The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 21, 2023

Filed:

Apr. 16, 2020
Applicant:

Medtronic, Inc., Minneapolis, MN (US);

Inventors:

Siddharth Dani, Minneapolis, MN (US);

Tarek D. Haddad, Minneapolis, MN (US);

Donald R. Musgrove, Minneapolis, MN (US);

Andrew Radtke, Minneapolis, MN (US);

Niranjan Chakravarthy, Singapore, SG;

Rodolphe Katra, Blaine, MN (US);

Lindsay A. Pedalty, Minneapolis, MN (US);

Assignee:

Medtronic, Inc., Minneapolis, MN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); A61N 1/39 (2006.01); G16H 50/50 (2018.01); G16H 10/60 (2018.01); G16H 40/63 (2018.01); A61N 1/365 (2006.01);
U.S. Cl.
CPC ...
A61N 1/3956 (2013.01); A61N 1/36592 (2013.01); G16H 10/60 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01);
Abstract

Techniques are disclosed for monitoring a patient for the occurrence of a cardiac arrhythmia. A computing system generates sample probability values by applying a machine learning model to sample patient data. The machine learning model determines a respective probability value that indicates a probability that the cardiac arrhythmia occurred during each respective temporal window. The computing system outputs a user interface comprising graphical data based on the sample probability values and receives, via the user interface, an indication of user input to select a probability threshold for a patient. The computing system receives patient data for the patient and applies the machine learning model to the patient data to determine a current probability value. In response to the determination that the current probability exceeds the probability threshold for the patient, the computing system generates an alert indicating the patient has likely experienced the occurrence of the cardiac arrhythmia.


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