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AI Biotech/Diagnostics: Other Innovation
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Application US20200357519
Published 2020-11-12
Reduced Power Machine Learning System For Arrhythmia Detection
Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.
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- 1. A method comprising:
sensing, by a medical device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient; performing, by the medical device, feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient; determining, by the medical device and based on the feature-based delineation, that the cardiac features satisfy threshold criteria for application of a machine learning model for verifying that the episode of arrhythmia has occurred in the patient; in response to determining that the cardiac features satisfy the threshold criteria for application of the machine learning model, applying, by the medical device, the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient; and in response to verifying, by the machine learning model, that the episode of arrhythmia has occurred in the patient:
generating, by the medical 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 medical 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.
- 15. A medical device comprising:
storage medium; and processing circuitry operably coupled to the storage medium and configured to:
sense cardiac electrogram data of a patient;
perform feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient;
determine, based on the feature-based delineation, that the cardiac features satisfy threshold criteria for application of a machine learning model for verifying that the episode of arrhythmia has occurred in the patient;
in response to determining that the cardiac features satisfy the threshold criteria for application of the machine learning model, apply the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient; and
in response to verifying, by the machine learning model, 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.
- 21. A medical device comprising:
storage medium; and processing circuitry operably coupled to the storage medium and configured to:
sense cardiac electrogram data of a patient;
perform feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data;
determine a similarity of the obtained cardiac features to cardiac features of each entry of a plurality of entries of an arrhythmia dictionary of the medical device, wherein each entry of the plurality of entries of the arrhythmia dictionary comprises a classification of arrhythmia of a plurality of classifications of arrhythmia in the patient and cardiac features that demonstrate the classification of arrhythmia;
in response to determining that the obtained cardiac features are not similar to the cardiac features of each entry of the plurality of entries of the arrhythmia dictionary, apply a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia of a first classification has occurred in the patient; and
store, in the arrhythmia dictionary, a first entry comprising the first classification of the episode of arrhythmia and the obtained cardiac features.
