Robust sparse graph regularized nonnegative matrix factorization for automatic depression diagnosis-Reference-Cited by-同舟云学术

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Robust sparse graph regularized nonnegative matrix factorization for automatic depression diagnosis

Published:2024-07 Issue: Volume:93 Page:106036
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Zhang Lu,Zhong Jitao,Wang Quanhong,Zhu Jinzhou,Liu Hele,Peng Hong,Hu Bin^ORCID

Funder

National Natural Science Foundation of China

Lanzhou University

National Key Research and Development Program of China

Ministry of Science and Technology, Taiwan

Ministry of Science and Technology of the People's Republic of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Reference61 articles.

1. The economic case for the prevention of mental illness;McDaid;Annu. Rev. Public Health,2019

2. A deep multiscale spatiotemporal network for assessing depression from facial dynamics;de Melo;IEEE Trans. Affect. Comput.,2022

3. A method for diagnosing depression: Facial expression mimicry is evaluated by facial expression recognition;Fu;J. Affect. Disord.,2023

4. 2-level hierarchical depression recognition method based on task-stimulated and integrated speech features;Xing;Biomed. Signal Process. Control,2022

5. Multimodal mild depression recognition based on EEG-EM synchronization acquisition network;Zhu;IEEE Access,2019

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