Abstract
Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients’ responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tool by analyzing brain functional connectivity for focusing on clinical interventions.
Highlights
Depression is one of the most common mental illnesses, which affects our mood, and it has become a global health concern as it affects millions of lives every year
Each experimental design differs according to each depression category, most of the articles summarized in this study considered the depression ideation for Major Depressive Disorder (MDD) and clinical depression
Researchers have devoted themselves to depression biomarker identification through
Summary
Depression is one of the most common mental illnesses, which affects our mood, and it has become a global health concern as it affects millions of lives every year. According to the World Mental Health Survey, every day about 3,000 suicide cases and 60,000 cases are found who may attempt to end their lives [1] and by 2022, we can expect. According to the World Health Organization (WHO), more than 264 million people are affected by depression [3] and about 35.8% of suicides occur due to depression [4]. According to WHO reports, each year about 30-35% of Major Depressive Disorder (MDD) or severely depressed patients have taken suicide attempts, and about 2-15% of them die due to depression [6]. “Differentiate between Support Vector Machine and Logistic Regression GeeksforGeeks.”. Machine,on%20the%20concept%20of%20probability.&text=Th e%20hypothesis%20of%20logistic%20regression,function%20b etween%200%20and%201%20. (accessed May 28, 2021).
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