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Automated EEG-based screening of depression using deep convolutional neural network.

U Rajendra Acharya1, Shu Lih Oh2, Yuki Hagiwara2

  • 1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.

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Summary

A new deep learning model accurately screens for depression using Electroencephalogram (EEG) signals. This advanced machine learning approach shows higher accuracy with right hemisphere EEG data, suggesting its potential for diagnosing depression severity.

Keywords:
Convolutional neural networkDeep learningDepressionEEGElectroencephalogram

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Electroencephalogram (EEG)-based diagnosis is increasingly utilizing neurocomputing and machine learning.
  • Deep neural networks offer advanced capabilities for analyzing complex biological signals.

Purpose of the Study:

  • To present a novel computer model for EEG-based depression screening.
  • To utilize a deep neural network, specifically a Convolutional Neural Network (CNN), for automated feature learning from EEG signals.

Main Methods:

  • Developed a deep learning model (CNN) that automatically learns features from EEG signals.
  • Tested the model on EEG data from 15 normal and 15 depressed patients.
  • Compared classification accuracy using left and right hemisphere EEG signals.

Main Results:

  • The CNN model achieved high accuracies: 93.5% (left hemisphere) and 96.0% (right hemisphere).
  • EEG signals from the right hemisphere demonstrated greater distinctiveness in differentiating depressive states.
  • Findings align with research linking depression to right hemisphere hyperactivity.

Conclusions:

  • The proposed CNN model offers an effective, automated method for EEG-based depression screening.
  • Right hemisphere EEG signals are more indicative of depression compared to the left.
  • Future work could involve diagnosing depression severity and developing a Depression Severity Index (DSI).