The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry
Abstract
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.
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“Notably, findings suggest that asymmetries in the alpha2 and theta bands, particularly when analyzed with a Support Vector Machine (SVM), lead to higher diagnostic precision, with an accuracy rate of 88.33%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [129]
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Metadata
- Authors
- Mahato, S., & Paul, S.
- Affiliations
- Birla Institute of Technology, Mesra (IN)
- Corresponding author
- Mahato, Shalini
- Venue
- Journal of Medical Systems
- Published
- 2019-12-13
- Volume
- 44
- Issue
- 1
- Pages
- 28-28
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2996393987
- Cited by
- 148
- References
- 28
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 24 · 2023: 30 · 2022: 25 · 2021: 17 · 2020: 8
- FWCI
- 7.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Health Sciences › Medicine › Pharmacology (medical) › Treatment of Major Depression
- Keywords
- major depressive disorder, depression diagnosis, EEG features, alpha power, theta power, feature selection, support vector machine, brain asymmetry
- MeSH terms
- Adult, Bayes Theorem, Depressive Disorder, Major, Major Depressive Disorder, Electroencephalography, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Logistic Models, Support Vector Machine
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