Version 1
: Received: 30 June 2021 / Approved: 1 July 2021 / Online: 1 July 2021 (13:21:56 CEST)
Version 2
: Received: 9 November 2021 / Approved: 10 November 2021 / Online: 10 November 2021 (15:29:25 CET)
How to cite:
Safayari, A.; Bolhasani, H. Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review. Preprints2021, 2021070028. https://doi.org/10.20944/preprints202107.0028.v1
Safayari, A.; Bolhasani, H. Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review. Preprints 2021, 2021070028. https://doi.org/10.20944/preprints202107.0028.v1
Safayari, A.; Bolhasani, H. Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review. Preprints2021, 2021070028. https://doi.org/10.20944/preprints202107.0028.v1
APA Style
Safayari, A., & Bolhasani, H. (2021). Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202107.0028.v1
Chicago/Turabian Style
Safayari, A. and Hamidreza Bolhasani. 2021 "Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202107.0028.v1
Abstract
Depression is considered by WHO as the main contributor to global disability and it poses dangerous threats to approximately all aspects of human life, in particular public and private health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.
Keywords
Deep Learning; Depression; Electroencephalogram; EEG; Depressive Disorders; Systematic Literature Review
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
