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AI Biotech/Diagnostics: Other Innovation
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Application US20190090769
Published 2019-03-28
System And Method For Machine-learning-based Atrial Fibrillation Detection
A system and method for machine-learning based atrial fibrillation detection are provided. A database is maintained that is operable to maintain a plurality of ECG features and annotated patterns of the features. At least one server is configured to: train a classifier based on the annotated patterns in the database; receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; use the trained classifier to identify patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculate a score indicative of whether the portion of the representation within that ECG signal is associated the patient experiencing atrial fibrillation; and take an action based on the score.
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- 1. A system for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising:
a database operable to maintain a plurality of electrocardiography (ECG) features and annotated patterns of the features, at least some of the patterns associated with atrial fibrillation; at least one server interconnected to the database, the at least one server configured to:
train a classifier based on the annotated patterns in the database;
receive a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows;
detect a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows;
use the trained classifier to identify patterns of the ECG features within one or more of the portions of the ECG signal;
for each of the portions, calculate a score indicative of whether the portion of the representation within that ECG signal is associated the patient experiencing atrial fibrillation; and
take an action based on the score.
- 11. A method for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising:
maintaining in a database a plurality of electrocardiography (ECG) features and annotated patterns of the features, at least some of the patterns associated with atrial fibrillation; training by an at least one server connected to the database a classifier based on the annotated patterns in the database; receiving by the at least one server a representation of an ECG signal recorded by an ambulatory monitor recorder during a plurality of temporal windows; detecting by the at least one server a plurality of the ECG features in at least some of the portions of the representation falling within each of the temporal windows; using by the at least one server the trained classifier to identify patterns of the ECG features within one or more of the portions of the ECG signal; for each of the portions, calculating by the at least one server a score indicative of whether the portion of the representation within that ECG signal is associated the patient experiencing atrial fibrillation; and taking by the at least one server an action based on the score.
