Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Kang, Min Soo; Kwon, Hyunjin; Park, Jinhyeok; Kang, Seokhwan; Lee, Young-Ho

doi:10.3390/s20226526

“…Kang M et al [39] introduced a novel methodology of feature extraction to detect depression which was exploiting asymmetry feature of EEG signals and converting it into 2D images to feed to the convolutional neural network. In the preprocessing level, each channel of raw EEG signals normalized by the min-max normalization method, and then artifacts elimination was done by the independent component analysis (ICA).…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

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

Safayari¹,

Bolhasani²

2021

Preprint

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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.

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“…Kang M et al [40] introduced a novel methodology of feature extraction to detect depression which was exploiting asymmetry feature of EEG signals and converting it into 2D images to feed to the convolutional neural network. In the preprocessing level, each channel of raw EEG signals normalized by the min-max normalization method, and then artifacts elimination was done by the independent component analysis (ICA).…”

Section: Deep Learning Methods For Depression Detection Using Eeg Signalsmentioning

confidence: 99%

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

Safayari¹,

Bolhasani²

2021

Preprint

View full text Add to dashboard Cite

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.

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“…This is a typical pattern seen in relaxed adults and is better visible after the age of 13 [34]. Using the alpha asymmetry image, Kang et al found the best performance for the classification model for detecting depression [36].…”

Section: Fig 2 Different Frequency Bands Of Eeg (Adopted From [31])mentioning

confidence: 96%

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

Dev

¹

,

Roy

²

,

Islam

³

et al. 2022

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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 tools by analyzing brain functional connectivity for focusing on clinical interventions.

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Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

Cited by 36 publications

References 26 publications

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

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

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

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

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