Application US20200352466 AI Biotech/Diagnostics: Other Innovation

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Application US20200352466

Published 2020-11-12

Arrythmia Detection With Feature Delineation And Machine Learning

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

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3 Independent Claims

1. A method comprising: receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device; applying, by the computing device, a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient; performing, by the computing device, feature-based delineation of the received cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data; in response to determining that the episode of arrhythmia has occurred in the patient: generating, by the computing device, a report comprising an indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia; and outputting, by the computing device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia.
12. A method comprising: receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device; obtaining, by the computing device, a first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data, wherein the feature-based delineation identifies cardiac features present in the cardiac electrogram data; applying, by the computing device, a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, a second classification of arrhythmia in the patient; determining, by the computing device and based on the first classification and second classification, that an episode of arrhythmia has occurred in the patient; and in response to determining that the episode of arrhythmia has occurred in the patient: generating, by the computing device, a report comprising an indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia; and outputting, by the computing device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia.
20. A computing device comprising processing circuitry and a storage medium, wherein the processing circuitry is configured to: receive cardiac electrogram data of a patient sensed by a medical device; apply a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient; perform feature-based delineation of the received cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data; in response to determining that the episode of arrhythmia has occurred in the patient: generate a report comprising an indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia; and output, for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia.