A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture-Reference-Cited by-同舟云学术

A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture

Published:2023-10-27 Issue: Volume: Page:
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Raja A. Ravi^ORCID,Polasi Phani Kumar^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-023-02792-9.pdf

Reference40 articles.

1. Korkalainen, H.: Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea. IEEE J. Biomed. Health Inform. 24(7), 2073–2081 (2020)

2. Goshtasbi, N., Boostani, R., Sanei, S.: SleepFCN: a fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2088–2096 (2022)

3. Qu, W.: A residual based attention model for EEG based sleep staging. IEEE J. Biomed. Health Inform. 24(10), 2833–2843 (2020)

4. Banluesombatkul, N.: MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. IEEE J. Biomed. Health Inform. 25(6), 1949–1963 (2021)

5. Chambon, S., Galtier, M.N., Arnal, P.J., Wainrib, G., Gramfort, A.: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Rehabil. Eng. 26(4), 758–769 (2018)

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