Velocity Vector Maps for Cardiac Rhythm Disorder 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 AF drivers, leading to inadequate cardiac ablation procedures and reduced therapeutic success.
Innovation Solution
A system and method that involve receiving electrogram signals, normalizing their amplitudes, generating two-dimensional spatial maps, and processing them to create three-dimensional electrogram surfaces and velocity vector maps, which reveal the location and nature of cardiac rhythm disorders, enabling more precise ablation procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic tools are used for detecting cardiac rhythm disorders, then the diagnostic process is simple, but the measurement precision of source location is insufficient
Solution Approach 1:
The diagnostic system segments the detection process into multiple stages: signal acquisition from multiple electrodes, amplitude normalization, 2D spatial mapping, 3D electrogram surface generation, and velocity vector map creation. Each stage processes specific aspects of the electrogram signals to progressively improve source location precision while managing system complexity through modular functional breakdown.
Solution Approach 2:
The system transitions from 2D spatial mapping to 3D electrogram surface representation, adding a temporal dimension through velocity vector maps. This dimensional escalation enables precise localization of cardiac rhythm disorder sources by visualizing electrical activity in three-dimensional space and time, directly improving measurement precision.
2Measurement precision
If amplitude normalization is applied to electrogram signals, then the detection accuracy of AF drivers is improved, but the processing time is increased
Solution Approach 1:
Amplitude normalization is performed as a preliminary step before generating 3D electrogram surfaces and velocity vector maps. By pre-processing the electrogram signals to equalize amplitude variations across different electrodes and time points, the system prepares optimized input data for subsequent analysis, improving AF driver detection accuracy while reducing the computational burden of later processing stages.
3Measurement precision
If three-dimensional electrogram surfaces are generated from electrogram signals, then the classification accuracy of cardiac rhythm disorders is improved, but the computational complexity is increased
Solution Approach 1:
The system generates 3D electrogram surfaces by adding a spatial dimension to traditional 2D electrogram displays. This dimensional transformation enables visualization of electrical activity distribution across the cardiac chamber, improving classification accuracy by revealing spatial patterns of arrhythmia sources that are not apparent in conventional representations.
Solution Approach 2:
The 3D electrogram surfaces act as an intermediary representation between raw electrogram signals and final diagnostic conclusions. This intermediate visualization layer translates complex multi-electrode signal data into intuitive spatial maps, facilitating accurate disorder classification while managing computational complexity through structured data transformation.
4Reliability
If velocity vector maps are created to reveal source locations, then the therapeutic efficacy of ablation procedures is improved, but the device complexity is increased
Solution Approach 1:
Velocity vector maps add a dynamic temporal dimension to the spatial 3D electrogram surfaces, showing the direction and speed of electrical wave propagation. This enables precise identification of rotor locations and rotation directions, directly improving ablation procedure efficacy by guiding therapists to exact target sites for arrhythmia source elimination.
Solution Approach 2:
The velocity vector maps provide real-time feedback on electrical activity patterns, allowing therapists to adjust ablation strategy based on observed wave propagation characteristics. This feedback loop ensures accurate targeting of arrhythmia sources, improving therapeutic reliability while the systematic approach to map generation manages the complexity of the mapping system.
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. Also described and disclosed are various intra-cardiac catheter-based navigation systems.