US 12,451,230 B2
Intra-aortic pressure forecasting
Ahmad El Katerji, Danvers, MA (US); Erik Kroeker, Danvers, MA (US); Elise Jortberg, Danvers, MA (US); Rose Yu, Boston, MA (US); and Rui Wang, Boston, MA (US)
Assigned to ABIOMED, Inc., Danvers, MA (US); and Northeastern University, Boston, MA (US)
Filed by ABIOMED, Inc., Danvers, MA (US); and Northeastern University, Boston, MA (US)
Filed on Mar. 18, 2024, as Appl. No. 18/608,424.
Application 18/608,424 is a continuation of application No. 18/096,589, filed on Jan. 13, 2023, granted, now 11,972,856.
Application 18/096,589 is a continuation of application No. 16/889,457, filed on Jun. 1, 2020, granted, now 11,581,083, issued on Feb. 14, 2023.
Claims priority of provisional application 62/855,389, filed on May 31, 2019.
Prior Publication US 2024/0379212 A1, Nov. 14, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. A61M 60/50 (2021.01); A61B 5/00 (2006.01); A61M 5/172 (2006.01); A61M 60/13 (2021.01); A61M 60/135 (2021.01); A61M 60/174 (2021.01); A61M 60/216 (2021.01); A61M 60/422 (2021.01); A61M 60/531 (2021.01); A61M 60/585 (2021.01); A61M 60/829 (2021.01); A61M 60/857 (2021.01); A61M 60/88 (2021.01); A61M 60/894 (2021.01); G06F 9/445 (2018.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G16H 20/40 (2018.01)
CPC G16H 20/40 (2018.01) [A61M 5/1723 (2013.01); A61M 60/13 (2021.01); A61M 60/174 (2021.01); A61M 60/216 (2021.01); A61M 60/422 (2021.01); A61M 60/531 (2021.01); A61M 60/585 (2021.01); A61M 60/829 (2021.01); A61M 60/857 (2021.01); A61M 60/88 (2021.01); A61M 60/894 (2021.01); G06N 20/00 (2019.01); A61M 2205/3331 (2013.01); A61M 2205/3365 (2013.01); A61M 2205/50 (2013.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising one or more processors configured to:
receive data from a transvalvular micro-axial heart pump during a period of time when the transvalvular micro-axial heart pump is at least partially located in a heart of a patient;
derive a set of features from the received data, wherein the set of features comprises (a) pressure measurements corresponding to pressure values measured by a pressure sensor of the transvalvular micro-axial heart pump, (b) motor speed measurements corresponding to rotational speeds of a motor of the transvalvular micro-axial heart pump, or (c) motor current measurements corresponding to an energy intake of the motor; and
predict, using a trained machine learning model, a cardiac condition of the patient based on the derived set of features, wherein the machine learning model is trained on a data set comprising increasing sequences, decreasing sequences, and stationary sequences, and wherein each sequence comprises pressure measurements, motor speed measurements, or motor current measurements.