An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals-Reference-Cited by-同舟云学术

An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals

Published:2023 Issue: Volume: Page:1179-1188
ISSN:2367-3370
Container-title:Hybrid Intelligent Systems
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Author:

Aswathy S. U.,Vincent Bibin,Jacob Pramod Mathew,Aniyan Nisha,Daniel Doney,Thomas Jyothi

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_108

Reference19 articles.

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2. Thoduparambil, P.P., Dominic, A., Varghese, S.: M:EEG-based deep learning model for the automatic detection of clinical depression. Phys. Eng. Sci. Med. 43(4), 1349–1360 (2020)

3. Dhas, G.G.D., Kumar, S.S.: A survey on detection of brain tumor from MRI brain images. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), July, pp. 871–877. IEEE (2014)

4. Cai, H., Han, J., Chen, Y., Sha, X., Wang, Z., Hu, B., Gutknecht, J.: A pervasive approach to EEG-based depression detection. Complexity (2018)

5. Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., Zhu, J.: EEG-based mild depression recognition using convolutional neural network. Med. Biol. Eng. Comput. 57(6), 1341–1352 (2019)

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