ArticleBMC medical informatics and decision making2015
Data mining EEG signals in depression for their diagnostic value.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Beyond the label "major depressive disorder"-detailed characterization of study population matters for EEG-biomarker research.Frontiers in neuroscience · 2025Pooled it
- Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.Journal of medical systems · 2019Pooled it
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- FSC-BiMamba: A Dual-Branch Bidirectional Mamba Network for Frontal Sparse-Channel EEG Depression Detection.Brain sciences · 2026Article
- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- MS-MDDNet: A Lightweight Deep Learning Framework for Interpretable EEG-Based Diagnosis of Major Depressive Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Associations of physical activity, sedentary time, sleep quality, and resting EEG with mild depressive symptoms in college students: a cross-sectional study.BMC public health · 2025Article
- A case-control study on QEEG as a marker of cognition in depression.Indian journal of psychiatry · 2025Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- AI and mental health: evaluating supervised machine learning models trained on diagnostic classifications.AI & society · 2025Article
- Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.Sensors (Basel, Switzerland) · 2024Article
- Analysis of BoDV-1 status, EEG resting-state alpha activity and pro-inflammatory cytokines in adults with and without major depressive disorder.Frontiers in psychology · 2024Article
- Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.Frontiers in neuroscience · 2024Article
- Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression.Diagnostics (Basel, Switzerland) · 2023Review
- Multi-channel EEG emotion recognition through residual graph attention neural network.Frontiers in neuroscience · 2023Article
- A novel EEG-based major depressive disorder detection framework with two-stage feature selection.BMC medical informatics and decision making · 2022Article
- Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers.Frontiers in psychiatry · 2022Article
- Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor.Frontiers in psychology · 2022Article
- Machine learning approaches for diagnosing depression using EEG: A review.Translational neuroscience · 2022Review
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models.
methodsThis paper uses a data mining methodology for classifying EEGs of 53 MDD patients and 43 HVs. This included: (a) pre-processing the data, including cleaning and normalization, applying Linear Discriminant Analysis (LDA) to map the features into a new feature space; and applying Genetic Algorithm (GA) to identify the most significant features; (b) building predictive models using the Decision Tree (DT) algorithm to discover rules and hidden patterns based on the reduced and mapped features; and (c) evaluating the models based on the accuracy and false positive values on the EEG data of MDD and HV participants. Two categories of experiments were performed. The first experiment analyzed each frequency band individually, while the second experiment analyzed the bands together.
resultsApplication of LDA and GA markedly reduced the total number of utilized features by ≥ 50 % and, with all frequency bands analyzed together, the model showed average classification accuracy (MDD vs. HV) of 80 %. The best results from model testing with additional test EEG recordings from 9 MDD patients and 35 HV individuals demonstrated an accuracy of 80 % and showed an average sensitivity of 70 %, a specificity of 76 %, and a positive (PPV) and negative predictive value (NPV) of 74 and 75 %, respectively.
conclusionsThese initial findings suggest that the proposed automated EEG analytical approach could be a useful adjunctive diagnostic approach in clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.