ECG Activation Pattern Clustering for High-Confidence Arrhythmia Localization
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Solution Overview
Problem
Existing methods for determining the source location of arrhythmias, such as PVC, in non-invasive ECGs lack the necessary confidence and accuracy for guiding invasive treatments like catheter ablation.
Innovation Solution
A processor-based algorithm that clusters ECG patterns into morphological templates, calculating a percentage match to a predefined threshold, thereby enhancing the confidence in identifying the arrhythmia source location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for determining arrhythmia source location using non-invasive ECGs are used, then the procedure can be performed without invasive intervention, but the confidence and accuracy are insufficient for guiding invasive treatments
Solution Approach 1:
The patent segments the ECG analysis process into distinct morphological templates (normal sinus rhythm, PVC, VT) and processes ECGs through multiple classification stages. This segmentation allows systematic evaluation of arrhythmia patterns and improves reliability by breaking down the complex diagnosis into manageable categories with specific criteria.
Solution Approach 2:
The patent performs preliminary classification of ECGs into morphological templates before final arrhythmia diagnosis. By pre-categorizing ECGs based on their morphological characteristics and comparing them against established templates, the system builds confidence in source location determination before proceeding to treatment guidance.
2Measurement precision
If a lengthy ECG acquisition (up to a minute) is performed to determine PVC frequency and morphology, then the accuracy of PVC detection improves, but the time required for acquisition increases
Solution Approach 1:
The patent employs periodic comparison of ECGs against morphological templates in a systematic sequence. Rather than continuously analyzing every beat, the system periodically evaluates ECGs against predefined templates (normal sinus rhythm, PVC, VT) and maintains classification confidence through repeated sampling, achieving accurate detection without requiring excessively long acquisition periods.
Data Source
AI summary
A method includes receiving a set of electrocardiograms (ECG) determined to belong to a given morphologic template indicative of a given type of arrhythmia. Using a location algorithm, a percentage of the ECGs in the set is calculated, that points to a same source location of the given type of arrhythmia. The calculated percentage is compared to a predefined threshold percentage. If the percentage of ECGs is found to exceed the threshold percentage, the source location is reported to a user.