Functional Near-Infrared Spectroscopy-Based Computer-Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter-Hemispheric Asymmetry in Prefrontal Hemodynamic Responses.

Journal: Depression and anxiety
PMID: 40226684

Abstract

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy.

Authors

  • Kyeonggu Lee
    Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
  • Jinuk Kwon
    Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
  • Minyoung Chun
    Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
  • JongKwan Choi
    OBELAB Inc., Seoul, Republic of Korea.
  • Seung-Hwan Lee
    Department of Psychiatry, Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.
  • Chang-Hwan Im
    Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.

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