A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features

Author:

Movahed Reza Akbari,Jahromi Gila Pirzad,Shahyad Shima,Meftahi Gholam Hossein

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Biomedical Engineering,Biophysics,Radiological and Ultrasound Technology,Biotechnology

Reference63 articles.

1. Seligman M (1975) Helplessness: on depression, development, and death

2. Marcus M, Yasamy MT, van Ommeren MV, Chisholm D, Saxena S (2012) Depression: a global public health concern

3. World Health Organization (2001) The World Health Report 2001: mental health: new understanding, new hope. World Health Organization, Geneva

4. Castillo R, Carlat D, Millon T, Millon C, Meagher S, Grossman S, Association AP et al (2007) Diagnostic and statistical manual of mental disorders. American Psychiatric Association Press, Washington, DC

5. Folstein MF, Robins LN, Helzer JE (1983) The mini-mental state examination. Arch Gen Psychiatry 40(7):812–812

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1. Effect of Artifact Removal in Machine Learning Based Depression Screening using EEG;Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing;2023-10-20

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3. Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder;Frontiers in Psychiatry;2023-04-26

4. A Multi-stream Deep Learning Model for EEG-based Depression Identification;2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2022-12-06

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