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

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.

Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

“…At the first step, a bandpass filter with cutoff frequencies of 0.1 Hz and 70 Hz was applied to the data, followed by a notch filter to suppress 50 Hz power line noise. Therefore, the frequency bands which do not have considerable influence on the MDD classification, and repetitive spectral noise were removed [37]. Detecting and correcting the background artifacts due to eye blinks, eye movements (vertical and horizontal), muscle and heart activities, and sensor motions were the next step.…”

Multidisciplinary of depression spectrum disorders from a health care system perspective

“…If their severity is significant, psychiatric consultation should take place as soon as possible. If depressive symptoms last only 2-3 days, but their occurrence recurs frequently or cyclically (e.g., monthly), a visit to a psychiatrist is also indicated [24].…”