Automatic Detection of Slow-Conduction Areas in Cardiac Arrhythmias
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Solution Overview
Problem
Current systems fail to provide clear guidance for identifying and characterizing slow-conduction areas in stable arrhythmias, such as atrial flutter, due to complex phenomenology like scar/line of blocked areas, synchronized wave direction, and low-amplitude bipolar signals, requiring time-consuming manual tagging and analysis by physicians.
Innovation Solution
An integrated automatic analysis algorithm that identifies potential slow-conduction areas by using a three-step process: identifying Early Meet Late (EML) areas, selecting relevant LAT bins, and filtering paths based on density of complex tags and activation wave velocities to highlight candidate slow-conduction gaps for ablation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging and analysis methods are used to identify slow-conduction areas, then physicians can characterize arrhythmia tissue, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs automatic analysis of electrophysiological data to identify slow-conduction areas without requiring manual physician tagging. The processor automatically calculates conduction velocities, detects isthmus regions, and generates ablation pathway recommendations, allowing the system to serve itself in the analysis task rather than relying on manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated computational system. The processor uses algorithms to analyze electrophysiological signals, calculate conduction velocities, and identify slow-conduction regions automatically, substituting the manual mechanical process with an electronic computation-based system.
2Manufacturing precision
If comprehensive electrophysiological analysis is performed to accurately characterize slow-conduction areas, then treatment precision is improved, but device complexity increases
Solution Approach 1:
The analysis process is segmented into distinct computational modules: signal acquisition, local activation time calculation, conduction velocity computation, isthmus region detection, and ablation pathway recommendation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and clinically usable while maintaining high precision.
Solution Approach 2:
The electrophysiological mapping system performs multiple functions: it maps cardiac anatomy, records electrophysiological signals, calculates conduction velocities, identifies slow-conduction areas, and recommends ablation pathways. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single integrated platform.
3Difficulty of detecting and measuring
If detailed electrophysiological data processing is implemented to detect slow-conduction gaps, then detection capability is enhanced, but operational complexity increases
Solution Approach 1:
The system automatically processes electrophysiological data, calculates conduction velocities, and identifies slow-conduction regions without requiring complex manual operations. The processor self-services by performing all necessary computations and generating visualizations automatically, simplifying the operator's task while maintaining high detection capability.
Solution Approach 2:
The system uses color-coded visualizations to represent different conduction velocities and tissue characteristics. Slow-conduction areas are highlighted with specific colors or shading, making them easily distinguishable on the anatomical map. This visual encoding simplifies the interpretation of complex electrophysiological data during clinical operation.
Data Source
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AI summary
A method for identifying candidate locations for ablation includes receiving an electrophysiological (EP) map comprising anatomical surface of cardiac chamber overlaid with (i) activation wave velocity vectors, (ii) data points comprising positions on surface and respective local activation times (LAT), and (iii) areas designated by early meet late (EML) LAT range. Set of shortest paths on cardiac surface is identified between different EML areas. One or more ranges of LAT values are selected, being characterized by lowest prevalence over data points of EP map. Complex tags are generated for positions having the LAT values within the one or more ranges of LAT values having lowest prevalence. Subset of the shortest paths is selected based on (i) density of complex tags along shortest paths and (ii) directions of activation wave velocity vectors relative to each of shortest paths. Selected subset of shortest paths are presented as candidate slow-conduction areas for ablation.