Body Surface Electrode Mapping for Cardiac Rotor Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic tools for cardiac rhythm disorders, such as atrial fibrillation, lack the spatial and temporal resolution to accurately identify and differentiate between active and passive rotors, limiting the effectiveness of radiofrequency ablation therapies.
Innovation Solution
A system using body surface electrodes and a computing device applies electrographic flow methods, video tracking analysis, and atrial discriminative machine learning to classify and detect the location and type of cardiac rhythm disorders, providing enhanced precision in identifying rotor mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current diagnostic tools are used to identify cardiac rhythm disorder sources, then the diagnostic process is simple, but the spatial and temporal resolution is insufficient to accurately differentiate between active and passive rotors
Solution Approach 1:
The diagnostic system segments the analysis by separating spatial mapping (electrode array positions) from temporal analysis (signal processing over time), allowing independent optimization of each dimension's resolution without proportionally increasing overall system complexity
Solution Approach 2:
The system transitions from traditional 2D electrogram display to 3D spatial mapping with temporal dimension, creating a four-dimensional representation (x, y, z, time) that simultaneously improves spatial and temporal resolution while using computational methods to manage the increased data complexity
2Reliability
If radiofrequency ablation is performed without precise localization tools, then the procedure can be performed with simpler equipment, but the success rate of treating atrial fibrillation is limited
Solution Approach 1:
The system provides real-time feedback during ablation procedures by continuously monitoring electrogram signals and updating the 3D map of rotor locations, allowing the ablation team to verify successful rotor elimination and adjust the procedure based on immediate diagnostic information
Solution Approach 2:
The system performs preliminary identification and classification of rotors (active vs. passive) before ablation begins, creating a prioritized target list that guides the ablation procedure and ensures that the most critical drivers are addressed first, thereby improving overall success rates
3Loss of information
If traditional mapping methods are used to identify AF drivers, then the procedure time is reduced, but the ability to discriminate between active and passive rotors is insufficient
Solution Approach 1:
The system replaces manual visual inspection and interpretation of electrogram signals with automated computational algorithms that analyze temporal patterns and classify rotors based on signal characteristics, preserving detailed information without requiring proportional increases in analysis time
Solution Approach 2:
The system transforms electrogram signals into derived parameters such as dominant frequency, phase singularity detection, and wavefront propagation velocity, which encode rotor activity information in a compressed format that is both information-rich and computationally efficient to process
Data Source
Figure 1(a)
Figure 1(b)
Figure 2
AI summary
Disclosed are various examples and embodiments of systems, devices, components and methods configured to classify, and to detect at least one location or type of at least one source of, at least one cardiac rhythm disorder in a patient's heart using one or more body surface electrodes, and/or intracardiac electrodes. Body surface electrogram data, and optionally intracardiac electrode data, representative of cardiac signals acquired from the patient are provided to a computing device, which in turn determines the location and type of the at least one source of the at least one cardiac rhythm disorder in the patient's heart using electrographic flow (EGF) methods, and then classifies same using electrographic volatility index (EVI) methods.