ReviewJournal of medical Internet research2020
Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review.
Review in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- Neural Responses to Affective Sentences Reveal Signatures of Depression.Translational psychiatry · 2026Article
- ASYM: multimodal depression recognition via mamba-enhanced attentive feature fusion.Frontiers in psychiatry · 2026Article
- A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.Journal of neuroengineering and rehabilitation · 2025Article
- Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution ofDepression and anxiety · 2024Observational
- Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?Advances in neurobiology · 2024Article
- Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?Advances in neurobiology · 2024Article
- Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy.Brain sciences · 2023Review
- Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.Npj mental health research · 2023Article
- When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures.JMIR mental health · 2023Review
- Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study.Brain sciences · 2022Article
- Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study.JMIR mental health · 2022Article
- Progress in Objective Detection of Depression and Online Monitoring of Patients Based on Physiological Complexity.Frontiers in psychiatry · 2022Article
- Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.Frontiers in physiology · 2022Article
- Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder.Frontiers in neuroinformatics · 2022Article
- Machine learning approaches for diagnosing depression using EEG: A review.Translational neuroscience · 2022Review
- Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.Biosensors · 2021Article
- The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection.Frontiers in physiology · 2021Review
- Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder.Journal of psychiatry & neuroscience : JPNArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.
objectiveThis review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models.
methodsTo detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology.
resultsWe reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism.
conclusionsThis review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.
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Registered trials
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