The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method
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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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Higuchi's fractal dimension (HFD) and sample entropy (SampEn) are efficacious in segregating depression patients from healthy individuals with high accuracy across machine learning classifiers.
“These nonlinear metrics have proven efficacious in segregating depression patients from healthy individuals, with high accuracy figures reported across a range of machine learning classifiers.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [128]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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- Authors
- Čukić, M., Stokić, M., Simić, S., & Pokrajac, D.
- Affiliations
- Universidad Complutense de Madrid (ES)
- Institute of Mental Health (RS)
- Delaware State University (US)
- Corresponding author
- Čukić, Milena
- Stokić, Miodrag
- Simić, Slobodan
- Pokrajac, Dragoljub
- Venue
- Cognitive Neurodynamics
- Published
- 2020-03-25
- Volume
- 14
- Issue
- 4
- Pages
- 443-455
- ISSN
- 1871-4080 (ISSN-L)
- 1871-4099
- PMC
- PMC7334335
- OpenAlex
- W3105495164
- Cited by
- 130
- References
- 91
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 19 · 2023: 23 · 2022: 27 · 2021: 13 · 2020: 12 · 2019: 4
- FWCI
- 10.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cognitive Neurodynamics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja (178027, 32032)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- depression, EEG, Higuchi's fractal dimension, sample entropy, feature extraction, machine learning classification, depression diagnosis, biomarkers, EEG classification
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