EA Map Electrogram Visualization for Arrhythmia Analysis
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Solution Overview
Problem
Electrophysiologists face challenges in assessing regional arrhythmogenic characteristics from intracardiac electrograms, as a large number of electrograms acquired by a multi-electrode catheter are often irrelevant, making it difficult to identify and visualize the relevant arrhythmogenic activity.
Innovation Solution
A processor-based system that analyzes intracardiac electrograms and electro-anatomical maps to automatically identify a subset of electrograms that best illustrate regional arrhythmia characteristics. This system graphically connects the identified arrhythmogenic activity on the EA map to the corresponding electrograms, using arrows and annotations to visually emphasize the characteristics of arrhythmogenic activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all intracardiac electrograms are displayed to provide complete information, then information completeness is improved, but information overload and difficulty in identifying relevant arrhythmogenic activity increases
Solution Approach 1:
The system extracts and displays only the relevant subset of electrograms that contain arrhythmogenic activity, separating useful information from irrelevant data. The processor automatically identifies and extracts electrograms showing arrhythmia characteristics, presenting them to the user without requiring manual selection from all acquired electrograms.
Solution Approach 2:
Different visual representations are applied to different electrograms based on their relevance. Relevant electrograms showing arrhythmogenic activity are highlighted with special visual indicators, while irrelevant electrograms are either dimmed or excluded from display, creating local quality differences that guide user attention.
2Reliability
If a large number of electrograms are acquired to ensure comprehensive coverage, then diagnostic accuracy is improved, but data complexity and processing difficulty increases
Solution Approach 1:
The system continuously monitors incoming electrogram data and provides real-time feedback by automatically identifying and highlighting electrograms that exhibit arrhythmogenic characteristics. This feedback mechanism allows the system to maintain diagnostic accuracy by focusing on relevant data without requiring the user to manually process all acquired electrograms.
Solution Approach 2:
The system performs self-service by automatically filtering and organizing electrograms based on their relevance to arrhythmia detection. The processor autonomously identifies patterns indicative of arrhythmogenic activity and presents them in an organized manner, eliminating the need for complex manual data processing by the user.
3Ease of operation
If manual selection of relevant electrograms is performed to reduce data volume, then ease of visualization is improved, but time consumption and operator workload increases
Solution Approach 1:
The system automatically performs the selection task that would otherwise require manual operator intervention. The processor analyzes incoming electrogram data in real-time and autonomously identifies relevant electrograms showing arrhythmogenic activity, eliminating the time-consuming manual selection process while maintaining ease of visualization.
Solution Approach 2:
The system performs preliminary analysis and filtering of electrograms before they are presented to the user. By pre-identifying and organizing relevant electrograms in advance, the system eliminates the need for time-consuming manual selection during the diagnostic process.
4Reliability
If all electrograms are analyzed to ensure complete arrhythmia detection, then diagnostic thoroughness is improved, but processing time and computational load increases
Solution Approach 1:
The system extracts only the electrograms that contain diagnostically relevant arrhythmogenic activity, excluding irrelevant data from further analysis. This extraction approach maintains diagnostic thoroughness by focusing computational resources on identifying and analyzing only those electrograms that contribute to accurate arrhythmia detection.
Solution Approach 2:
Rather than analyzing all electrograms equally, the system applies partial action by focusing computational analysis only on the subset of electrograms that exhibit characteristics of arrhythmogenic activity. This approach achieves sufficient diagnostic thoroughness without the excessive processing time required to analyze every electrogram in detail.
Data Source
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AI summary
A system includes a display device and a processor. The processor is configured to (i) present, on the display device, intracardiac electrograms recorded over tissue of a portion of a cardiac chamber, (ii) identify a subset of the electrograms that show arrhythmogenic activity, (iii) analyze one or more characteristics of the arrhythmogenic activity, and (iv) graphically interconnect the electrograms in the subset to present the one or more characteristics of the arrhythmogenic activity to a user.