Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals
Author:
Affiliation:
1. School of Medical Technology, Beijing Institute of Technology, Beijing, China
2. Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Funder
Beijing Institute of Technology Research Fund Program for Young Scholars
Fundamental Research Funds for the Central Universities
National Key Research and Development Program of China
National Natural Science Foundation of China
MOST
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10171108/10107698.pdf?arnumber=10107698
Reference63 articles.
1. An electroencephalography connectome predictive model of major depressive disorder severity
2. Quantitative EEG abnormalities in major depressive disorder with basal ganglia stroke with lesions in different hemispheres
3. EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers
4. Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study
5. EEG-based mild depressive detection using feature selection methods and classifiers
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1. Physiological Electrosignal Asynchronous Acquisition Technology: Insight and Perspectives;IEEE Transactions on Computational Social Systems;2024-02
2. Diagnosis of Depression Based on New Features Extractive from the Frequency Space of the EEG;2023 13th International Conference on Computer and Knowledge Engineering (ICCKE);2023-11-01
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