ArticleEntropy (Basel, Switzerland)2022
Ensemble Approach for Detection of Depression Using EEG Features.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 58 citations in OpenAlex.
- A Comparative Analysis of EWT and EMD Techniques in the Diagnosis of Major Depressive Disorder Using EEG: Asymmetry Features and Explainability via SHAP.Diagnostics (Basel, Switzerland) · 2026Article
- An Effective and Interpretable EEG-Based Depression Recognition Method Using Hybrid Feature Selection.Bioengineering (Basel, Switzerland) · 2026Article
- Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.Micromachines · 2025Article
- Altered dynamic functional connectivity and reduced higher order information interaction in Parkinson's patients with hyposmia.NPJ systems biology and applications · 2025Article
- Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.Scientific reports · 2025Article
- Research on Depression Recognition Model and Its Temporal Characteristics Based on Multiscale Entropy of EEG Signals.Entropy (Basel, Switzerland) · 2025Article
- Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders.Frontiers in psychiatry · 2025Article
- Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques.Computational and structural biotechnology journal · 2024Article
- Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.Brain sciences · 2024Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- A novel approach for migraine detection using localized component filtering and electroencephalographic spectral asymmetry index.Current journal of neurology · 2024Article
- FC-TFS-CGRU: A Temporal-Frequency-Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture.Sensors (Basel, Switzerland) · 2024Article
- Clinical Sensitivity of Fractal Neurodynamics.Advances in neurobiology · 2024Article
- Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?Advances in neurobiology · 2024Article
- A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.PloS one · 2024Article
- Neurosteroids: mechanistic considerations and clinical prospects.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Multimodal Approach for Pilot Mental State Detection Based on EEG.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- Towards Context-Aware Facial Emotion Reaction Database for Dyadic Interaction Settings.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is a public health issue that severely affects one's well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel-Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.
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Registered trials
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.