3D Electrogram Surfaces for Cardiac Rhythm Source Detection
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Solution Overview
Problem
Current diagnostic tools for cardiac rhythm disorders, such as atrial fibrillation, lack precision in determining the source and location of drivers, leading to inadequate cardiac ablation procedures and reduced therapeutic success rates.
Innovation Solution
A system and method that processes intracardiac electrogram signals to generate three-dimensional electrogram surfaces and velocity vector maps, enabling accurate detection and classification of cardiac rhythm disorders, including active rotors and other sources, to guide targeted treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic tools are used to detect cardiac rhythm disorder sources, then the diagnostic process is simple, but the measurement precision is insufficient
Solution Approach 1:
The patent transitions from traditional two-dimensional electrogram mapping to three-dimensional electrogram surfaces and velocity vector maps. This dimensional expansion allows precise localization of cardiac rhythm disorder sources in 3D space, resolving the technical contradiction by providing superior measurement precision through enhanced spatial representation.
Solution Approach 2:
The patent segments the cardiac electrical activity into discrete electrogram signals from multiple electrodes, then processes these segmented signals to generate 3D electrogram surfaces and velocity vector maps. This segmentation approach enables precise source localization by analyzing individual signal components and their spatial-temporal relationships.
2Measurement precision
If traditional electrogram signal processing is used, then the processing method is simple, but the detection precision of active rotors and sources is insufficient
Solution Approach 1:
The patent introduces 3D electrogram surfaces and velocity vector maps as additional dimensional representations of cardiac electrical activity. This dimensional enhancement enables precise detection of active rotors and sources by visualizing spatial-temporal patterns that are invisible in traditional 2D electrograms, directly resolving the detection precision problem.
Solution Approach 2:
The patent employs 3D electrogram surfaces and velocity vector maps as intermediary representations between raw electrogram signals and source detection. These intermediary visualizations transform complex multi-electrode signals into intuitive spatial-temporal patterns, enabling accurate identification of active rotors and drivers while managing processing complexity.
3Reliability
If cardiac ablation procedures are performed without precise source identification, then the treatment can be applied broadly, but the therapeutic success rate is reduced
Solution Approach 1:
The patent performs preliminary 3D mapping and velocity vector analysis before cardiac ablation procedures. This preliminary action precisely identifies the locations and types of drivers (active rotors, breakthrough points, etc.), enabling targeted ablation that improves therapeutic success rates by ensuring ablation is delivered to the correct anatomical targets.
Solution Approach 2:
The patent provides real-time feedback through 3D electrogram surfaces and velocity vector maps during electrophysiological studies. This feedback mechanism allows operators to continuously monitor driver locations and adjust ablation strategy accordingly, improving therapeutic reliability by ensuring accurate target identification and treatment delivery.
Data Source
AI summary
Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart, such as atrial fibrillation, and to classify same. Velocity vector maps reveal the location of the source of the at least one cardiac rhythm disorder in the patient's heart, which may be, by way of example, an active rotor in the patient's myocardium and atrium. The resulting velocity vector map may be further processed and/or analyzed to classify the nature of the patient's cardiac rhythm disorder, e.g., as Type A, B or C atrial fibrillation. The resulting cardiac rhythm classification then can be used to determine the optimal, most efficacious and/or most economic treatment or surgical procedure that should be provided to the individual patient. A simple and computationally efficient intra-cardiac catheter-based navigation system is also described.