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.

Date of Patent:
Apr. 13, 2021

Filed:

Jul. 12, 2018
Applicant:

The Chinese University of Hong Kong, Hong Kong, CN;

Inventors:

Ni Zhao, Guangdong, CN;

Peng Su, Hubei, CN;

Yuanting Zhang, Hong Kong, CN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/0205 (2006.01); G16H 50/20 (2018.01); A61B 5/316 (2021.01); A61B 5/318 (2021.01); G06N 3/04 (2006.01); A61B 5/024 (2006.01); A61B 5/021 (2006.01); A61B 5/0245 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7267 (2013.01); A61B 5/0205 (2013.01); A61B 5/316 (2021.01); A61B 5/318 (2021.01); A61B 5/7203 (2013.01); A61B 5/7278 (2013.01); G16H 50/20 (2018.01); A61B 5/0245 (2013.01); A61B 5/02108 (2013.01); A61B 5/02416 (2013.01); G06N 3/04 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01);
Abstract

Methods and devices for long term, cuffless, and continuous arterial blood pressure estimation using an end-to-end deep learning approach are provided. A deep learning system comprises three modules: a deep convolutional neural network (CNN) module for learning powerful features; a one- or multi-layer recurrent neural network (RNN) module for modeling the temporal dependencies in blood pressure dynamics; and a mixture density network (MDN) module for predicting final blood pressure value. This system takes raw physiological signals, such as photoplethysmogram and/or electrocardiography signals, as inputs and yields arterial blood pressure readings in real time.


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