Cardiac EP Mapping With Cloud Maps for Complex Electrograms
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Solution Overview
Problem
Existing electrophysiology maps, such as LAT maps, are inadequate for complex electrograms with low amplitude and long, fractionated potentials, particularly in low-voltage myocardium, limiting their utility in complex cardiac diagnostic and therapeutic procedures.
Innovation Solution
An electroanatomical mapping system classifies EP data points as substrate or healthy points, generates cloud maps using Gaussian splatting and iso-contouring algorithms, and computes wavelet domain scalograms to create detailed graphical representations of cardiac activity, including cloud maps and propagation wave maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional LAT maps are used for complex electrograms, then the mapping system remains simple and easy to operate, but the measurement precision and reliability deteriorate for low-voltage myocardium with fractionated potentials
Solution Approach 1:
The patent segments the complex electrogram signal analysis into multiple specialized visualization modes: cloud maps for activation timing, voltage maps for amplitude, and fractionation maps for potential complexity. Each map type processes specific features of the electrogram data independently, allowing precise analysis of different aspects without requiring a single complex measurement system.
Solution Approach 2:
The patent transitions from traditional two-dimensional LAT maps to three-dimensional cloud maps that display activation timing in spatial volume. The cloud map represents activation time as a 3D distribution with density and height encoding temporal information, adding a dimensional perspective that captures fractionated potentials and low-voltage regions more effectively than planar maps.
2Measurement precision
If high-density electrophysiology data is collected to improve map quality, then the measurement precision improves, but the productivity and time required for data processing increases
Solution Approach 1:
The patent extracts and separates different features from the high-density electrophysiology data into distinct visualization layers. Instead of processing all data points uniformly, the system extracts activation timing information for cloud maps, voltage information for amplitude maps, and fractionation characteristics for specialized maps, processing each extracted feature set independently to maintain speed while utilizing high-density data.
Solution Approach 2:
The patent changes the parameter representation from single-value LAT measurements to multi-parameter visualizations including activation time, voltage amplitude, and fractionation indices. By transforming the data parameters into different visual encodings (color, height, density), the system efficiently processes high-density data through parallel computation of multiple parameter sets rather than sequential analysis.
3Loss of information
If traditional LAT map generation methods are used, then the device complexity remains low, but the visualization quality and diagnostic information for complex electrograms deteriorates
Solution Approach 1:
The patent applies local quality by providing different visualization types for different regions of the cardiac anatomy. Cloud maps are generated for regions with fractionated potentials, voltage maps for low-voltage regions, and traditional LAT maps for normal tissue. This localized approach ensures that each region is visualized with the appropriate level of detail and feature representation, maximizing information visibility without uniformly increasing system complexity across all areas.
Data Source
AI summary
A plurality of electrophysiology (EP) data points, each including an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as substrate or healthy, and a cloud map of the substrate EP data points can be generated. A graphical representation of the cloud map can be output in combination with a graphical representation of an electrophysiology map of the healthy EP data points. In alternative embodiments, the electrogram signals can be transformed into the wavelet domain, thereby computing a plurality of scalograms, and computing a wave function of each scalogram, thereby computing a plurality of wave functions. A propagation map, such as a propagation wave map and/or propagation wave trail map, can then be generated from the wave functions and output graphically.