ECG Signal Analysis Algorithm for Atrial Tachycardia Mapping
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Solution Overview
Problem
Current methods for analyzing electrocardiograph (ECG) signals struggle to accurately and efficiently map atrial tachycardias, particularly in distinguishing between different types of cardiac activations and identifying their origins, leading to challenges in rapid diagnosis and stratification.
Innovation Solution
A signal analyzing algorithm that processes multi-channel unipolar ECG signals to track and match time patterns, using a processor to calculate weighted averages and determine similarity between signal annotations, enabling the separation and tracking of distinct heart activation mechanisms and generating LAT maps that focus on single types of heart activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG signal analysis methods are used, then the analysis process is simple, but the accuracy in distinguishing different cardiac activations and identifying origins is poor
Solution Approach 1:
The patent segments the multi-channel ECG signals by identifying and separating different activation types (e.g., sinus vs. ectopic beats) based on their distinct temporal patterns. The algorithm divides the signal stream into homogeneous groups, analyzing each segment independently to improve classification accuracy while managing computational complexity through focused processing.
Solution Approach 2:
The patent introduces a temporal dimension to the analysis by examining the timing relationships between signals across multiple channels. Instead of analyzing amplitude or morphology alone, the method uses time interrelations (temporal patterns) as an additional dimension for distinguishing cardiac activations, enabling more accurate identification of activation origins.
2Measurement precision
If detailed analysis of all ECG signal characteristics is performed, then diagnostic accuracy improves, but the time required for analysis increases
Solution Approach 1:
The patent extracts only the most discriminative temporal features from the ECG signals - specifically the time interrelations between channels - rather than analyzing all signal characteristics. This selective extraction of key temporal patterns enables rapid classification of activation types while maintaining high diagnostic accuracy, significantly reducing analysis time compared to comprehensive signal analysis.
Solution Approach 2:
The algorithm performs partial analysis by focusing on specific temporal relationships that are most informative for distinguishing cardiac activations. Rather than exhaustively analyzing all aspects of the signals, it applies analysis only to the critical temporal dimensions needed for rapid diagnosis, achieving sufficient accuracy with reduced computational effort.
3Adaptability or versatility
If multiple types of heart activations are analyzed together, then comprehensive cardiac assessment is achieved, but the ability to track specific activation mechanisms is reduced
Solution Approach 1:
The patent segments the mixed signal stream into separate homogeneous groups based on activation type. By dividing the comprehensive dataset into distinct categories (sinus activations, ectopic beats, etc.), the algorithm enables precise tracking of each specific mechanism while still providing comprehensive cardiac assessment through analysis of all segments collectively.
Solution Approach 2:
The patent introduces template matching as an intermediary mechanism that bridges comprehensive assessment and specific tracking. Templates serve as reference patterns that mediate between the overall signal ensemble and individual activation events, enabling the system to maintain versatility while achieving precise tracking through pattern recognition and comparison.
Data Source
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AI summary
A method, including, receiving a first group of electrocardiograph (ECG) signals derived from a single heartbeat and generated at a respective plurality of electrodes on a catheter in a heart of a subject, formulating a template relating times of annotations of the first group of the ECG signals, and assigning the template an index. The method further includes receiving a second group of ECG signals derived from a subsequent single heartbeat and generated at the electrodes, calculating times of annotations of the second group, formulating a comparison between the template and the times of annotations of the second group, and, when the comparison indicates that the times of annotations of the second group correspond to the template, assigning the index to the second group of ECG signals, and presenting graphically on a display an occurrence of the template relative to a timeline representing heartbeats from the heart.