Cardiac Activation Pattern Classification via Similarity Measures
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Solution Overview
Problem
Current cardiac mapping systems face challenges in identifying and classifying complex activation signal patterns during electrophysiological studies, making it difficult to diagnose and treat heart rhythm disorders effectively.
Innovation Solution
A method involving a system with multiple mapping electrodes that senses activation signals, generates similarity measures between patterns, classifies them into groups based on these measures, and displays characteristic representations to aid in pattern identification and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If activation patterns are displayed as voltage-based activation maps, then electrical signals can be visualized, but the complex and varying patterns become increasingly difficult to identify and classify
Solution Approach 1:
The patent creates simplified copies of complex activation patterns by generating representative patterns that capture the essential characteristics of each pattern class. These representative patterns serve as templates that make it easier to identify and classify the original complex patterns without losing critical diagnostic information.
Solution Approach 2:
The patent transforms the representation parameters of activation patterns by converting raw voltage signals into classified pattern categories with prevalence measurements. This parameter transformation simplifies the data structure while preserving the essential diagnostic features needed for pattern identification.
2Loss of information
If all activation signals are analyzed in detail, then comprehensive diagnostic information is obtained, but the overall survey of patient health becomes overwhelming and difficult to interpret
Solution Approach 1:
The patent merges individual activation signal analyses into grouped pattern classifications. By combining multiple similar patterns into unified pattern classes with aggregated prevalence measurements, the system provides both detailed diagnostic information and an interpretable overall survey of patient health.
Solution Approach 2:
The patent replaces manual analysis of complex activation signals with automated pattern recognition and classification algorithms. This substitution transforms the overwhelming detailed data into structured, interpretable pattern prevalence information that maintains diagnostic completeness while improving ease of interpretation.
3Measurement precision
If multiple pattern classification groups are created to capture pattern variations, then pattern identification accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the continuous space of activation patterns into discrete pattern classification groups. This segmentation enables precise pattern identification by categorizing complex variations into manageable classes, while the systematic approach to segmentation keeps the overall system complexity controlled and interpretable.
Data Source
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AI summary
A system and method for mapping an anatomical structure includes sensing activation signals of physiological activity with a plurality of mapping electrodes disposed in or near the anatomical structure. Patterns among the sensed activation signals are identified based on a similarity measure generated between each unique pair of identified patterns which are classified into groups based on a correlation between the corresponding pairs of similarity measures. A characteristic representation is determined for each group of similarity measures and displayed as a summary plot of the characteristic representations.