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:
May. 30, 2023

Filed:

May. 12, 2017
Applicant:

Siemens Healthcare Gmbh, Erlangen, DE;

Inventors:

Ahmet Tuysuzoglu, Plainsboro, NJ (US);

Tiziano Passerini, Plainsboro, NJ (US);

Shun Miao, Princeton, NJ (US);

Tommaso Mansi, Plainsboro, NJ (US);

Assignee:

Siemens Healthcare GmbH, Erlangen, DE;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G06N 3/08 (2023.01); G16H 50/50 (2018.01); G06N 7/08 (2006.01); G06N 3/082 (2023.01); G06F 30/23 (2020.01); A61B 5/00 (2006.01); G16Z 99/00 (2019.01); G06N 3/045 (2023.01); G06N 20/00 (2019.01); G06N 3/048 (2023.01); G06N 5/01 (2023.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); A61B 5/7264 (2013.01); G06F 30/23 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/082 (2013.01); G06N 7/08 (2013.01); G16H 50/50 (2018.01); G16Z 99/00 (2019.02); G06N 3/048 (2023.01); G06N 5/01 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01);
Abstract

A method and system for deep learning based cardiac electrophysiological model personalization is disclosed. Electrophysiological measurements of a patient, such as an ECG trace, are received. A computational cardiac electrophysiology model is personalized by calculating patient-specific values for a parameter of the computational cardiac electrophysiology model based at least on the electrophysiological measurements of the patient using a trained deep neural network (DNN). The parameter of the computational cardiac electrophysiology model corresponds to a spatially varying electrical cardiac tissue property.


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