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

Journal: Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
PMID: 36949687

Abstract

OBJECTIVE: 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.

Authors

  • Ran Tao
    Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.
  • Sheng-Nan Ding
    School of Biological and Medical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Xue-Min Zhu
    Department of Medical Technology, Peking University Sixth Hospital, Peking University Institute of Mental Health, and Key Laboratory of Mental Health of the National Health Commission (Peking University), Beijing 100191, China.
  • Zhao-Jun Ni
    Department of Sleep Medicine, Peking University Sixth Hospital, Peking University Institute of Mental Health, and Key Laboratory of Mental Health of the National Health Commission (Peking University), Beijing 100191, China.
  • Ling-Ming Hu
    Department of Sleep Medicine, Peking University Sixth Hospital, Peking University Institute of Mental Health, and Key Laboratory of Mental Health of the National Health Commission (Peking University), Beijing 100191, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
  • Hong-Qiang Sun
    Department of Sleep Medicine, Peking University Sixth Hospital, Peking University Institute of Mental Health, and Key Laboratory of Mental Health of the National Health Commission (Peking University), Beijing 100191, China.

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