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Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.

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...

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Published in:Journal of Medical Systems Vol. 44; no. 1; pp. 1 - 9
Main Authors: Mahato, Shalini, Paul, Sanchita
Format: equations & formulas research tables/charts Journal Article
Published: Springer Nature Jan2020
Online Access:View this record in EBSCOhost
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      dt: Jan2020
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-019-1486-z
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        atl: Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.
      aug:
        au:
          Mahato, Shalini
          Paul, Sanchita
        affil: Department of Computer Science and Engineering, Birla Institute of Technology, 835215, Ranchi, Mesra, India
      sug:
        subj:
          Depression Diagnosis
          Patient Classification Methods
          Electroencephalography
          Human
          Logistic Regression
          Paired T-Tests
          Decision Trees
          Descriptive Statistics
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          Female
          Adult
          Middle Age
          Malaysia
          Data Analysis Software
          Adult: 19-44 years
          Middle Aged: 45-64 years
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      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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