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BRIEF RESEARCH REPORT article

Front. Psychiatry
Sec. Computational Psychiatry
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1346838
This article is part of the Research Topic Deep Learning for High-Dimensional Sense, Non-Linear Signal Processing and Intelligent Diagnosis View all 3 articles

TanhReLU -based Convolutional Neural Networks for MDD classification

Provisionally accepted
Qiao Zhou Qiao Zhou *Sheng Sun Sheng Sun Shuo Wang Shuo Wang
  • Hubei Polytechnic University, Huangshi, China

The final, formatted version of the article will be published soon.

    Major Depression Disorder (MDD), a complex mental health disorder, poses significant challenges in accurate diagnosis. In addressing the issue of gradient vanishing in the classification of MDD using current data-driven electroencephalogram (EEG) data, this study introduces a TanhReLU-based Convolutional Neural Network (CNN). By integrating the TanhReLU activation function, which combines the characteristics of the hyperbolic tangent (Tanh) and rectified linear unit (ReLU) activations, the model aims to improve performance in identifying patterns associated with MDD while alleviating the issue of model overfitting and gradient vanishing. Experimental results demonstrate promising outcomes in the task of MDD classification upon the publicly available EEG data, suggesting potential clinical applications.

    Keywords: Classification, Major depression disorder (MDD), EEG, TanhReLU, CNN

    Received: 30 Nov 2023; Accepted: 08 May 2024.

    Copyright: © 2024 Zhou, Sun and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qiao Zhou, Hubei Polytechnic University, Huangshi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.