Abstract: |
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. |
Inventor: |
Boleyn, Rodney (Bellevue, WA, US); Dreisbach, Ezra M. (Vashon, WA, US); Dulken, Chuck (Sammamish, WA, US); Bardy, Gust H. (Carnation, WA, US) |
Applicant: |
Bardy Diagnostics, Inc. (Seattle, WA, US) |
Face Assignee: |
N/A |
Filed: |
2018-11-26 |
Issued: |
2019-03-28 |
Claims: |
20 |
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US20190090769
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1. A system for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising:
(6)
(2)
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11. A method for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising:
(6)
(7)
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