Focal Point Identification Using Phase Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electrocardiographic mapping technologies face challenges in accurately identifying and visualizing the focal points of cardiac arrhythmias, such as atrial and ventricular fibrillation, due to limitations in detecting and classifying the origin of abnormal electrical activity.
Innovation Solution
The system analyzes spatial and temporal information from electroanatomic data to identify focal points by comparing phase values of nodes on a geometric surface, using a phase calculator and rules engine to classify candidate nodes as focal points, and generates graphical maps to visualize these points for clinical targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrocardiographic mapping technologies are used to identify focal points, then the system can detect electrical signals from the heart, but the accuracy in identifying and visualizing focal points of cardiac arrhythmias is insufficient
Solution Approach 1:
The system segments the analysis process into distinct modules: signal acquisition from multiple electrodes, phase calculation for each node, focal point identification through phase comparison, and graphical map generation. This segmentation allows each module to be optimized independently, improving focal point identification accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces phase information as an additional dimension for analyzing cardiac electrical activity. By calculating and comparing phase values across different nodes and time points, the system transforms traditional 2D electrocardiographic data into a multi-dimensional phase space, enabling more precise focal point identification through phase singularities and wavefront analysis.
2Manufacturing precision
If the system analyzes spatial and temporal information from electroanatomic data to identify focal points, then the localization precision is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary phase calculation for all nodes before focal point identification. By pre-computing phase values and their temporal derivatives, the system prepares the data structure needed for rapid focal point detection, reducing real-time computational complexity while maintaining high localization precision through thorough preliminary analysis.
Solution Approach 2:
The patent implements efficient algorithms that skip unnecessary computational steps in focal point identification. By directly comparing phase values and detecting phase singularities without exhaustive search, the system achieves high localization precision while minimizing computational burden through optimized detection pathways.
3Reliability
If the system uses phase comparison methods to classify candidate nodes as focal points, then false positives and negatives are reduced, but the processing time increases
Solution Approach 1:
The system employs self-organizing algorithms where nodes automatically classify themselves as focal points or non-focal based on their phase relationships with neighboring nodes. Each node's phase value and its comparison with adjacent nodes provides self-sufficient information for classification, reducing the need for extensive global processing and thereby decreasing processing time while maintaining high reliability through distributed decision-making.
Solution Approach 2:
The patent implements iterative feedback mechanisms where initial focal point classifications are refined through multiple passes of phase comparison. The system uses feedback from detected phase singularities to adjust detection parameters and re-evaluate candidate nodes, progressively improving classification reliability while limiting processing time through convergence criteria that stop iterations when sufficient accuracy is achieved.
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
A method can determine one or more origins of focal activation. The method can include computing phase for the electrical signals at a plurality of nodes distributed across a geometric surface based on the electrical data across time. The method can determine whether or not a given candidate node of the plurality of nodes is a focal point based on the analyzing the computed phase and magnitude of the given candidate node. A graphical map can be generated to visualize focal points detected on the geometric surface.