Electrographical Flow Maps for Atrial Fibrillation Source Localization
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Solution Overview
Problem
Current diagnostic tools for cardiac rhythm disorders, such as atrial fibrillation, lack precision in identifying the source and type of AF drivers, leading to inadequate cardiac ablation procedures and suboptimal therapeutic outcomes.
Innovation Solution
The development of systems and methods that utilize electrogram signals to generate electrographical flow maps, allowing for the determination of source activity levels, flow angle variability, and active fractionation levels, which are then combined to calculate an Electrographical Volatility Index (EVI) to estimate the probability of a patient being free from atrial fibrillation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic tools are used to detect AF drivers, then the basic location can be identified, but the precision and accuracy of source identification is insufficient
Solution Approach 1:
The patent segments the electrogram signal analysis into multiple independent components: source activity levels, flow angle variability, and active fractionation levels. Each component is processed separately through dedicated algorithms, allowing precise identification of different AF driver characteristics without interference from other parameters.
Solution Approach 2:
The patent transitions from traditional single-parameter detection to multi-dimensional analysis by introducing electrographical flow mapping that simultaneously measures spatial location, temporal characteristics, and directional flow information. This dimensional expansion enables precise differentiation between various AF driver types.
2Measurement precision
If more comprehensive analysis of AF sources is performed, then classification accuracy improves, but processing complexity increases
Solution Approach 1:
The complex classification task is divided into three independent analysis modules: source activity level detection, flow angle variability measurement, and active fractionation assessment. Each module processes specific signal features separately, reducing the computational burden on any single algorithm while achieving comprehensive classification through combination of results.
Solution Approach 2:
The patent creates a simplified representation model (electrographical flow map) that copies and transforms the complex electrogram data into a more manageable format containing extracted features. This copied representation can be processed more efficiently while preserving the essential characteristics needed for accurate AF source classification.
3Reliability
If precise localization of AF sources is achieved, then ablation procedure effectiveness improves, but the frequency and extent of ablation procedures may increase
Solution Approach 1:
The patent implements a feedback mechanism where the electrographical flow mapping results are used to guide and optimize ablation procedures. By providing real-time information about source activity levels and flow patterns, the system enables more targeted and effective ablation, reducing the need for repeated procedures and minimizing overall lesion load.
Solution Approach 2:
The patent performs preliminary comprehensive mapping and classification before initiating ablation procedures. This preliminary analysis identifies the most critical AF drivers and prioritizes treatment targets, allowing for more efficient ablation procedures that achieve better outcomes with fewer and less extensive interventions.
Data Source
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AI summary
Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect the locations of sources of cardiac rhythm disorders in a patient's heart, and then to generate an estimate or probability of the patient being free from atrial fibrillation. The various embodiments employ at least one computing device to process a plurality of electrogram surfaces through time to generate at least one electrographical flow (EGF) map, representation, pattern, or data set, and then process the at least one EGF map, representation, pattern, or data set to determine at least two of source activity levels, flow angle variability (FAV) levels, and active fractionation (AFR) levels corresponding thereto. On the basis of a combination of the determined at least two of source activity levels, FAV levels, and AFR levels, an electrographical volatility index (EVI) score or metric representative of the estimate or probability of the patient being free from AF is generated.