WS-BiLSTM-MA: Wavelet Scattering-Based BiLSTM With Mixed Attention Block for MDD Recognition Using Multichannel EEG Signals

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 13
• Main Authors: Zhang, Feng, Yang, Chunfeng, You, Linlin, Wang, Xiaojia, Yuan, Yonggui, Jeannes, Regine Le Bouquin, Shu, Huazhong, Xiang, Wentao
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Attention mechanism; bidirectional long short-term memory network (BiLSTM); Character recognition; Classification; Convolutional neural networks; Datasets; Electrodes; Electroencephalography; Experiments; Feature extraction; Hospitals; Long short term memory; major depressive disorder (MDD) recognition; multichannel electroencephalography (EEG) signals; Performance enhancement; Performance evaluation; Recall; Recognition; S matrix theory; Scattering; Scattering coefficient; wavelet scattering network

Major depressive disorder (MDD) recognition using multichannel electroencephalography (EEG) signals has profound clinical value with its richness and accessibility of temporal information, but such signals may suffer from nonstationarity and redundant characteristics. To cope with these problems, th...