The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 04, 2020
Filed:
May. 05, 2016
The Johns Hopkins University, Baltimore, MD (US);
Boston Scientific Scimed Inc., Maple Grove, MN (US);
Qian Liu, Toronto, CA;
Nichaluk Leartprapun, Ithaca, NY (US);
Jackline Wanjala, San Francisco, CA (US);
Soumyadipta Acharya, Baltimore, MD (US);
Andrew Bicek, Elk River, MN (US);
Viachaslau Barodka, Baltimore, MD (US);
Umang Anand, Plymouth, MN (US);
Majd Alghatrif, Baltimore, MD (US);
David Kass, Baltimore, MD (US);
B. Westbrook Bernier, Miami, FL (US);
Chao-Wei Hwang, West Friendship, MD (US);
Peter Johnston, Baltimore, MD (US);
Trent Langston, Costa Mesa, CA (US);
The Johns Hopkins University, Baltimore, MD (US);
Boston Scientific Scimed Inc., Maple Grove, MN (US);
Abstract
The present application relates to systems and methods for non-invasively determining at least one of left ventricular end diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) in a subject's heart, comprising: receiving, by a computer, a plurality of signals from a plurality of non-invasive sensors that measure a plurality of physiological effects that are correlated with functioning of said subject's heart, said plurality of physiological effects including at least one signal correlated with left ventricular blood pressure and at least one signal correlated with timing of heartbeat cycles of said subject's heart; training a machine learning model on said computer using said plurality of signals for periods of time in which said plurality of signals were being generated during a heart failure event of said subject's heart; determining said LVEDP or PCWP using said machine learning model at a time subsequent to said training and subsequent to said heart failure event.