Application US20200357517 AI Biotech/Diagnostics: Other Innovation

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Application US20200357517

Published 2020-11-12

Machine Learning Based Depolarization Identification And Arrhythmia Localization Visualization

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

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3 Independent Claims

1. A computer-implemented method comprising: receiving, by processing circuitry of a medical device system, episode data for an episode stored by a medical device of a patient, wherein the episode is associated with a period of time, and the episode data comprises a cardiac electrogram sensed by the medical device during the period of time; applying, by the processing circuitry, one or more machine learning models to the episode data, the one or more machine learning models configured to output a respective likelihood value for each of a plurality of arrhythmia type classifications, each of the likelihood values representing a likelihood that the respective arrhythmia type classification occurred at any point during the period of time; based on the application of the one or more machine learning models to the episode data, deriving, by the processing circuitry and for each of the arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over the period of time; and displaying, by the processing circuitry and to a user, a graph of the varying likelihoods of the arrhythmia type classifications over the period of time.
13. A medical device system comprising: a medical device configured to: sense a cardiac electrogram of a patient via a plurality of electrodes; and store episode data for an episode, wherein the episode is associated with a period of time, and the episode data comprises the cardiac electrogram sensed by the medical device during the period of time; and processing circuitry configured to: receive the episode data; apply one or more machine learning models to the episode data, the one or more machine learning models configured to output a respective likelihood value for each of a plurality of arrhythmia type classifications, each of the likelihood values representing a likelihood that the respective arrhythmia type classification occurred at any point during the period of time; based on the application of the one or more machine learning models to the episode data, derive, for each of the arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over the period of time; and display, to a user, a graph of the varying likelihoods of the arrhythmia type classifications over the period of time.
27. A non-transitory computer-readable medium comprising instructions that, when executed by processing circuitry of a computing system, cause the computing system to: receive episode data for an episode stored by a medical device of a patient, wherein the episode is associated with a period of time, and the episode data comprises a cardiac electrogram sensed by the medical device during the period of time; apply one or more machine learning models to the episode data, the one or more machine learning models configured to output a respective likelihood value for each of a plurality of arrhythmia type classifications, each of the likelihood values representing a likelihood that the respective arrhythmia type classification occurred at any point during the period of time; based on the application of the one or more machine learning models to the episode data, derive class activation data indicating varying likelihoods of the classification over the period of time; and display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time.