bpm) to elicit different repolarization responses. By controlling and systematically varying this single parameter, the system achieves comprehensive tissue characterization without requiring complex multi-parameter manipulation, thus improving precision while managing procedural complexity.
3Measurement precision
If spatial gradient calculations are performed on activation recovery intervals, then the ability to differentiate healthy from abnormal tissue improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system calculates spatial gradients of ARIs at each local anatomical position and pacing rate combination. This local quality analysis identifies regions with abnormal gradient patterns that indicate arrhythmogenic tissue. By focusing calculations on local spatial variations rather than global analysis, the system achieves high tissue differentiation accuracy while managing computational complexity through localized processing.
4Ease of operation
If three-dimensional visualizations of spatial gradient data are displayed, then the ease of identifying arrhythmogenic regions improves, but the device complexity and computational resources required increase
Solution Approach 1:
The system transforms scalar ARI data into three-dimensional spatial gradient visualizations that display magnitude and direction of gradients across the cardiac anatomy. This dimensional transformation provides intuitive visual cues for identifying arrhythmogenic regions, significantly improving ease of operation. The complexity is managed by implementing visualization algorithms that leverage the existing spatial framework already established during the mapping procedure.
Data Source
AI summary
Methods and systems for cardiac mapping are disclosed. An example system includes a catheter shaft with one or more electrodes coupled to a distal end of the catheter shaft. Electrodes sense electrical signals at anatomical locations within a heart. A processor coupled to the catheter shaft acquires electrogram signals of the heart using the electrodes. Each electrogram signal relates to three-dimensional positional data corresponding to the anatomical locations. The processor also store the electrogram signals of the heart corresponding to electrical activities sensed at corresponding anatomical locations, calculate an activation recovery interval associated with each of the corresponding anatomical locations, determine spatial gradient data of the activation recovery interval based on a distance between at least two neighboring anatomical locations. The system also includes a display device to display a three-dimensional graphical representation of the spatial gradient data between the at least two neighboring anatomical.