US 12,226,195 B2
System and methods for determining health-related metrics from collected physiological data
Matthew Steven Whitehill, Seattle, WA (US); Jamien McCullum, Seattle, WA (US); Eric Chen, Seattle, WA (US); and Jessie Young, Seattle, WA (US)
Assigned to MEASURE LABS, INC., Seattle, WA (US)
Filed by Measure Labs, Inc., Seattle, WA (US)
Filed on Jul. 28, 2022, as Appl. No. 17/815,891.
Claims priority of provisional application 63/365,670, filed on Jun. 1, 2022.
Claims priority of provisional application 63/226,541, filed on Jul. 28, 2021.
Prior Publication US 2023/0036114 A1, Feb. 2, 2023
Int. Cl. A61B 5/021 (2006.01); A61B 5/00 (2006.01); A61B 5/1455 (2006.01); A61B 5/024 (2006.01)
CPC A61B 5/021 (2013.01) [A61B 5/14552 (2013.01); A61B 5/4839 (2013.01); A61B 5/486 (2013.01); A61B 5/6898 (2013.01); A61B 5/7221 (2013.01); A61B 5/7267 (2013.01); A61B 5/742 (2013.01); A61B 5/746 (2013.01); A61B 5/02416 (2013.01); A61B 2560/0223 (2013.01); A61B 2560/0431 (2013.01); A61B 2562/0219 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of obtaining a physiological signal representing a patient metric, the method comprising:
acquiring patient physiological data from a sensor operating in ambient conditions;
generating a Photoplethysmography (PPG) signal or a pseudo PPG signal from the acquired patient physiological data, the PPG signal or the pseudo PPG signal having a signal quality characteristic associated with a quality indicator of the PPG signal or the pseudo PPG signal;
comparing the signal quality characteristic of the PPG signal or the pseudo PPG signal to a correlating signal quality characteristic or parameter of a deep learning-based model PPG signal; and
determining that the comparison of the signal quality characteristic of the PPG signal or the pseudo PPG signal to the signal quality characteristic or parameter of the deep-learning based model PPG signal does not meet a signal quality criterion.