Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Saeedi, Abdolkarim; Saeedi, Maryam; Maghsoudi, Arash; Shalbaf, Ahmad

doi:10.1007/s11571-020-09619-0

Cited by 108 publications

(63 citation statements)

References 43 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%

“…Wajid Mumtaz (2016) [12] Frequency power + asymmetry feature SVM 98.4 Shalini Mahato (2018) [14] Alpha power + RWE MLPNN 93. 33 Wajid Mumtaz (2019) [18] raw EEG 1D CNN 98.32 Shalini Mahato (2019) [13] Alpha power + theta asymmetry SVM 88.33 Abdolkarim Saeedi (2020) [35] Effective Similar to other studies, the size of the data set is the limitation of the study. Because small datasets are at risk of overfitting.…”

Section: Methods Classification Methods Accuracymentioning

confidence: 99%

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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%

Section: Methods Classification Methods Accuracymentioning

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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“…This study had some limitations. First, due to a lack of time series data, we were unable to perform time series-based deep learning models such as one-dimensional convolutional neural network and long short-term memory models [ 63 ]. Similarly, due to a lack of time series data, we were unable to divide training and testing datasets separately according to a timeline, for example, patients before 2015 for training and the patients beyond 2015 for testing in the cross-validation procedure.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

Lin

Kuo

Lin

et al. 2021

JPM

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In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

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“…Saeedi A et al [34] practiced using generalized partial directed coherence (GPDC) and direct directed transfer function (DDTF) along with deep learning algorithms to achieve the goal of detection. GPDC and DDTF were intended to extract the available connection between EEG signals channels as an effective analysis of brain connectivity, combined with eight frequency bands in pairs.…”

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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Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Cited by 108 publications

References 43 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

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

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

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