Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 6; pp. 2265 - 2275
• Main Authors: Zhang, Xiaowei, Shen, Jian, Din, Zia ud, Liu, Jinyong, Wang, Gang, Hu, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain modeling; Classifiers; Correlation; Decisions; Depression - diagnosis; EEG; Electroencephalography; Electroencephalography - methods; Feature extraction; Female; Humans; Interviews; late fusion; Male; Mental depression; Mental disorders; multi-agent strategy; multi-modal; Multiagent systems; Pervasive depression detection; Physiology; Signal Processing, Computer-Assisted; Sound Spectrography - methods; Speech - classification; Strategy; Tensors

Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study...