US 11,925,469 B2
Non-invasive cardiac monitor and methods of using recorded cardiac data to infer a physiological characteristic of a patient
Steven Szabados, Sausalito, CA (US); Yuriko Tamura, San Mateo, CA (US); Xixi Wang, South San Francisco, CA (US); and George Mathew, Berkeley, CA (US)
Assigned to iRhythm Technologies, Inc., San Francisco, CA (US)
Filed by iRhythm Technologies, Inc., San Francisco, CA (US)
Filed on Jul. 1, 2022, as Appl. No. 17/856,329.
Application 17/856,329 is a continuation of application No. 17/671,285, filed on Feb. 14, 2022, granted, now 11,382,555.
Application 17/671,285 is a continuation of application No. 17/174,143, filed on Feb. 11, 2021, granted, now 11,246,524, issued on Feb. 15, 2022.
Claims priority of provisional application 63/090,951, filed on Oct. 13, 2020.
Claims priority of provisional application 62/975,626, filed on Feb. 12, 2020.
Prior Publication US 2022/0330874 A1, Oct. 20, 2022
Int. Cl. A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61B 5/257 (2021.01); A61B 5/28 (2021.01); A61B 5/352 (2021.01); A61B 5/361 (2021.01); A61B 5/363 (2021.01); G06F 21/62 (2013.01)
CPC A61B 5/361 (2021.01) [A61B 5/0006 (2013.01); A61B 5/11 (2013.01); A61B 5/257 (2021.01); A61B 5/28 (2021.01); A61B 5/352 (2021.01); A61B 5/363 (2021.01); A61B 5/4809 (2013.01); A61B 5/6801 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); G06F 21/6245 (2013.01); A61B 2560/0406 (2013.01); A61B 2562/0219 (2013.01); A61B 2562/166 (2013.01); A61B 2562/168 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A system comprising a wearable monitor device for monitoring signal data of a patient, the wearable monitor device comprising:
a housing comprising a surface configured to be engaged to the patient;
a sensor positioned to detect continuous physiological signals of the patient while the surface is engaged to the patient;
a hardware processor configured to:
determine an activity level of the patient;
select a machine learning model based at least in part on the activity level of the patient;
access an encoder of the machine learning model, wherein the encoder of the machine learning model is trained together with a decoder of the machine learning model; and
process the detected continuous physiological signals through the encoder; and
a transmitter configured to transmit an output of the encoder or a signal derived from the output of the encoder to a computing system, wherein the computing system is configured to process the output of the encoder or the signal derived from the output of the encoder via the decoder.