System and method for determining the position of a catheter

Through the training and learning system, the problem of low efficiency of CPM matrix generation in the prior art is solved, and efficient and accurate current-to-position matrix generation is achieved.

CN119317970APending Publication Date: 2025-01-14BIOSENSE WEBSTER (ISRAEL) LTD
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202380043511.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Priority Date
2022-07-05
Filing Date
2023-07-04
Publication Date
2025-01-14

AI Technical Summary

Technical Problem

The prior art requires the acquisition of electric signals and position data across a wide range when generating a comprehensive current-to-position (CPM) matrix, and is time- and labor-intensive, making it difficult to effectively use sparse data for the generation of supplementary data.

Method used

Through training and learning systems, the sparse CPM matrix is ​​used to generate supplementary CPM matrix, and algorithms such as neural networks are used to predict catheter position and cardiac electrical signals based on sparse data, reducing dependence on catheter position and improving matrix generation efficiency.

Benefits of technology

It realizes the generation of high-resolution CPM matrix without a large amount of catheter position information, which reduces the time for catheter position data acquisition and improves the efficiency and accuracy of matrix generation.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119317970A_ABST
    Figure CN119317970A_ABST
Patent Text Reader

Abstract

Systems, apparatuses, and techniques for automatically generating CPM matrices are disclosed. The system includes a processor configured to receive a plurality of historical sparse CPM matrices and a plurality of historical supplemental CPM matrices, where each sparse CPM matrix is associated with a respective supplemental CPM matrix; training a learning system based on the plurality of historical sparse CPM matrices and the plurality of historical complementary CPM matrices, where the learning system is trained to generate a complementary CPM matrix given a sparse CPM matrix; receiving, by the trained learning system, a new sparse CPM matrix; and generating a new complementary CPM matrix with the trained learning system.
Need to check novelty before this filing date? Find Prior Art