Automatic Electroanatomic Map Generation from Morphologic Beat Classification
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Solution Overview
Problem
Current methods for generating electroanatomic maps of the heart often require manual separation of arrhythmia morphologies, which is time-consuming and inefficient, especially when dealing with multiple arrhythmias present in a patient's data.
Innovation Solution
The method automatically generates multiple electroanatomic maps by analyzing intracardiac electrograms morphologically and using pre-defined or adaptively created templates to classify beats based on their morphologic characteristics, allowing for real-time comparison and inclusion or rejection of beats in the maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual separation of arrhythmia morphologies is performed, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual mechanical separation of arrhythmia morphologies with an automated computer-based system that uses signal processing and pattern recognition algorithms to classify beats into different arrhythmia types, thereby maintaining diagnostic accuracy while eliminating time-consuming manual operations
Solution Approach 2:
The system performs self-service by automatically analyzing electroanatomic map data, identifying different arrhythmia morphologies, and generating classified maps without requiring manual intervention, allowing the diagnostic process to be both accurate and efficient
2Adaptability or versatility
If multiple arrhythmias are analyzed simultaneously, then diagnostic comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of analyzing multiple arrhythmias by creating separate classified electroanatomic maps for each arrhythmia type, allowing the system to handle multiple conditions simultaneously while maintaining manageable complexity through organized data separation
Solution Approach 2:
The system achieves universality by implementing a single automated analysis platform that can handle multiple types of arrhythmias (atrial fibrillation, atrial flutter, ventricular tachycardia, etc.) using the same signal processing and classification algorithms, thereby improving diagnostic comprehensiveness without proportionally increasing system complexity
Data Source
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AI summary
Cardiac electrograms are recorded in a plurality of channels. Beats are classified automatically into respective classifications according to a resemblance of the morphologic characteristics of the beats to members of a set of templates. Respective electroanatomic maps of the heart are generated from the classified beats.