Cardiac Activation Onset Time Optimization via Vector Field Pattern Matching
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Solution Overview
Problem
Current methods for diagnosing and treating heart rhythm disorders face challenges in reliably estimating onset times of activation signals due to noise and artifacts, which affect the accuracy of visualizing underlying activation patterns and identifying targets for therapy.
Innovation Solution
A system that senses activation signals with mapping electrodes, generates vector fields, determines onset times and alternative candidates, identifies initial vector field patterns, and optimizes onset times based on similarity with template patterns to improve the accuracy of signal mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional characteristic feature methods (steepest descent, most negative peak) are used to determine onset times, then the method is simple and easy to implement, but the reliability and measurement precision deteriorate due to ambiguity from noise and artifacts
Solution Approach 1:
The system performs preliminary actions by pre-processing the electrogram signal to enhance the signal of interest and suppress noise and artifacts before onset time determination. This includes applying filters and transformations to prepare the signal for more accurate pattern matching, thereby improving reliability without requiring overly complex real-time processing during the actual detection phase.
Solution Approach 2:
The patent introduces an intermediary approach by using enhanced electrogram signals as a mediator between the raw signal and the final onset time determination. The enhanced signal serves as an intermediate representation that combines information from multiple sources (local and far-field components) to resolve ambiguities in onset time detection while maintaining system manageability.
2Measurement precision
If noise and artifacts are present in the electrogram signal, then the signal contains more information about the cardiac chamber, but the measurement precision of onset times deteriorates due to superimposed signals
Solution Approach 1:
The patent converts the harmful effect of noise and artifacts into a beneficial outcome by using enhanced electrogram signals that explicitly model and separate the signal of interest from noise and artifacts. The enhancement process transforms the problematic mixed signal into a structured representation where the underlying activation pattern can be reliably identified despite the presence of interfering components.
Solution Approach 2:
The system extracts the signal of interest from the complex electrogram by separating the local activation signal from far-field artifacts and noise. This extraction is achieved through signal enhancement techniques that isolate the relevant information, allowing precise onset time measurement even when the original signal contains harmful interference.
3Reliability
If multiple large negative peaks are present in the electrogram, then the signal reflects complex cardiac activation, but the reliability of onset time identification deteriorates due to superposition effects
Solution Approach 1:
The patent applies segmentation by dividing the electrogram signal into distinct components - the signal of interest (local activation) and interfering components (far-field artifacts, noise). This segmentation is achieved through signal enhancement that separates overlapping peaks and assigns them to their respective sources, making it possible to reliably identify onset times even in the presence of multiple large negative peaks.
Data Source
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AI summary
An anatomical mapping system and method includes mapping electrodes configured to detect activation signals of cardiac activity. A processing system is configured to record the detected activation signals and generate a vector field for each sensed activation signal during each instance of the physiological activity. The processing system determines an onset time and alternative onset time candidates, identifies an initial vector field template based on a degree of similarity between the initial vector field and a vector field template from a bank of templates, then determines an optimized onset time for each activation signal based on a degree similarity between the onset time candidates and initial vector field template.