Application US20190090769

SYSTEM AND METHOD FOR MACHINE-LEARNING-BASED ATRIAL FIBRILLATION DETECTION
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US20190090769

1. A system for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising: (6) (2)

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.

2. A system for machine-learning-based atrial fibrillation detection according to claim 1, the at least one server further configured to: (0) (2)

3. A system for machine-learning-based atrial fibrillation detection according to claim 1, the at least one server further configured to: (0) (1)

4. A system for machine-learning-based atrial fibrillation detection according to claim 1, the at least one server further configured to: (3) (3)

calculate a further score indicative of whether the portion of the representation within that ECG signal is associated with the patient not experiencing atrial fibrillation;

compare the further score to the score;

determine that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation based on the comparison.

5. A system for machine-learning-based atrial fibrillation detection according to claim 4, wherein the determination is made upon the score exceeding the further score. (0)

6. A system for machine-learning-based atrial fibrillation detection according to claim 4, wherein the action comprises sending an alert of the determination. (0)

7. A system for machine-learning-based atrial fibrillation detection according to claim 4, the at least one server further configured to: (0) (2)

8. A system for machine-learning-based atrial fibrillation detection according to claim 1, wherein each of the temporal windows is between 2 and 60 seconds. (0)

9. A system for machine-learning-based atrial fibrillation detection according to claim 1, wherein the database comprises 32 of the ECG features. (0)

10. A system for machine-learning-based atrial fibrillation detection according to claim 1, the at least one server further configured to: (0) (1)

11. A method for machine-learning-based atrial fibrillation detection with the aid of a digital computer, comprising: (6) (7)

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.

12. A method for machine-learning-based atrial fibrillation detection according to claim 11, further comprising: (0) (2)

13. A method for machine-learning-based atrial fibrillation detection according to claim 11, further comprising: (0) (1)

14. A method for machine-learning-based atrial fibrillation detection according to claim 11, further comprising: (3) (3)

calculating a further score indicative of whether the portion of the representation within that ECG signal is associated with the patient not experiencing atrial fibrillation;

comparing the further score to the score;

determining that the portion of the ECG signal is associated with the patient experiencing atrial fibrillation based on the comparison.

15. A method for machine-learning-based atrial fibrillation detection according to claim 14, wherein the determination is made upon the score exceeding the further score. (0)

16. A method for machine-learning-based atrial fibrillation detection according to claim 14, wherein the action comprises sending an alert of the determination. (0)

17. A method for machine-learning-based atrial fibrillation detection according to claim 14, further comprising: (0) (2)

18. A method for machine-learning-based atrial fibrillation detection according to claim 11, wherein each of the temporal windows is between 2 and 60 seconds. (0)

19. A method for machine-learning-based atrial fibrillation detection according to claim 11, wherein the database comprises 32 of the ECG features. (0)

20. A method for machine-learning-based atrial fibrillation detection according to claim 11, further comprising: (0) (1)

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Application US20190090769

SYSTEM AND METHOD FOR MACHINE-LEARNING-BASED ATRIAL FIBRILLATION DETECTION

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