Real-Time Cardiac Propagation Velocity Mapping With LAT-Based PCA
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Solution Overview
Problem
Existing cardiac mapping technologies face challenges in accurately computing local propagation velocities and distinguishing electrically-inactive tissue, leading to inaccurate diagnosis and treatment.
Innovation Solution
The use of algorithms for computing propagation velocities based on local activation times (LATs) with Principal Component Analysis (PCA) and selecting suitable electrode pairs aligned with local propagation directions, combined with real-time display of velocity markers, to enhance accuracy and visibility of conduction patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional algorithms are used to compute propagation velocities, then computation can be performed, but accuracy in distinguishing electrically-inactive tissue is insufficient
Solution Approach 1:
The patent changes the computational parameters by using local activation times (LATs) as the primary input parameter instead of traditional voltage-based methods. It also introduces velocity magnitude thresholds as a new parameter to distinguish inactive tissue, transforming the computation from purely velocity-based to a combined velocity-magnitude assessment that improves reliability in identifying electrically-inactive regions
Solution Approach 2:
The patent replaces traditional mechanical/electrical signal-based methods with a computational algorithm that uses Principal Component Analysis (PCA) on LAT data. This substitution of the computational mechanism enables more accurate velocity computation and tissue characterization by using statistical analysis rather than direct electrical signal interpretation
2Measurement precision
If real-time computation of propagation velocities is implemented, then diagnostic accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct stages: (1) LAT computation from electrogram signals, (2) velocity computation using PCA on LAT data, (3) velocity magnitude calculation, and (4) tissue activity classification. This segmentation allows each stage to be optimized independently and processed in real-time without overwhelming computational resources
Solution Approach 2:
The patent performs preliminary computation of LATs at multiple electrode sites before computing propagation velocities. By pre-processing the electrogram data to extract LATs and storing them for subsequent velocity calculations, the system prepares data in advance, reducing the computational burden during real-time velocity computation and enabling faster processing
3Area of stationary object
If multiple electrode pairs are used for velocity computation, then measurement coverage is improved, but data processing complexity increases
Solution Approach 1:
The patent makes the velocity computation algorithm universal by using PCA, a general statistical method that can handle any number of electrode pairs and any spatial configuration. The same core algorithm processes data from 3, 4, or more electrodes without requiring different computational approaches, enabling the system to scale to multiple electrode pairs while maintaining manageable complexity through a unified processing framework
Data Source
AI summary
A method includes, based on respective signals acquired by a plurality of electrodes on an anatomical surface of a heart, computing respective local activation times (LATs) at respective locations of the electrodes. The method further includes, based on the LATs, computing respective directions of electrical propagation at the locations. The method further includes selecting pairs of adjacent ones of the electrodes such that, for each of the pairs, a vector joining the pair is aligned, to within a predefined threshold degree of alignment, with the direction of electrical propagation at the location of one of the electrodes belonging to the pair. The method further includes associating respective bipolar voltages measured by the pairs of electrodes with a digital model of the anatomical surface. Other examples are also described.