公开(公告)号：US20230420132A1

公开(公告)日：2023-12-28

申请号：US18251337

申请日：2021-11-10

Applicant: UNIVERSITY OF SOUTHERN CALIFORNIA

Inventor： Niema M. PAHLEVAN ,  Rashid ALAVI DEHKORDI ,  Ray V. MATTHEWS

IPC: G16H50/20 ,  A61B5/352 ,  A61B5/00 ,  G16H50/70 ,  G16H50/30 ,  A61B5/021

CPC classification number: G16H50/20 ,  A61B5/352 ,  A61B5/7267 ,  G16H50/70 ,  G16H50/30 ,  A61B5/02108

Abstract: Some embodiments relate to non-invasive techniques for determining whether a patient has experienced heart failure. In some embodiments, a machine learning model may be trained to determine whether the patient has experienced heart failure using blood pressure waveforms and ECGs taken concurrently. Using the intrinsic frequency methodology and the cardiac triangle mapping methodology, features about the patient's cardiac cycle can be extracted and provided to the trained model as input.