Depression Detection Using an Automatic Sleep Staging Method With an Interpretable Channel-Temporal Attention Mechanism
Author:
Affiliation:
1. School of Software, South China Normal University, Guangzhou, China
2. Department of Sleep Medicine, Guangdong Provincial People’s Hospital, Guangzhou, China
3. Pazhou Lab, Guangzhou, Haizhu, China
Funder
STI 2030-Major Projects
National Natural Science Foundation of China
Special Innovation Projects of Colleges and Universities in Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7274989/10633860/10415278.pdf?arnumber=10415278
Reference51 articles.
1. A different depression: clinical distinctions between bipolar and unipolar depression
2. Depression diagnoses following the identification of bipolar disorder: costly incongruent diagnoses
3. Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
4. Minimal EEG channel selection for depression detection with connectivity features during sleep
5. Artificial intelligence approach to classify unipolar and bipolar depressive disorders
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness;IEEE Transactions on Cognitive and Developmental Systems;2024-08
2. GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals;Journal of Neural Engineering;2024-06-01
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