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. 23, 2019

Filed:

Aug. 28, 2013
Applicant:

The Regents of the University of California, Oakland, CA (US);

Inventors:

Majid Sarrafzadeh, Anaheim, CA (US);

Myung-Kyung Suh, Los Angeles, CA (US);

Mars Lan, Los Angeles, CA (US);

Hassan Ghasemzadeh, Los Angeles, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/0402 (2006.01); A61B 5/00 (2006.01); G06N 99/00 (2019.01); A61B 5/0205 (2006.01); A61B 5/0476 (2006.01); A61B 5/11 (2006.01); G16H 50/20 (2018.01); G06F 19/00 (2018.01);
U.S. Cl.
CPC ...
A61B 5/7282 (2013.01); A61B 5/02055 (2013.01); A61B 5/0402 (2013.01); A61B 5/0476 (2013.01); A61B 5/112 (2013.01); A61B 5/4094 (2013.01); A61B 5/4818 (2013.01); A61B 5/725 (2013.01); A61B 5/726 (2013.01); A61B 5/7264 (2013.01); A61B 5/746 (2013.01); G06F 19/00 (2013.01); G06N 99/005 (2013.01); G16H 50/20 (2018.01); F04C 2270/041 (2013.01);
Abstract

Systems and methods for generalized precursor pattern discovery that work with a wide range of biomedical signals and applications to detect a wide range of medical events are disclosed. In some embodiments, the methods and systems do not require domain-specific knowledge or significant reconfiguration based on the medical event being analyzed, hence it is also possible to discover patterns previously unknown to experts. In some embodiments, to build precursor pattern detection models, the system obtains annotated monitoring data. Positive and negative segments are extracted from the annotated monitoring data, and are preprocessed. Features are extracted from the preprocessed segments, and selected features are chosen from the extracted features. The selected features are classified to create the precursor pattern detection model The precursor pattern detection model may then be used in real time to detect occurrences of the medical event of interest.


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