Machine learning using simulated cardiograms

Abstract

A system is provided for generating a classifier for classifying electromagnetic data (e.g., ECG) derived from an electromagnetic source (e.g., heart). The system accesses a computational model of the electromagnetic source. The computational model models the electromagnetic output of the electromagnetic source over time based on a source configuration (e.g., rotor location) of the electromagnetic source. The system generates, for each different source configuration (e.g., different rotor locations), a modeled electromagnetic output (e.g., ECG) of the electromagnetic source for that source configuration. For each modeled electromagnetic output, the system derives the electromagnetic data for the modeled electromagnetic output and generates a label (e.g., rotor location) for the derived electromagnetic data from the source configuration for the modeled electromagnetic data. The system trains a classifier with the derived electromagnetic data and the labels as training data. The classifier can then be used to classify the electromagnetic output collected from patients.

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.