US 12,471,786 B2
Methods and systems for arrhythmia tracking and scoring
Ravi Gopalakrishnan, San Francisco, CA (US); Lev Korzinov, San Francisco, CA (US); Fei Wang, San Francisco, CA (US); Euan Thomson, Los Gatos, CA (US); Nupur Srivastava, San Francisco, CA (US); Omar Dawood, San Francisco, CA (US); Iman Abuzeid, San Francisco, CA (US); and David E. Albert, Oklahoma City, OK (US)
Assigned to ALIVECOR, INC., Mountain View, CA (US)
Filed by AliveCor, Inc.
Filed on Oct. 3, 2022, as Appl. No. 17/959,164.
Application 17/959,164 is a continuation of application No. 16/827,310, filed on Mar. 23, 2020, granted, now 11,457,822.
Application 16/827,310 is a continuation of application No. 16/588,201, filed on Sep. 30, 2019, granted, now 10,595,731, issued on Mar. 24, 2020.
Application 16/588,201 is a continuation of application No. 16/153,446, filed on Oct. 5, 2018, granted, now 10,426,359, issued on Oct. 1, 2019.
Application 16/153,446 is a continuation of application No. 15/393,077, filed on Dec. 28, 2016, granted, now 10,159,415, issued on Dec. 25, 2018.
Application 15/393,077 is a continuation of application No. 14/730,122, filed on Jun. 3, 2015, granted, now 9,572,499, issued on Feb. 21, 2017.
Application 14/730,122 is a continuation of application No. 14/569,513, filed on Dec. 12, 2014, granted, now 9,420,956, issued on Aug. 23, 2016.
Claims priority of provisional application 62/014,516, filed on Jun. 19, 2014.
Claims priority of provisional application 61/970,551, filed on Mar. 26, 2014.
Claims priority of provisional application 61/969,019, filed on Mar. 21, 2014.
Claims priority of provisional application 61/953,616, filed on Mar. 14, 2014.
Claims priority of provisional application 61/915,113, filed on Dec. 12, 2013.
Prior Publication US 2023/0099854 A1, Mar. 30, 2023
Int. Cl. A61B 5/00 (2006.01); A61B 5/0205 (2006.01); A61B 5/024 (2006.01); A61B 5/0245 (2006.01); A61B 5/349 (2021.01); A61B 5/361 (2021.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G16Z 99/00 (2019.01); A61B 5/021 (2006.01); A61B 5/11 (2006.01); G16H 15/00 (2018.01); G16H 20/40 (2018.01); G16H 40/63 (2018.01); G16H 50/30 (2018.01)
CPC A61B 5/02055 (2013.01) [A61B 5/0022 (2013.01); A61B 5/02405 (2013.01); A61B 5/02416 (2013.01); A61B 5/0245 (2013.01); A61B 5/349 (2021.01); A61B 5/361 (2021.01); A61B 5/681 (2013.01); A61B 5/6898 (2013.01); A61B 5/7264 (2013.01); A61B 5/7275 (2013.01); A61B 5/746 (2013.01); G16H 10/60 (2018.01); G16H 40/67 (2018.01); G16Z 99/00 (2019.02); A61B 5/021 (2013.01); A61B 5/02438 (2013.01); A61B 5/1118 (2013.01); G16H 15/00 (2018.01); G16H 20/40 (2018.01); G16H 40/63 (2018.01); G16H 50/30 (2018.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
a processing device;
a physiological parameter sensor operatively coupled to the processing device;
an electrocardiogram (ECG) sensor operatively coupled to the processing device; and
a memory, operatively coupled to the processing device, the memory having instructions stored thereon that, when executed by the processing device, cause the processing device to:
receive physiological parameter data of a user from the physiological parameter sensor;
receive ECG data of the user from the ECG sensor;
determine a subset of the ECG data corresponding to an event;
determine an RR interval based on the determined subset of the ECG data;
analyze the RR interval and the physiological parameter data using a machine learning (ML) model to detect a presence of an arrhythmia based on both the RR interval and the physiological parameter data; and
in response to detecting the presence of the arrhythmia, provide one or more recommendations to the user.