Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

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. Preprints 2021, 2021070028 (doi: 10.20944/preprints202107.0028.v1). Safayari, A.; Bolhasani, H. Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review. Preprints 2021, 2021070028 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.