A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography

Li, Xiaowei; La, Rong; Wang, Ying; Hu, Bin; Zhang, Xuemin

doi:10.3389/fnins.2020.00192

Cited by 72 publications

(50 citation statements)

References 67 publications

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“…Several methods for the conversion of 1D signal data into a 2D image have been considered. For example, the STFT, wavelet-based spectrogram method [33], and brain coherence network-based method [34,35] have been proposed. However, to implement an effective 2D CNN image, this study used brain asymmetry, which has been identified as an important biomarker of depression.…”

Section: Discussionmentioning

confidence: 99%

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

Kang

Kwon

Park

et al. 2020

Sensors

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To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

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Section: Discussionmentioning

confidence: 99%

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

Kang

Kwon

Park

et al. 2020

Sensors

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“…Frontal (FP1, FP2, FPz) [68], [82], [100], [70], [ Not Mentioned [47], [48], [43] Most of these studies used EEG data from the individual modality and used a large number of electrodes. Out of 52 articles, 19 research articles have been found where authors have used 19 electrodes EEG device and the electrode positions for the 19-channel EEG are Fp1, Fp2, F3, F4, F7, F8, C3, C4, T3, T4, T5, T6, P3, P4, O1, O2, Fz, Cz, Pz.…”

Section: ) Signal Acquisition and Electrode Placementmentioning

confidence: 99%

“…Cutoff frequency/ Bandpass filters [61], [63], [42], [36], [117] , [48], [43], [87], [72], [88] Certain ranges of high Bandpass and low Bandpass filters are used to reduce undesired large signal distortions.…”

Section: Research Articles Remarksmentioning

confidence: 99%

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

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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“…Yunlong et al [20] used combination of brain functional network based on phase lag index (PLI) and a simple 2D CNN, giving 67.67% classification accuracy tested on 10 HC and 10 patients. Similar combination was also used by Xiaowei et al [21] in which functional connectivity from different EEG bands was transformed into images and trained on 2-stacked CNN to achieve 80.74% classification accuracy for mild depression patients and HC. In [22], mixed feature matrices obtained from inter-hemispheric asymmetry and cross-correlation of EEG signals from 64 electrodes were used for training of a 2D CNN achieving 94.13% classification accuracy for 16 MDD and 16 HC.…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network

et al. 2021

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A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography

Cited by 72 publications

References 67 publications

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

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

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

Automated Diagnosis of Major Depressive Disorder Using Brain Effective Connectivity and 3D Convolutional Neural Network

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