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

• Published in: Interdisciplinary sciences : computational life sciences Vol. 10; no. 3; pp. 558 - 565
• Main Authors: Cai, Hanshu, Chen, Yunfei, Han, Jiashuo, Zhang, Xiangzi, Hu, Bin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2018; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Channels; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Decision trees; Diagnosis; EEG; Electroencephalography; Entropy; Entropy (Information theory); Health Sciences; Inclination; Kurtosis; Life Sciences; Logistics; Mathematical and Computational Physics; Medicine; Mental depression; Original Research Article; Patients; Regression analysis; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Feature selection; EEG; Depressive diagnosis

The early diagnosis of depression is important to the treatment of this condition, whereas a timely diagnosis can reduce the incidence of mortality caused in patients with depression. In the present study, we collected the EEG signals of Fp2, Fpz and Fp1, compared with 128 channels EEG, a simpler te...