US 12,406,184 B2
Methods and systems to configure and use neural networks in characterizing physiological systems
Ali Khosousi, Toronto (CA); Timothy William Fawcett Burton, Toronto (CA); Horace R. Gillins, Toronto (CA); Shyamlal Ramchandani, Kingston (CA); William Sanders, Bethesda, MD (US); and Ian Shadforth, Morrisville, NC (US)
Assigned to Analytics For Life Inc., Toronto (CA)
Filed by Analytics For Life Inc., Toronto (CA)
Filed on Apr. 23, 2024, as Appl. No. 18/643,601.
Application 18/643,601 is a continuation of application No. 18/114,753, filed on Feb. 27, 2023, granted, now 11,989,652.
Application 18/114,753 is a continuation of application No. 16/725,430, filed on Dec. 23, 2019, granted, now 11,589,829, issued on Feb. 28, 2023.
Claims priority of provisional application 62/907,141, filed on Sep. 27, 2019.
Claims priority of provisional application 62/784,925, filed on Dec. 26, 2018.
Prior Publication US 2024/0419964 A1, Dec. 19, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/00 (2022.01); A61B 5/00 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06V 10/776 (2022.01)
CPC G06N 3/08 (2013.01) [A61B 5/7275 (2013.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 20/00 (2019.01); G06V 10/776 (2022.01)] 21 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a processor, a biophysical signal data set of a subject acquired from one or more acquisition channels of one or more sensors;
pre-processing, by the processor, the biophysical signal data set to generate one or more pre-processed data sets;
determining, by the processor, a first value indicative of presence or non-presence of cardiac disease or condition by directly inputting at least one of the pre-processed data sets to at least a first deep neural network of a plurality of deep neural networks; and
determining, by the processor, a second value indicative of a location of the presence or non-presence of cardiac disease or condition by directly inputting at least one of the pre-processed data sets to at least a second deep neural network of the plurality of deep neural networks; and
generating, by the processor, an output data set by aggregating a first output from the first deep neural network and a second output from the second deep neural network,
wherein the first and second deep neural networks are trained with a training biophysical signal data set acquired from patients diagnosed with the cardiac disease or condition and labeled with the presence or non-presence of the cardiac disease or condition and location of the cardiac disease or condition, and
wherein the output data set is outputted via a report and/or a display based on the determined first value indicative of the presence or non-presence of cardiac disease or condition and the second value indicative of the location of the cardiac disease or condition.