Application of Deep Learning Models in the Detection Depression Using Time-Frequency Transformation of Electroencephalogram Signals

Document Type : Original Article(s)

Authors

1 PhD, Department of Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Associate Professor, Department of Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abstract

Background: Major Depressive Disorder (MDD) is a prevalent mental disorder worldwide, and timely diagnosis is necessary for efficient treatment. In the present study, an electroencephalogram (EEG) signal was utilized to automatically and precisely detect MDD using deep learning models.
Methods: Thirty MDD and twenty-eight healthy subjects participated, and their psychological evaluation was conducted by a specialist psychiatrist using the standard Beck questionnaire. 19-channel EEG signals were acquired from all participants in a resting state with eyes closed. Short-Time Fourier Transform (STFT) was applied to the sequential segments of the EEG signals and resulted two-dimensional matrix fed to the deep learning models. DeepEEGNet model was developed based on the EEGNet model utilized for the MDD classification and Healthy subjects. A holdout data was used to test the final model.
Findings: The DeepEEGNet model proposed in this study classified MDD and healthy participants with 84.1% accuracy, 86% sensitivity, and 82.7% specificity.
Conclusion: The deep learning model proposed in this study could accurately classify Healthy subjects and MDD patients using EEG signals and can be utilized as a helpful tool by psychiatrists.

Highlights

Mohsen Sadat Shahabi: Google Scholar, PubMed 

Ahmad Shalbaf: Google Scholar

Keywords

Main Subjects

References

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Journal of Isfahan Medical School
Volume 42, Issue 796
4th Week, February
January and February 2025
Pages 1123-1128

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History

  • Receive Date: 07 July 2024
  • Accept Date: 14 October 2024

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