Study on Feature Selection Methods for Depression Detection Using Three-Electrode EEG Data-Reference-Cited by-同舟云学术

Study on Feature Selection Methods for Depression Detection Using Three-Electrode EEG Data

Published:2018-05-04 Issue:3 Volume:10 Page:558-565
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Cai Hanshu,Chen Yunfei,Han Jiashuo,Zhang Xiangzi,Hu Bin^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

http://link.springer.com/article/10.1007/s12539-018-0292-5/fulltext.html

Reference32 articles.

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2. Beck AT, Steer RA, Carbin MG (1988) Psychometric properties of the beck depression inventory: Twenty-five years of evaluation. Clin Psychol Rev 8(1):77–100

3. Association A. P. and Association A. P. Diagnostic and statistical manual of mentaldisorders (DSM). American Psychiatric Association, Washington. 1994: pp 143–147

4. Chattopadhyay S, Kaur P, Rabhi F et al (2012) Neural network approaches to grade adult depression. J Med Syst 36(5):2803–2815

5. Hosseinifard B, Moradi MH, Rostami R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Progr Biomed 109(3):339–345

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