Electroanatomical Cardiac Mapping With Sub-Interval Feature Detection
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Solution Overview
Problem
Existing cardiac mapping technologies face challenges in generating high-quality cardiac geometries and electrophysiology maps with increased density and rapidity, particularly due to the complexity of electrocardiographic mapping and the presence of multiple deflections within the roving activation interval (RAI).
Innovation Solution
An electroanatomical mapping system that identifies an initial event time within an activation interval using energy functions, template matching, or weighted window functions, and defines sub-intervals to analyze electrophysiological characteristics, allowing for the creation of electrophysiology maps that include data points beyond the activation interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrocardiographic mapping methods are used to analyze intracardiac electrograms over a specified time interval, then the mapping can be performed with standard processing, but the analysis is complicated by the presence of multiple deflections within the activation interval and boundary effects reduce data quality
Solution Approach 1:
The patent divides the activation interval into multiple sub-intervals, each associated with a specific electrophysiological feature (e.g., local activation time, fractionated electrogram features). This segmentation allows the system to identify and analyze specific features within defined time windows, reducing confusion from multiple deflections and improving measurement precision without requiring complex global signal processing.
Solution Approach 2:
The patent performs preliminary identification of electrophysiological features within sub-intervals before conducting full map generation. By pre-identifying key features like local activation times and their associated sub-intervals, the system simplifies subsequent mapping operations and avoids the complexity of analyzing entire activation intervals at once.
2Loss of information
If the activation interval is extended to capture more electrophysiological features, then more comprehensive mapping data can be obtained, but boundary effects become more pronounced and data quality near boundaries deteriorates
Solution Approach 1:
By segmenting the activation interval into multiple sub-intervals, the patent ensures that each electrophysiological feature is analyzed within its own dedicated time window. This prevents boundary effects from contaminating feature identification, as each feature is captured in a sub-interval that is optimized for that specific feature rather than relying on a single large activation interval.
Solution Approach 2:
The patent applies different analysis methods and time window definitions to different regions of the activation interval based on the specific electrophysiological features present. Each sub-interval is tailored to capture the characteristics of its associated feature, ensuring optimal data quality locally rather than applying a uniform approach that suffers from boundary effects.
3Measurement precision
If high density electrophysiology maps are generated with increased detail, then map quality improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary identification of electrophysiological features and their associated sub-intervals before generating the full high-density map. This preliminary processing organizes the data structure in advance, allowing subsequent map generation to proceed more efficiently by utilizing the pre-organized feature data rather than processing all signals from scratch.
Solution Approach 2:
By organizing electrophysiological data into segmented sub-intervals with identified features, the patent enables parallel processing and optimized computational routines for each segment. This segmentation strategy reduces overall processing time while maintaining high map quality, as the system can efficiently handle multiple smaller segments rather than one large complex dataset.
Data Source
AI summary
Electrophysiological activity can be mapped using sub-intervals of electrophsyiological signals. An electroanatomical mapping system receives a plurality of electrophsyiological signals (402), each of which spans an activation interval. For each signal, the system identifies an initial event time within the activation interval, such as by identifying a time of maximum signal energy (404), and defines a sub-interval about the initial event time (406). The system then analyzes the sub-interval to identify one or more electrophysiological characteristics of the electrophysiological signal (408) and adds a corresponding electrophysiology data point to an electrophysiology map (410). Advantageously, the sub-interval can extend outside of the activation interval, such that the instant teachings allow for capture and analysis of deflections that occur at or near the boundaries of the activation interval.