SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification-Reference-Cited by-同舟云学术

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SleepExpertNet: high-performance and class-balanced deep learning approach inspired from the expert neurologists for sleep stage classification

Published:2022-10-20 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Lee Choel-Hui,Kim Hyun-Ji,Kim Young-Tak,Kim Hakseung,Kim Jung-Bin,Kim Dong-Joo

Funder

Korea Medical Device Development Fund

National Research Foundation of Korea

Institute for Information and Communications Technology Promotion

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-022-04443-2.pdf

Reference58 articles.

1. Ali A, Shamsuddin SM, Ralescu AL (2013) Classification with class imbalance problem. Int J Adv Soft Comput Appl 5:1

2. Alickovic E, Subasi A (2018) Ensemble SVM method for automatic sleep stage classification. IEEE Trans Instrum Meas 67:1258–1265. https://doi.org/10.1109/TIM.2018.2799059

3. Altevogt BM, Colten HR (2006) Sleep disorders and sleep deprivation: an unmet public health problem. https://doi.org/10.17226/11617

4. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828. https://doi.org/10.1109/tpami.2013.50

5. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

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2. Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review;Physiologia;2023-12-20

3. Current status and prospects of automatic sleep stages scoring: Review;Biomedical Engineering Letters;2023-07-10

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