Atrial Fibrillation Mapping via Finite Element Model and Neural Network
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Solution Overview
Problem
Current methods for treating atrial fibrillation lack precision and predictability due to the absence of a reliable methodology for analyzing and mapping the condition, making catheter ablation procedures less effective.
Innovation Solution
A system utilizing a finite element model (FEM) of the heart, combined with electrogram data and an artificial neural network (ANN), to generate function parameters that enable precise identification and characterization of atrial fibrillation, predicting occurrences, and determining appropriate treatments such as drug delivery and ablation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If catheter ablation is used to treat atrial fibrillation, then treatment can be applied to terminate the arrhythmia, but the procedure lacks precision and predictability due to absence of accurate analysis methodology
Solution Approach 1:
The heart is divided into multiple discrete elements within a finite element model, allowing individual analysis of each element's electrophysiological properties. This segmentation enables precise localization of abnormal electrical activity and facilitates targeted ablation therapy by treating specific elements rather than large areas.
Solution Approach 2:
The system calculates multiple function parameters for each FEM element, including activation time, dominant frequency, and AF burden. These parameter changes transform raw electrogram data into quantifiable metrics that enable precise characterization of AF and guide treatment decisions with improved reliability.
2Ease of operation
If traditional catheter ablation procedures are performed based on doctor judgment, then treatment can be delivered, but the procedure is not precise or predictable
Solution Approach 1:
An artificial neural network serves as an intermediary between raw electrogram data and treatment decisions. The ANN automatically processes complex electrogram signals and calculates function parameters, eliminating the need for complex manual analysis while providing precise, objective guidance for ablation location and energy delivery.
Solution Approach 2:
The system replaces subjective doctor judgment with an automated computational approach using finite element modeling and artificial neural networks. This substitution transforms the procedure from a manual, judgment-based process to an automated, data-driven system that provides precise and predictable ablation guidance.
3Loss of time
If no accurate methodology is used to determine ablation parameters, then treatment can proceed without complex analysis, but the procedure lacks predictability
Solution Approach 1:
The system performs preliminary calculation of function parameters for all FEM elements before ablation begins. By pre-calculating activation times, dominant frequencies, and AF burden for each element, the system establishes a complete treatment map in advance, enabling predictable and systematic ablation delivery without time-consuming intra-procedural analysis.
Data Source
AI summary
A method and system for atrial fibrillation analysis, characterization, and mapping is disclosed. A finite element model (FEM) representing a physical structure of a heart is generated. Electrogram data can be sensed at various locations in the heart using an electrophysiology catheter, and the electrogram data is mapped to the elements of the FEM. Function parameters, which measure some characteristics of AF arrhythmia, are then simultaneously calculated for all of the elements of the FEM based on the electrogram data mapped to the elements of the FEM. An artificial neural network (ANN) can be used to calculate the function parameters.