Neural Network CPM Matrix Generation for Cardiac Catheter Localization
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Solution Overview
Problem
Current methods for visualizing and mapping intra-body surfaces during cardiac procedures, such as cardiac arrhythmia treatment, are time-consuming and resource-intensive, requiring extensive data collection and processing.
Innovation Solution
A system utilizing a neural network with body surface electrodes to generate current to position mapping (CPM) matrices based on historical data and patient properties, allowing for the prediction of catheter locations and expedited CPM matrix generation without the need for extensive location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods for visualizing and mapping intra-body surfaces are used, then accurate mapping can be achieved, but the process requires greater than desirable amount of time and resources
Solution Approach 1:
The system performs preliminary data collection during the cardiac procedure itself, using body surface electrodes to capture electrical signals that will be used for subsequent CPM matrix generation and catheter location prediction, eliminating the need for separate extensive mapping procedures
Solution Approach 2:
The system generates synthetic CPM matrices using a trained neural network model that copies and generalizes from historical CPM data, allowing accurate catheter location prediction without requiring actual extensive physical mapping for each new procedure
2Measurement precision
If extensive data collection and processing is performed for cardiac mapping, then accurate visualization and mapping can be obtained, but the procedure becomes more resource-intensive
Solution Approach 1:
The system extracts only the essential electrical signal data from body surface electrodes during the procedure, separating this critical information from unnecessary extensive data collection, and uses this extracted data with a pre-trained model to generate accurate CPM matrices
Solution Approach 2:
The system changes the approach from collecting extensive spatial mapping data to collecting electrical signal parameters from body surface electrodes, and changes the processing from complex real-time mapping calculations to neural network inference using pre-trained models
3Ease of operation
If real-time visualization and mapping are performed during cardiac procedures, then procedural guidance can be provided, but the time and resource requirements increase significantly
Solution Approach 1:
The system uses the electrical signals naturally present during the cardiac procedure itself, captured by body surface electrodes, to automatically generate CPM matrices and predict catheter locations without requiring additional active measurement actions or external resources
Solution Approach 2:
The neural network model is pre-trained on historical CPM data before the procedure, so that during the actual procedure, the system only needs to perform fast inference operations to generate real-time guidance, eliminating the need for extensive real-time data collection and processing
Data Source
AI summary
Systems, devices, and techniques are disclosed for automatically generating CPM matrices. The system includes a plurality of body surface electrodes configured to sense electric signals and a processor comprising a neural network. The processor is configured to receive a plurality of historical CPM matrices, patient properties, and corresponding catheter locations, train a learning system based on the plurality of CPM matrices, patient properties, and corresponding catheter locations, generate a model based on the learning system, receive new patient properties at least in part from the plurality of body surface electrodes and generate new CPM matrices based on new patient properties at least in part from the plurality of body surface electrodes.