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:
Jun. 25, 2024
Filed:
Oct. 02, 2018
Advanced Telecommunications Research Institute International, Kyoto, JP;
Hiroshima University, Higashihiroshima, JP;
Giuseppe Lisi, Soraku-gun, JP;
Jun Morimoto, Soraku-gun, JP;
Mitsuo Kawato, Soraku-gun, JP;
Takashi Yamada, Soraku-gun, JP;
Naho Ichikawa, Hiroshima, JP;
Yasumasa Okamoto, Hiroshima, JP;
ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL, Soraku-Gun, JP;
HIROSHIMA UNIVERSITY, Higashihiroshima, JP;
Abstract
Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression. The classifier is generated so as to discriminate a disease label of a depressive symptom based on a weighted sum of a plurality of functional connectivities selected by feature selection as being relevant to the disease label of the depressive symptom through machine learning from among functional connectivities of the plurality of predetermined regions. The discriminating device further includes a processor configured to execute discriminating processing of generating a classification result for the depressive symptom of the subject by using the classifier.