MACHINE LEARNING USING SIMULATED CARDIOGRAMS

1 . A method performed by one or more computing systems for generating a classifier for classifying electromagnetic data derived from an electromagnetic source within a body, the method comprising:

accessing a computational model of the electromagnetic source, the computational model for modeling electromagnetic output of the electromagnetic source over time based on a source configuration of the electromagnetic source;

for each of a plurality of source configurations, generating using the computational model a modeled electromagnetic output of the electromagnetic source for that source configuration;

for each modeled electromagnetic output,

deriving the electromagnetic data for the modeled electromagnetic output; and

generating a label for the derived electromagnetic data based on the source configuration for the modeled electromagnetic data; and

training a classifier with the derived electromagnetic data and the labels as training data.

2 . The method of claim 1 wherein the modeled electromagnetic output for a source configuration includes, for each of a plurality of time intervals, an electromagnetic mesh with a modeled electromagnetic value for each of a plurality of locations of the electromagnetic source.

3 . The method of claim 2 wherein the derived electromagnetic data, for a time interval, is an equivalent source representation of the electromagnetic output.

4 . The method of claim 3 wherein the equivalent source representation is generated using principal component analysis.

5 . The method of claim 3 further comprising identifying cycles within the derived electromagnetic data for a modeled electromagnetic output.

6 . The method of claim 5 wherein the same label is generated for each cycle.

7 . The method of claim 5 further comprising identifying a sequence of cycles that are similar and wherein the same label is generated for each sequence.

8 . The method of claim 5 wherein the deriving of the electromagnetic data for a modeled electromagnetic output includes normalizing the modeled electromagnetic output on a per-cycle basis.

9 . The method of claim 1 wherein the classifier is a convolutional neural network.

10 . The method of claim 9 wherein the convolutional neural network inputs a one-dimensional image.

11 . The method of claim 1 wherein the classifier is a recurrent neural network, an autoencoder, or a restricted Boltzmann machine.

12 . The method of claim 1 wherein the classifier is a support vector machine.

13 . The method of claim 1 wherein the classifier is Bayesian.

14 . The method of claim 1 wherein the electromagnetic source is a heart, a source configuration represents source location and other properties of a heart disorder, the modeled electromagnetic output represents activation of the heart, and the electromagnetic data is based on body-surface measurements such as an electrocardiogram.

15 . The method of claim 14 wherein the heart disorder is selected from a set consisting of atrial fibrillation, ventricular fibrillation, atrial tachycardia, ventricular tachycardia, atrial flutter, premature ventricular complexes, atrioventricular nodal reentrant tachycardia, atrioventricular reentrant tachycardia, and junctional tachycardia.

16 . A method performed by a computing system for classifying electromagnetic output collected from a target that is an electromagnetic source within a body, the method comprising:

accessing a classifier to generate a classification for electromagnetic output of an electromagnetic source, the classifier trained using training data generated from modeled electromagnetic output for a plurality of source configurations of an electromagnetic source, the modeled electromagnetic output being generated using a computational model of the electromagnetic source that models the electromagnetic output of the electromagnetic source over time based on a source configuration;

collecting target electromagnetic output from the target; and

applying the classifier to the target electromagnetic output to generate a classification for the target.

17 . The method of claim 16 wherein the training data is generated by running, for each of the source configurations, a simulation that generates an electromagnetic mesh for each of a plurality of simulation intervals, each electromagnetic mesh having an electromagnetic value for a plurality of locations of the electromagnetic source.

18 . The method of claim 16 wherein the electromagnetic source is a heart, a source configuration represents a source location of a heart disorder, and the modeled electromagnetic output represents activation of the heart, and the classifier is trained using electromagnetic data derived from an electrocardiogram representation of the electromagnetic output.

19 . One or more computing systems for generating a classifier for classifying electromagnetic output of an electromagnetic source, the systems comprising:

one or more computer-readable storage mediums storing:

a computational model of the electromagnetic source, the computational model for modeling electromagnetic output of the electromagnetic source over time based on a source configuration of the electromagnetic source; and

computer-executable instructions for controlling the one or more computing systems to:

for each of a plurality of source configurations, generate training data from the electromagnetic output of the computational model that is based on the source configuration; and

train the classifier using the training data; and

one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.

20 . The one or more computing systems of claim 19 wherein the computer-executable instructions to generate the training data for a source configuration further control the one or more computing systems to generate derived electromagnetic data from the electromagnetic output for the source configuration and generate a label for the electromagnetic data based on the source configuration.