Cardiac Restitution Mapping via EP Data Clustering
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Solution Overview
Problem
Current methods for mapping cardiac restitution during electrophysiology studies are not capable of real-time data acquisition and analysis, which is essential for understanding dynamic changes in heart rate and tissue properties that control the heart's behavior.
Innovation Solution
A system and method for mapping cardiac restitution by acquiring electrophysiology data points, identifying clusters, fitting exponential restitution curves, and outputting graphical representations on a three-dimensional cardiac surface model, allowing for real-time analysis of restitution metrics such as maximum slope and asymptotic limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cardiac restitution mapping methods are used, then comprehensive restitution data can be obtained, but real-time data acquisition and analysis capability is lost
Solution Approach 1:
The cardiac surface is divided into multiple regions, and restitution analysis is performed separately for each region using local EP data point clusters. This segmentation enables parallel processing of different cardiac regions, achieving both comprehensive data coverage and real-time analysis capability simultaneously.
Solution Approach 2:
The system pre-processes EP data points by organizing them into clusters and pre-calculating restitution metrics as new data arrives. This preliminary action allows the system to maintain real-time analysis capability while ensuring accurate restitution measurements without requiring complete data sets before analysis.
2Measurement precision
If detailed EP data point clustering is performed for accurate restitution mapping, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system uses a sufficient number of EP data points for accurate restitution curve fitting without requiring exhaustive data collection. By identifying clusters with adequate data density rather than processing all possible data points, the system achieves precise restitution measurements while reducing computational complexity.
3Loss of information
If comprehensive EP data is collected across the entire cardiac surface, then complete restitution mapping is achieved, but data processing time and system complexity increase
Solution Approach 1:
The cardiac surface is divided into multiple regions, and restitution analysis is performed separately for each region using local EP data point clusters. This segmentation enables parallel processing of different cardiac regions, achieving both comprehensive data coverage and real-time analysis capability simultaneously.
Solution Approach 2:
The system continuously acquires and processes EP data points in real-time as they become available, rather than waiting for complete data sets. This continuous processing maintains up-to-date restitution maps while minimizing processing delays and enabling dynamic monitoring of cardiac restitution changes.
Data Source
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AI summary
A method of mapping cardiac restitution includes: receiving a plurality of EP data points including location and restitution data; identifying a subset of the EP data points forming an EP data point cluster; fitting a restitution curve to the restitution data of the EP data points forming the cluster; and identifying at least one restitution metric for a region of the cardiac surface corresponding to the cluster from the restitution curve. The restitution curve can be an exponential function using quiescent interval data (e.g., DI and/or CL) as the independent variable and cardiac repolarization activity data (e.g., APD, ARI, and/or EGM width) as the dependent variable. The parameters of the exponential function can be determined by optimizing a cost function. A graphical representation of the restitution metric can also be output on a three-dimensional model of the cardiac surface.