Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network-Reference-Cited by-同舟云学术

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network

Published:2024 Issue: Volume:32 Page:33-42
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Yuan Xue¹^ORCID,Chen Maozhou¹,Ding Peng¹,Gan Anan¹,Gong Anmin²,Chu Zhaosong³^ORCID,Nan Wenya⁴,Fu Yunfa¹^ORCID,Cheng Yuqi³

Affiliation:

1. Faculty of Information Engineering and Automation and the Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China

2. School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China

3. Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China

4. Department of Psychology, College of Education, Shanghai Normal University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10354389.pdf?arnumber=10354389

Reference52 articles.

1. A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data

2. Course and prognosis;Malhi;Lancet,2018

3. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

4. Depression Detection Using Feature Extraction and Deep Learning from sMRI Images

5. A Region-Based Feature Extraction Method for Rs-fMRI of Depressive Disorder Classification

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