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

Author:

Mahato Shalini,Paul Sanchita

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Reference29 articles.

1. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders. 5th edition. American Psychiatric Association Washington, DC.

2. World Health Organization, Depression and other common mental disorders Global Health estimates. Geneva: WHO Document Production Services, 2017.

3. Mahato, S., and Paul, S., Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): A review. In: Nath, V., Mandal, J. K. (Eds), Nanoelectronics, circuits and communication systems (NCCS 2017). Singapore: Springer, 2017, 323–336.

4. Hosseinifard, B., Moradi, M. H., Rostami, R., Classifying depression patients and Normal subjects using machine learning techniques. In: 19th Iranian conference on electrical engineering, Tehran, Iran, pp 339–345, (2011).

5. Cai, H., Han, J., Chen, Y., Sha, X., Wang, Z., et al. A pervasive approach to EEG-based depression detection. In: Hindawi, pp 1–13, (2018).

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