Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning

To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. The sleep EEG signals of 28 patients with depressive disorder and 37 normal cont...

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Bibliographic Details
Published in:Sichuan da xue xue bao. Journal of Sichuan University. Yi xue ban 2023-03, Vol.54 (2), p.287-292
Main Authors: Tao, Ran, Ding, Sheng-Nan, Chen, Jie, Zhu, Xue-Min, Ni, Zhao-Jun, Hu, Ling-Ming, Zhang, Yang, Xu, Yan, Sun, Hong-Qiang
Format: Article
Language:Chinese
Subjects:
Deep Learning
Depressive Disorder
Electroencephalography - methods
Humans
Sleep Stages
Sleep, REM
Online Access:Get full text
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Summary:To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder. Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG f
ISSN:1672-173X
DOI:10.12182/20230360212