Depression Detection Using Deep Learning

Authors

  • B. Adithya Chowdary  CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • B. Jay Chandra  CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • H.Shiva Teja  CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • P. Madhukar  CSE Department, JB Institute of Engineering and Technology, Hyderabad, India
  • Mr. D. Himagiri  Professor, CSE Department, JB Institute of Engineering and Technology, Hyderabad, India

Keywords:

Convolutional Neural Network, EEG, DL, DeprNet

Abstract

Depression is a common reason for an increase in suicide cases worldwide. Thus, to mitigate the effects of depression, accurate diagnosis and treatment are needed. An electroencephalogram (EEG) is an instrument used to measure and record the brain’s electrical activities. It can be utilized to produce the exact report on the level of depression. Previous studies proved the feasibility of the usage of EEG data and deep learning (DL) models for diagnosing mental illness. Therefore, this study proposes a L-based convolutional neural network (CNN) called DeprNet for classifying the EEG data of depressed and normal subjects. Here, the Patient Health Questionnaire 9 score is used for quantifying the level of depression. The performance of DeprNet is remarkable compared with the other eight baseline models. Furthermore, on visualizing the last CNN layer, it is found that the values of right electrodes are prominent for depressed subjects, whereas, for normal subjects, the values of left electrodes are prominent.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Adithya Chowdary, B. Jay Chandra, H.Shiva Teja, P. Madhukar, Mr. D. Himagiri "Depression Detection Using Deep Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.542-546, March-April-2023.