ArticleCognitive neurodynamics2020
The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.
Article in Cognitive neurodynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A time-scale representation of EEG Higuchi fractal dimension.Scientific reports · 2026Article
- EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis.Brain topography · 2026Article
- Depression Detection from Three-Channel Resting-State EEG Using a Hybrid Conv1D and Spectral-Statistical Fusion Model.Sensors (Basel, Switzerland) · 2026Article
- Research on Depression Recognition Model and Its Temporal Characteristics Based on Multiscale Entropy of EEG Signals.Entropy (Basel, Switzerland) · 2025Article
- Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- A novel fast detection algorithm for depression based on 3-channel EEG signals.Frontiers in neuroscience · 2025Article
- Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.Sensors (Basel, Switzerland) · 2024Review
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- Classification of bruxism based on time-frequency and nonlinear features of single channel EEG.BMC oral health · 2024Article
- Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?Advances in neurobiology · 2024Article
- Clinical Sensitivity of Fractal Neurodynamics.Advances in neurobiology · 2024Article
- Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG).Advances in neurobiology · 2024Article
- A free association semantic task for fNIRS-based perinatal depression assessment.Frontiers in neurology · 2024Article
- Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.Health information science and systems · 2023Article
- Correlation between electroencephalographic markers in the healthy brain.Scientific reports · 2023Article
- When Heart Beats Differently in Depression: Review of Nonlinear Heart Rate Variability Measures.JMIR mental health · 2023Review
- Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.Computational intelligence and neuroscience · 2023Article
- Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.Frontiers in systems neuroscience · 2023Article
- Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning on Early Diagnosis of Depression.Psychiatry investigation · 2022Article
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4 authors.
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Abstract
Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.
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