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

  • Systems-Level Quality Improvement
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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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Acknowledgments

The authors would like to thank Mumtaz et al. [9] for the dataset contribution.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Mesra, 835215, India

    Shalini Mahato & Sanchita Paul

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  1. Shalini Mahato
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  2. Sanchita Paul
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Correspondence to Shalini Mahato.

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The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

The dataset used in this study is described in the paper by Mumtaz et al. and is publically available dataset (https://figshare.com/articles/EEG-based_Diagnosis_and_Treatment_Outcome_Prediction_for_Major_Depressive_Disorder/338516) [9]. According to the contributors of the dataset [9], all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

According to the contributors of the dataset [9], informed consent was obtained from all individual participants included in the study.

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Mahato, S., Paul, S. Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry. J Med Syst 44, 28 (2020). https://doi.org/10.1007/s10916-019-1486-z

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