Different Approaches of Diagnosing Depressed and Non-depressed Patients
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-1645-8_21
Reference16 articles.
1. Wu C-T, Dillon DG, Hsu H-C, Huang S, Barrick E, Liu Y-H (2018) Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Appl Sci 8(8):1244
2. Ramalingam D, Sharma V, Zar P (2019) Study of depression analysis using machine learning techniques. Int J Innov Technol Explor Eng (IJITEE) 8(7C2)
3. Ay B, Yildirim O, Talo M et al (2019) Automated depression detection using deep representation and sequence learning with EEG signals. J Med Syst 43:205
4. Mohammadi Y, Hajian M, Moradi MH (2019) Discrimination of depression levels using machine learning methods on EEG signals. In: 2019 27th Iranian conference on electrical engineering (ICEE), pp 1765–1769
5. Zhu J, et al (2019) Toward depression recognition using EEG and eye tracking: an ensemble classification model CBEM. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 782–786
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