System and method to determine the location of a catheter
Systems, devices, and techniques are disclosed for automatically generating CPM matrices. The system includes a processor configured to receive a plurality of historical, sparse CPM matrices and a plurality of historical, supplemented CPM matrices, wherein each sparse CPM matrix is associated with a respective supplemented CPM matrix; train a learning system based on the plurality of historical, sparse CPM matrices and the plurality of historical, supplemented CPM matrices, wherein the learning system is trained so as to generate a supplemented CPM matrix given a sparse CPM matrix; receive, by the trained learning system, a new, sparse CPM matrix; and generate, with the trained learning system, a new supplemented CPM matrix.
1 . A system for generating improved current-to-position mapping, CPM, matrices, comprising:
a processor configured to:
receive a plurality of historical, sparse CPM matrices and a plurality of historical, supplemented CPM matrices, wherein each historical, sparse CPM matrix is associated with a respective historical, supplemented CPM matrix, each historical, sparse CPM matrix mapping electrical current data to catheter position data at a fewer number of locations than the respective supplemented CPM matrix;
train a learning system based on the plurality of historical, sparse CPM matrices and the plurality of historical, supplemented CPM matrices, wherein the learning system is trained so as to generate a supplemented CPM matrix given a sparse CPM matrix;
receive, by the trained learning system, a new, sparse CPM matrix;
generate, with the trained learning system, a new supplemented CPM matrix based, in part, on the new, sparse CPM matrix; and
ablate tissue based, at least in part, on the new supplemented CPM matrix.
2 . The system of claim 1 , further comprising a plurality of body surface electrodes configured to sense electrical signals.
3 . The system of claim 1 , wherein receiving the new sparse CPM matrix comprises:
receiving a subset of patient properties having known catheter locations; and
generating, from the subset of patient properties and known catheter locations, the new, sparse CPM matrix.
4 . The system of claim 1 , wherein the processor is configured to:
receive a subset of patient properties without known catheter locations; and
determine a catheter location for each of the subset of patient properties without known catheter locations based on said subset of patient properties without known catheter locations and the new, supplemented CPM matrix.
5 . The system of claim 3 , wherein each catheter location comprises a location of a respective electrode of the catheter.
6 . The system of claim 3 , wherein the subset of patient properties having known catheter locations and the subset of patient properties without known catheter locations both comprise electrical current signals associated with respective electrodes of the catheter.
7 . The system of claim 6 , when dependent on claim 2 , wherein the electrical current signals associated with respective electrodes of the catheter are sensed by the plurality of body surface electrodes.
8 . The system of claim 3 wherein the patient properties of the subset of patient properties having known catheter locations include data from historic procedures.
9 . The system of claim 1 , wherein the learning system is trained using at least one of a classification, regression and a clustering algorithm.
10 . The system of claim 1 , wherein the learning system comprises a neural network.
11 . A computer-implemented method for generating current-to-position mapping, CPM, matrices, the method comprising:
receiving a plurality of historical, sparse CPM matrices and a plurality of historical, supplemented CPM matrices, wherein each historical, sparse CPM matrix is associated with a respective historical, supplemented CPM matrix, each historical, sparse CPM matrix mapping electrical current data to catheter position data at a fewer number of locations than the respective supplemented CPM matrix;
training a learning system based on a plurality of historical, sparse CPM matrices and the plurality of historical, supplemented CPM matrices, wherein the learning system is trained so as to generate a supplemented CPM matrix given a sparse CPM matrix;
receiving, by the trained learning system, a new, sparse CPM matrix;
generating, with the trained learning system, a new supplemented CPM matrix based, in part, on the new, sparse CPM matrix; and
ablating tissue based, at least in part, on the new supplemented CPM matrix.
12 . The method of claim 11 , wherein receiving a new, spare CPM matrix comprises:
receiving a subset of patient properties having known catheter locations; and
generating, from the subset of patient properties and known catheter locations, the new, sparse CPM matrix.
13 . The method of claim 11 , further comprising:
receiving a subset of patient properties without known catheter locations; and
determining a catheter location for each of the subset of patient properties without known catheter locations based on said subset of patient properties without known catheter locations and the new, supplemented CPM matrix.
14 . The method of claim 12 , wherein each catheter location comprises a location of a respective electrode of the catheter.
15 . The method of claim 12 , wherein the subset of patient properties having known catheter locations and the subset of patient properties without known catheter locations both comprise electrical current signals recorded at respective electrodes of the catheter.
16 . The method of claim 12 wherein the patient properties of the subset of patient properties having known catheter locations include data from historic procedures.
17 . The method of claim 11 , wherein the learning system is trained using at least one of a classification, regression and a clustering algorithm.
18 . The method of claim 14 , wherein the learning system comprises a neural network.