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

Khan, Danish M.; Yahya, Norashikin; Kamel, Nidal; Faye, Ibrahima

doi:10.1109/access.2021.3049427

“…The proposed model by [35] was depression detection by adopting a three-dimensional convolutional neural network (3D-CNN) using effective connectivity inside the brain default mode network (DMN) region which was estimated from 19-channel EEG signals. The process of removing artifacts by eye blinks, muscles, etc.…”

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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“…The proposed model by [36] was depression detection by adopting a three-dimensional convolutional neural network (3D-CNN) using effective connectivity inside the brain default mode network (DMN) region which was estimated from 19-channel EEG signals. The process of removing artifacts by eye blinks, muscles, etc.…”

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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“…Known for its excellent result in various automated classification [31], [32], deep learning approaches are also employed to improve the ASD classification based on FC patterns. Heinsfeld, et al [25], extracted the FC based on the Pearson correlation between voxels time series and feeds to deep neural networks (DNN), giving 70% classification accuracy.…”

Section: A Related Workmentioning

confidence: 99%

Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI

Sadiq

¹

,

Al-Hiyali

²

,

Yahya

³

et al. 2022

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Resting-state functional magnetic resonance imaging (rs-fMRI) is an efficient tool to measure brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) from normal controls (NC). It is established that the fractal nature of neuroimaging signals will affect the estimation of brain's functional connectivity. Therefore, the ordinary correlation of rs-fMRI may not provide the original neuronal activity of the brain. In this work, the non-oscillatory brain connectivity method is proposed to distinguish subtypes of ASD from NC. The three subtypes of ASD namely autistic disorder (ATD), Asperger's disorder (APD), and Pervasive developmental disorder-not other specified (PDD) are classified from NC by extracting the non-oscillatory connectivity from the BOLD rs-fMRI signal. A number of significant connections are extracted by utilizing the p-value analysis and these significant connections are fed to machine learning (ML) classifiers for classification of ASD subtypes against normal control. The performance for binary classification is recorded at accuracy of 98.6%, 97.2%, 97.2%, respectively, for ATD vs. NC, APD vs. NC and PDD vs. NC. Whereas, for multiclass (ATD, APD, PDD and NC), the best accuracy is 88.9%. Both binary and multiclass classification outperformed the conventional Pearson correlation-based connectivity and benchmark approaches in terms of accuracy, sensitivity, specificity. This work demonstrates the great potential of non-oscillatory connectivity approaches, not only for autism diagnosis but also for other neurological disorders.

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

Cited by 46 publications

References 57 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

Non-Oscillatory Connectivity Approach for Classification of Autism Spectrum Disorder Subtypes Using Resting-State fMRI

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