2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 2 , PP: 109-118, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Madhu Sudhan H. V. 1 * , S. Saravana Kumar 2
Doi: https://doi.org/10.54216/FPA.140209
Depression is one of the common psychological disorders that affects many people all over the world. The primary typical behavior of depression is persistent low mood, and it is one of the main reasons for disability worldwide. Due to the lack of awareness, treatment, and social stigma, it is leading to suicide and self-harm. It is necessary to identify the depression at a very initial stage to overcome further complications that may lead to suicide. In recent years, certain studies have been done on identifying depression through Machine Learning and Deep Learning techniques. Electroencephalogram (EEG) can be used to detect depression since it is easy to record and non-invasive. The current paper focuses on developing an algorithm that will use the brain signals received through EEG and predict the person as Healthy or with Major Depressive Disorder (MDD) with the help of CNN through an asymmetry matrix, which achieved an accuracy of 89.5%, and it outperformed the previous traditional models. The current study shows that depression detection through EEG is one of the efficient techniques for detecting depression at its early stages.
Clinical Depression , Artificial Intelligence , Machine Learning , Pattern Recognition , Mathematical Fusion , Convolutional Neural Network , Electroencephalogram (EEG) , Depression , EEG Asymmetry , European Data Format , EEG Visualization , Fusion Based
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