Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.
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| Title: | Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry. |
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| Authors: | Mahato, Shalini1 swarup.shalini@gmail.com, Paul, Sanchita1 |
| Source: | Journal of Medical Systems. Jan2020, Vol. 44 Issue 1, p1-8. 8p. 2 Diagrams, 8 Charts, 1 Graph. |
| Subjects: | Diagnosis of mental depression, Classification, Decision trees, Electroencephalography, Patients, T-test (Statistics), Logistic regression analysis, Data analysis software, Descriptive statistics |
| Geographic Terms: | Malaysia |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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