Cardiac Mapping Catheter Interpolation Algorithm
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Solution Overview
Problem
Current medical devices for cardiac tissue mapping face limitations in accurately detecting and displaying electrical activity due to incomplete or inaccurate signal detection, particularly in three-dimensional spatial configurations, which can lead to missing information and visual representation issues.
Innovation Solution
The development of a medical device with a catheter shaft and processor that interpolates missing activation times using geodesic distance calculations and weighting coefficients, generating accurate activation maps and confidence levels to enhance data interpolation and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrode array methods are used for cardiac mapping, then the device structure is simple, but the measurement precision and completeness of electrical activity detection deteriorate due to incomplete signal detection in three-dimensional spatial configurations
Solution Approach 1:
The patent replaces complex physical electrode arrays with dense spatial sampling using a streamlined catheter structure. Instead of increasing mechanical complexity of the electrode array, the system uses computational methods (Gaussian process regression) to achieve complete three-dimensional electrical activity detection, substituting mechanical complexity with algorithmic processing.
Solution Approach 2:
The patent combines limited physical electrodes with virtual electrode technology created through Gaussian process regression. This composite approach merges actual sensor data with computationally generated data points, creating a complete three-dimensional electrical map without requiring physically dense electrode placement.
2Measurement precision
If more electrodes are added to improve signal detection coverage, then the measurement precision improves, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent creates virtual copies of electrode data through Gaussian process regression. Instead of manufacturing and placing additional physical electrodes, the system generates synthetic data points that replicate what additional electrodes would measure, achieving complete spatial coverage without increased manufacturing complexity.
Solution Approach 2:
The patent changes the parameter of electrode density from a physical manufacturing constraint to a computational parameter. By using Gaussian process regression with appropriate kernel functions and hyperparameters, the system achieves high-resolution three-dimensional mapping without the need for physically dense electrode arrays.
3Reliability
If traditional interpolation methods are used for missing data, then the device complexity remains low, but the reliability and accuracy of the activation maps deteriorate
Solution Approach 1:
The patent replaces simple linear interpolation algorithms with Gaussian process regression, substituting basic computational mechanics with advanced probabilistic modeling. This provides reliable uncertainty quantification and accurate activation time estimation while maintaining computational efficiency through optimized implementation.
Solution Approach 2:
The patent implements feedback through the probabilistic nature of Gaussian process regression, where the model continuously refines predictions based on observed data patterns. The uncertainty estimates provide feedback on data quality, allowing the system to adaptively improve activation map reliability.
Data Source
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AI summary
Medical devices and methods for making and using medical devices are disclosed. An example medical device may include a catheter shaft with a plurality of electrodes coupled thereto and a processor coupled to the catheter shaft. The processor may be capable of collecting a set of signals from the plurality of electrodes and generating a data set from at least one of the set of signals. The data set may include at least one known data point and one or more unknown data points. The processor may also be capable of interpolating at least one of the unknown data points by conditioning the data set, assigning an interpolated value to at least one of the unknown data points, and assigning a confidence level to the interpolated value.