Cited by 68EEG and Brain-Computer InterfacesEmotion and Mood RecognitionTreatment of Major Depression

Study on feature selection methods for depression detection using three-electrode EEG data

Cai, Hanshu, Chen, Yunfei, Han, Jiashuo, Zhang, Xiangzi, Hu, Bin·2018·Interdisciplinary Sciences: Computational Life Sciences

Abstract

The early diagnosis of depression is important to the treatment of this condition, whereas a timely diagnosis can reduce the incidence of mortality caused in patients with depression. In the present study, we collected the EEG signals of Fp2, Fpz and Fp1, compared with 128 channels EEG, a simpler test (3 channels EEG) can make diagnosis more accessible and widespread, researchers can perform more tests on more patients given the same amount of time and money. The difference between the depressed and the non-depressed patients was explored by the linear and non-linear characteristics of these EEG signals. A total of 152 patients with depression and 113 healthy subjects participated in the study. In the current report, the linear features were as follows: peak, variance, inclination, kurtosis and Hjorth parameter. The nonlinear features included C0 complexity, correlation dimension, Shannon entropy, Kolmogorov entropy and power spectrum entropy. With regard to the aforementioned characteristics, the present report utilized four feature selection algorithms, namely WrapperSubsetEval, CorrelationAttributeEval, GainRatioAttributeEval, and PrincipalComponents and five classification algorithms that included Support Vector Machine, K-Nearest Neighbor, Decision Tree, Logistics Regression and Random Forest. The experimental results indicated that the WrapperSubsetEval of the wrapper class exhibited higher performance compared with the other three feature selection algorithms on each classifier, whereas the highest classification accuracy was 76.4. It is suggested that this analysis may be a complementary tool to aid psychiatrists in the diagnosis of depressed patients.

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Crossref · 10.1007/s12539-018-0292-5

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  • The data preparation phase is critical for achieving optimal classification accuracy in EEG-based depression diagnosis.

    “Through rigorous data preprocessing and feature extraction—identifying noise types and harnessing both linear and nonlinear features—the critical role of the data preparation phase is emphasized for achieving optimal classification accuracy.”

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Authors
Cai, H., Chen, Y., Han, J., Zhang, X., & Hu, B.
Affiliations
  • Lanzhou University (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
Hu, Bin
Venue
Interdisciplinary Sciences: Computational Life Sciences
Published
2018-05-04
Volume
10
Issue
3
Pages
558-565
ISSN
  • 1867-1462 (ISSN-L)
  • 1913-2751
OpenAlex
W2799687171
Cited by
68
References
30
Citations by year
2026: 8 · 2025: 13 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 4 · 2020: 7 · 2019: 2
FWCI
1.94
Type
journal article
Language
en
Open access
closed
Locations
  • Interdisciplinary Sciences Computational Life Sciences (publishedVersion)
  • PubMed (publishedVersion)
  • Lanzhou University Institutional Repository (submittedVersion)
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 detection, feature selection, wrapper-based feature selection, nonlinear EEG features, Hjorth parameters, correlation dimension, Shannon entropy, Kolmogorov-Sinai entropy, power spectrum entropy, EEG complexity, early detection of depression, psychiatric diagnosis
MeSH terms
Algorithms, Depression, Electrodes, Electroencephalography, Humans, Entropy, Support Vector Machine

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