ArticleJournal of medical systems2020
Detection of Depression and Scaling of Severity Using Six Channel EEG Data.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.Journal of neuroengineering and rehabilitation · 2025Article
- Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the effects of aerobic and resistance exercise on mood-related symptoms and EEG activity.Frontiers in human neuroscience · 2025Article
- Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.Brain sciences · 2024Article
- User experiences of college students using mental health applications to improve self-care: Implications for improving engagement.Internet interventions · 2023Article
- Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.Health information science and systems · 2023Article
- Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder.BMC psychiatry · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression.Brain sciences · 2023Article
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior.Frontiers in psychiatry · 2023Article
- An electroencephalography connectome predictive model of major depressive disorder severity.Scientific reports · 2022Article
- Artificial intelligence and Psychiatry: An overview.Asian journal of psychiatry · 2022Review
- Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression.International journal of environmental research and public health · 2022Article
- Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm.Frontiers in public health · 2022Article
- Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.Frontiers in physiology · 2022Article
- Machine learning approaches for diagnosing depression using EEG: A review.Translational neuroscience · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).
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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.