Local Activation Source Detection Using Gaussian Integrals
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Solution Overview
Problem
Existing electro-anatomical maps of the heart during atrial fibrillation are overloaded with vectors, obscuring the pattern of local activation sources, making it difficult for physicians to identify and treat focal points causing arrhythmias.
Innovation Solution
A catheter-based system with sensing electrodes and position sensors generates electrocardiogram signals and position data, using a processor to calculate gaussian integrals of propagation vector-fields within defined virtual closed contours to pinpoint the location of local activation sources, and display tags on an electro-anatomical map for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If electro-anatomical map displays all propagation vectors to show comprehensive electrophysiologic signal information, then the completeness of data representation is improved, but the map becomes overloaded and obscures the pattern of local activation sources
Solution Approach 1:
The patent segments the continuous vector field data into discrete circulation patterns by identifying closed loops in the propagation vectors. This segmentation transforms the overwhelming continuous data into manageable discrete entities (circulation patterns) that can be easily detected and analyzed, resolving the contradiction between data completeness and detectability
Solution Approach 2:
The patent extracts the essential feature (circulation patterns) from the complex vector field data by applying circulation criteria to identify and highlight only the relevant structures. This extraction removes unnecessary visual clutter while preserving the critical information about local activation sources, thereby improving pattern detectability without losing important diagnostic data
2Measurement precision
If high density mapping is performed to improve detection precision of activation sources, then the measurement precision is improved, but the complexity of vector field analysis increases making patterns harder to identify
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze complex high-density mapping data through automated circulation pattern detection algorithms. The computer automatically identifies closed loops and calculates circulation criteria without requiring manual analysis, thereby maintaining high measurement precision while reducing the operational complexity for physicians
Solution Approach 2:
The patent changes the analysis parameter from examining individual vectors to calculating circulation criteria (such as circulation index or circulation area) for defined regions. This parameter transformation simplifies the interpretation of high-density data by converting complex vector orientations into scalar values that indicate the presence and strength of local activation sources
Data Source
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AI summary
A system includes a processor and a display. The processor is configured to: (i) produce an electroanatomic (EA) map of a surface of the organ, the EA map specifying a propagation vector-field (PVF) indicative of propagation of an electrophysiological (EP) wave over at least the surface, (ii) select at least a region of the organ, (iii) calculate a gaussian integral of the PVF along a perimeter of at least a closed contour indicative of the selected region, and (iii) produce at the region, a tag indicative of the LAS, in case the calculated gaussian integral meets a criterion. The display is configured to display the EA map and at least the tag over the EA map.