Electrographic Flow Mapping for Cardiac Rhythm Disorder Localization
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Solution Overview
Problem
Current diagnostic tools for cardiac rhythm disorders, such as atrial fibrillation, lack precision in locating the sources of AF drivers, leading to inadequate cardiac ablation procedures and reduced therapeutic success rates.
Innovation Solution
Electrographic flow (EGF) mapping technology that reconstructs spatial and temporal electrographic potentials from endocardial unipolar electrogram data, using body surface electrodes to identify and characterize AF drivers, enabling precise localization of cardiac rhythm disorder sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic tools are used for cardiac rhythm disorders, then the diagnostic process is simple, but the precision in locating sources of AF drivers is insufficient
Solution Approach 1:
The patent transitions from traditional 2D electrogram mapping to 3D electrographic flow mapping by adding the temporal dimension through velocity vector calculations. This dimensional enhancement enables precise localization of AF driver sources in three-dimensional space, directly resolving the measurement precision limitation while managing system complexity through computational methods.
Solution Approach 2:
The patent introduces velocity vector maps as an intermediary representation between raw electrogram signals and driver source localization. This intermediary layer processes and transforms the complex signal data into visually interpretable flow patterns, enabling precise source identification without requiring direct complex interaction with the raw signals.
2Reliability
If traditional mapping methods are used, then the procedure is less complex, but the therapeutic success rate of cardiac ablation is reduced
Solution Approach 1:
The patent performs preliminary characterization of AF drivers through velocity vector map analysis before conducting cardiac ablation procedures. By pre-identifying and localizing driver sources with high precision, the system enables targeted ablation strategies that improve therapeutic success rates while reducing the complexity of the ablation procedure itself through better upfront planning.
Solution Approach 2:
The patent implements a feedback mechanism where velocity vector maps provide real-time information about driver source locations and characteristics, guiding the ablation procedure dynamically. This feedback loop ensures that ablation energy is delivered precisely to the identified driver sources, thereby improving therapeutic success rates while maintaining procedural efficiency.
3Object-affected harmful factors
If body surface electrodes are used instead of intracardiac electrodes, then the invasiveness is reduced, but the precision of source localization may be affected
Solution Approach 1:
The patent replaces the mechanical intracardiac electrode system with a body surface electrode system combined with computational velocity vector analysis. This substitution uses mathematical modeling and signal processing algorithms to achieve precise source localization from external measurements, eliminating the need for invasive heart penetration while maintaining or improving localization accuracy through enhanced computational methods.
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. In some embodiments, electrogram signals are acquired from a patient's body surface, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector map corresponding at least partially to the 2D map. The resulting velocity vector map maybe employed to classify a patient as one of an A-type patient, a B-type patient, and a C-type patient, and to guide therapy subsequently delivered to the patient. Trained atrial discriminative machine learning models that facilitate the foregoing systems and methods, and that provide predictions or results concerning a patient's condition, are also disclosed.