US 12,226,236 B2
Systems and methods of analyte measurement analysis
Conner Daniel Cross Galloway, Sunnyvale, CA (US); Alexander Vainius Valys, Sunnyvale, CA (US); David E. Albert, Oklahoma City, OK (US); and Frank Losasso Petterson, Los Altos Hills, CA (US)
Assigned to AliveCor, Inc., Mountain View, CA (US)
Filed by AliveCor, Inc.
Filed on Jul. 21, 2021, as Appl. No. 17/382,217.
Application 17/382,217 is a continuation of application No. 15/842,419, filed on Dec. 14, 2017, granted, now 11,103,194.
Claims priority of provisional application 62/457,713, filed on Feb. 10, 2017.
Claims priority of provisional application 62/434,339, filed on Dec. 14, 2016.
Prior Publication US 2021/0345972 A1, Nov. 11, 2021
Int. Cl. A61B 5/00 (2006.01); A61B 5/145 (2006.01); A61B 5/332 (2021.01); A61B 5/339 (2021.01); A61B 5/349 (2021.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01)
CPC A61B 5/7267 (2013.01) [A61B 5/14546 (2013.01); A61B 5/332 (2021.01); A61B 5/339 (2021.01); A61B 5/349 (2021.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); A61B 5/14532 (2013.01); A61B 5/4845 (2013.01); A61B 5/4848 (2013.01); A61B 5/746 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A system comprising:
an electrocardiogram sensor; and
at least a processing device operatively coupled to the electrocardiogram sensor, wherein the at least a processing device is configured to:
train an untrained machine learning model, wherein training the untrained machine learning model comprises:
receiving previous electrocardiogram data of a user from the electrocardiogram sensor;
receiving a first measurement of a level of an analyte within the user at a first time;
generating a set of estimated values of the level of the analyte in the user over a time period subsequent to the first time based in part on the level of the analyte within the user at the first time; and
training the untrained machine learning model using the previous electrocardiogram data and one or more of the set of estimated values;
receive current electrocardiogram data of the user from the electrocardiogram sensor;
determine an indication of a current level of the analyte in the user based on the current electrocardiogram data using the trained machine learning model, wherein the current electrocardiogram data is provided to the trained machine learning model as an input to output the current level of the analyte in the user; and
generate an alert to the user and a recommendation to contact a doctor or physician if the indication of the current level of the analyte in the user is above or below a threshold, wherein the at least a processor is further configured to, if the indication of the current level of the analyte in the user is above or below the threshold, provide a request to the user to retest the current electrocardiogram data.