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AI Biotech/Diagnostics: Cardio
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Application US20190328257
Published 2019-10-31
Machine Learning Using Simulated Cardiograms
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.
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- 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 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.
- 16-21. (canceled)
- 21. One or more computing systems for generating a classifier for classifying electromagnetic data derived from an electromagnetic source within a human body, the one or more computing systems comprising:
one or more computer-readable storage mediums storing computer-executable instructions for controlling the one or more computing systems to:
access 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,
generate using the computational model a modeled electromagnetic output of the electromagnetic source for that source configuration;
derive electromagnetic data for the modeled electromagnetic output; and
generate a label for the derived electromagnetic data based on the source configuration; and
train a classifier with the derived electromagnetic data and the labels as training data; and
one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.
- 33. One or more computing systems for generating a classifier for classifying electromagnetic data derived from an electromagnetic source, the electromagnetic source being a human heart and the electromagnetic data representing a cardiogram, the one or more computing systems comprising:
one or more computer-readable storage mediums storing:
a computational model of the heart, the computational model for modeling electromagnetic output that represents activation of the heart over time based on a source configuration of the heart; and
computer-executable instructions for controlling the one or more computing systems to:
for each of a plurality of source configurations for a heart, generate training data that includes a cardiogram and a label, the cardiogram being derived from the electromagnetic output of the computational model that is based on the source configuration, the label being based on the source configuration; and
train the classifier using the training data, the classifier for identifying a label for a cardiogram collected from a target human; and
one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.
