DenSleepNet: DenseNet based model for sleep staging with two-frequency feature fusion and coordinate attention-Reference-Cited by-同舟云学术

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DenSleepNet: DenseNet based model for sleep staging with two-frequency feature fusion and coordinate attention

Published:2023-07-11 Issue:4 Volume:13 Page:751-761
ISSN:2093-9868
Container-title:Biomedical Engineering Letters
language:en
Short-container-title:Biomed. Eng. Lett.

Author:

Liu Zhi,Qin Meiqiao,Lu Yunhua,Luo Sixin,Zhang Qinhan

Funder

The Scientific Research Foundation of Chongqing University of Technology

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13534-023-00301-y.pdf

Reference30 articles.

1. Alimoradi Z, Gozal D, Tsang HW, Lin CY, Broström A, Ohayon MM, Pakpour AH. Gender-specific estimates of sleep problems during the covid-19 pandemic: systematic review and meta-analysis. J Sleep Res. 2022;31(1): e13432.

2. Eldele E, Chen Z, Liu C, Wu M, Kwoh CK, Li X, Guan C. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehab Eng. 2021;29:809–18.

3. Eldele E, Ragab M, Chen Z, Wu M, Kwoh CK, Li X, Guan C. Adast: attentive cross-domain EEG-based sleep staging framework with iterative self-training. IEEE Trans Emerging Top Comput Intell. 2022.

4. Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR. A review of automated sleep stage scoring based on physiological signals for the new millennia. Comput Methods Program Biomed. 2019;176:81–91.

5. Fiorillo L, Favaro P, Faraci FD. Deepsleepnet-lite: a simplified automatic sleep stage scoring model with uncertainty estimates. IEEE Trans Neural Syst Rehab Eng. 2021;29:2076–85.

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1. A review of automated sleep stage based on EEG signals;Biocybernetics and Biomedical Engineering;2024-07

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