Development of Wavelet Coherence EEG as a Biomarker for Diagnosis of Major Depressive Disorder

Khan, Danish M.; Masroor, Komal; Jailani, Muhammad Fahim Mohd; Yahya, Norashikin; Yusoff, Mohd Zuki; Khan, Shariq Mahmood

doi:10.1109/jsen.2022.3143176

Cited by 28 publications

(17 citation statements)

References 41 publications

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“…In order to further verify the ability of the proposed model to classify MDD patients and HCs, this paper designed subject-dependent experiments based on the EEG data of MDD patients and HCs as previous studies [37], [38]. Concretely, the proposed model and the five comparison models were carried out five rounds of 10-fold CV experiments.…”

Section: Further Studymentioning

confidence: 99%

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Xia

Zhang

et al. 2023

IEEE Access

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Major depressive disorder (MDD) is a prevalent mental illness associated with abnormalities in structural and functional brain connectivity, and it has become a global public health problem. Early diagnosis is important and challenging for the treatment of MDD. Previous studies proposed the classification methods for MDD based on brain connectivity features through functional connectivity (FC) or effective connectivity (EC) measures. However, it requires prior knowledge and experience to manually select a algorithm to calculate the brain connectivity features. Given that the representation learning capabilities of deep learning (DL) models and the ability to capture correlations between data of self-attention mechanism, we proposed an end-to-end integrated DL model for classifying MDD patients and healthy controls (HCs) based on resting-state electroencephalography (EEG) data. This model first automatically learned the potential connectivity relationships among EEG channels through a multi-head self-attention mechanism, and then extracted higher-level features through a parallel two-branch convolution neural network (CNN) module, and finally completed the classification through a fully connected layer. A public resting-state EEG dataset was utilized to evaluate the validity of the proposed model. The experimental results indicated that the proposed model achieved 91.06% average accuracy that was better than those of comparison methods using the leave-one-subject-out cross-validation (LOSOCV) method. This study may provide a novel approach for brain connectivity modeling of MDD detection.

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Section: Further Studymentioning

confidence: 99%

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Xia

Zhang

et al. 2023

IEEE Access

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“…Nonetheless, in an electroencephalography study a CNN relying on wavelet coherence of the DMN was able to reliably classify MDD patients with over 98%. 124 Concluding, there is a promise in neurodynamic analyses for identifying MDD-associated biomarkers, but the limited number of studies so far only led to a relatively consistent finding of the instability of the DMN network in MDD. More consistent findings of other potentially affected networks related to MDD are needed to draw reliable conclusions.…”

Section: Functional Neurodynamicsmentioning

confidence: 99%

“…To date, no depression fMRI study has implemented wavelet coherence. Nonetheless, in an electroencephalography study a CNN relying on wavelet coherence of the DMN was able to reliably classify MDD patients with over 98% 124 …”

Section: Future Opportunitiesmentioning

confidence: 99%

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

Pilmeyer

Huijbers

Lamerichs

et al. 2022

Journal of Neuroimaging

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Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging-based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network-based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

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“…Machine learning (ML) methods are of increasing interest for the medical industry at present, and it has emerged as an essential part of the diagnosis and treatment of conditions pertaining to oncology, neurology, and cardiology. The process involved in a typical deep learning pipeline for the identification of MDD can be highlighted as follows: region of interest (ROI) extraction from R-fMRI, functional connectivity matrix generation, graph construction, deep learning model training, and classification ( 9 ). Figure 1 depicts the processes required in identifying MDD from HCs.…”

Section: Introductionmentioning

confidence: 99%

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

et al. 2023

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Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

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Development of Wavelet Coherence EEG as a Biomarker for Diagnosis of Major Depressive Disorder

Cited by 28 publications

References 41 publications

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder Classification

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity

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