US 10,376,192 B2
System and method for contactless blood pressure determination
Kang Lee, Toronto (CA); Evgueni Kabakov, North York (CA); and Phil Levy, Brampton (CA)
Assigned to NURALOGIX CORPORATION, Toronto, Ontario (CA)
Filed by NURALOGIX CORPORATION, Toronto (CA)
Filed on Mar. 16, 2018, as Appl. No. 15/923,225.
Application 15/923,225 is a continuation of application No. PCT/CA2017/051533, filed on Dec. 19, 2017.
Claims priority of provisional application 62/435,942, filed on Dec. 19, 2016.
Prior Publication US 2018/0199870 A1, Jul. 19, 2018
Int. Cl. A61B 5/1455 (2006.01); A61B 5/00 (2006.01); A61B 5/021 (2006.01); A61B 5/0205 (2006.01); A61B 5/026 (2006.01); A61B 5/145 (2006.01); G16H 50/20 (2018.01); G16H 40/63 (2018.01); G16H 50/30 (2018.01); A61B 5/08 (2006.01); A61B 5/024 (2006.01)
CPC A61B 5/14551 (2013.01) [A61B 5/021 (2013.01); A61B 5/0205 (2013.01); A61B 5/026 (2013.01); A61B 5/145 (2013.01); A61B 5/1455 (2013.01); A61B 5/725 (2013.01); A61B 5/7267 (2013.01); A61B 5/7278 (2013.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); A61B 5/0037 (2013.01); A61B 5/024 (2013.01); A61B 5/0261 (2013.01); A61B 5/0816 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A method for contactless blood pressure determination of a human subject, the method executed on one or more processors, the method comprising:
receiving a captured image sequence of light re-emitted from the skin of one or more humans;
determining, using a trained hemoglobin concentration (HC) changes machine learning model trained with a HC changes training set, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes training set comprising the captured image sequence;
determining a blood flow data signal of one or more predetermined regions of interest (ROIs) of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes;
applying a plurality of band-pass filters, each having a separate passband, to each of the blood flow data signals to produce a bandpass filter (BPF) signal set for each ROI;
extracting one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs;
building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set comprising the BPF signal set of the one or more predetermined ROIs and the one or more domain knowledge signals;
determining, using the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure for the human subject; and
outputting the determination of blood pressure.