Atrial Fibrillation Mapping Using Local Efficiency Graph Analysis
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Solution Overview
Problem
Current methods for locating and classifying the source of atrial fibrillation (AF) are inadequate for optimizing radiofrequency (RF) or irreversible electroporation (IRE) ablation, as they do not effectively account for atrial remodeling, which is crucial for effective treatment strategies.
Innovation Solution
A system using a catheter with closely spaced electrodes to measure local activation times (LATs) and calculate mutual information metrics, generating graphs to estimate local efficiency, and classify AF based on remodeling percentages, facilitating precise ablation targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AF analysis methods are used, then the basic detection of AF is possible, but the classification accuracy based on atrial remodeling is insufficient
Solution Approach 1:
The atrium is divided into multiple regions with closely spaced electrodes (less than 3mm apart), allowing localized measurement of activation times and efficiency metrics for each segment. This segmentation enables precise mapping of remodeling patterns across different atrial regions, resolving the contradiction between classification accuracy and detection complexity by breaking down the complex remodeling assessment into manageable local measurements.
Solution Approach 2:
The patent introduces a new dimension of analysis by calculating local efficiency metrics and mutual information between electrode pairs, transforming traditional 1D activation time measurements into a multi-dimensional characterization of atrial tissue properties. This dimensional expansion enables sophisticated classification of AF types based on remodeling patterns without overwhelming complexity, as the additional dimensions are derived systematically from the electrode network.
2Measurement precision
If closely spaced electrodes are used to measure local activation times, then the spatial resolution is improved, but the device complexity increases
Solution Approach 1:
The catheter with closely spaced electrodes serves multiple functions: measuring local activation times, calculating mutual information metrics, determining local efficiency, and classifying AF types. By making the electrode system multi-functional, the patent justifies the increased device complexity through enhanced capability, as a single electrode array performs both mapping and classification tasks without requiring separate systems.
Solution Approach 2:
The electrode network automatically generates its own classification output by processing the signals from its closely spaced electrodes through computational algorithms. The system self-characterizes the atrial tissue properties and produces AF classification without external intervention, allowing the complexity of the electrode configuration to be offset by the automated processing that transforms the raw data into clinically useful classifications.
3Reliability
If multiple metrics (local efficiency, mutual information) are calculated to classify AF, then the classification robustness is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of local efficiency metrics and mutual information values during the signal acquisition and processing stage, preparing these metrics in advance for the final classification decision. By computing these metrics beforehand and storing them for comparison against threshold values, the system reduces the computational burden during classification while maintaining robust multi-metric analysis, thus improving reliability without proportionally increasing real-time computational complexity.
Data Source
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AI summary
A method, including acquiring, from a plurality of electrodes in contact with heart tissue undergoing atrial fibrillation, respective signals, and calculating from the signals mutual information metrics between pairs of the electrodes. A graph is generated with the electrodes as nodes, and edges as connections therebetween exceeding a selected mutual information metric threshold. Respective local efficiency metrics are calculated for each of the nodes, and the metrics are averaged to formulate a resultant local efficiency for the selected mutual information metric threshold. The resultant local efficiency and the selected mutual information metric threshold are analyzed to classify the atrial fibrillation.