US 11,915,825 B2
Systems and methods of analyte measurement analysis
Conner Daniel Cross Galloway, Sunnyvale, CA (US); Alexander Vainius Valys, Sunnyvale, CA (US); Frank Losasso Petterson, Los Altos Hills, CA (US); and Daniel Treiman, San Francisco, CA (US)
Assigned to AliveCor, Inc., Mountain View, CA (US)
Filed by AliveCor, Inc., Mountain View, CA (US)
Filed on Feb. 12, 2018, as Appl. No. 15/894,775.
Claims priority of provisional application 62/570,432, filed on Oct. 10, 2017.
Claims priority of provisional application 62/457,713, filed on Feb. 10, 2017.
Prior Publication US 2018/0233227 A1, Aug. 16, 2018
Int. Cl. G06N 20/00 (2019.01); G06N 3/08 (2023.01); A61B 5/145 (2006.01); G16H 50/20 (2018.01); G06N 3/04 (2023.01); A61B 5/00 (2006.01); A61B 5/349 (2021.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); A61B 5/25 (2021.01)
CPC G16H 50/20 (2018.01) [A61B 5/14546 (2013.01); A61B 5/349 (2021.01); A61B 5/7267 (2013.01); G06N 3/04 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); A61B 5/25 (2021.01); A61B 5/743 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A system for non-invasively predicting a level of an analyte comprising:
an electrocardiogram sensor; and
a processing device operatively coupled to the electrocardiogram sensor, wherein the processing device is to:
analyze electrocardiogram training data comprising an electrocardiogram of each of a plurality of subjects and one or more measured analyte levels associated with each electrocardiogram to determine an associated label for each electrocardiogram of the electrocardiogram training data, the associated label for each electrocardiogram of the electrocardiogram training data based on a regression line fitted to the one or more measured analyte levels associated with each electrocardiogram, wherein the associated label is determined from the regression line at a time of measurement of the associated electrocardiogram;
train a machine learning model by:
analyzing each electrocardiogram of the electrocardiogram training data to generate an output; and
comparing the output generated for each electrocardiogram of the electrocardiogram training data to the associated label for each electrocardiogram of the electrocardiogram training data to update the machine learning model using backpropagation, wherein one or more weight matrices of the machine learning model are adjusted based on a confidence interval associated with the associated label for each electrocardiogram of the electrocardiogram training data;
receive electrocardiogram data of a subject from the electrocardiogram sensor; and
apply the machine learning model to the received electrocardiogram data to determine an indication of a measured analyte level of the subject based on the electrocardiogram data.