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. 12, 2022

Filed:

Oct. 16, 2018
Applicant:

University of South Florida, Tampa, FL (US);

Inventors:

Abolfazl Saghafi, Tampa, FL (US);

Chris P. Tsokos, Tampa, FL (US);

Assignee:

University of South Florida, Tampa, FL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/24 (2021.01); A61B 5/372 (2021.01); G06F 17/18 (2006.01); G06N 7/00 (2006.01); A61B 5/316 (2021.01);
U.S. Cl.
CPC ...
A61B 5/4076 (2013.01); A61B 5/24 (2021.01); A61B 5/2415 (2021.01); A61B 5/316 (2021.01); A61B 5/725 (2013.01); G06F 17/18 (2013.01); G06N 7/00 (2013.01);
Abstract

Methods and systems detect a random state change in a subject in real time. Eye state changes may be identified in encephalogram brain signals, or honeybee dance patterns may be classified. Multivariate signals including state change information are received via a plurality of channels. The signals are sampled and may be filtered to remove DC components. Statistical characteristics of the signals are monitored. When the statistical characteristics exceed a threshold during a critical time interval, a potential change of state is detected. The critical time segment of the signals may be filtered to generate respective state change artifact signals. The state change artifact signals are decomposed by MEMD, and intrinsic mode functions are generated. Features are extracted from the intrinsic mode functions. These steps may be repeated while the extracted features are provided to a logistic regression classifier that is used to predict a state of the subject.


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