Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning-Reference-Cited by-同舟云学术

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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Published:2021-06 Issue:6 Volume:68 Page:1787-1798
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Phan Huy^ORCID,Chen Oliver Y.^ORCID,Koch Philipp,Lu Zongqing^ORCID,McLoughlin Ian^ORCID,Mertins Alfred,De Vos Maarten

Funder

Vlaamse regering

Leuven.AI - KU Leuven institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/9439268/09181436.pdf?arnumber=9181436

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1. Exploring the statistical and computational analysis of sleep stages across different age groups;International Journal of Information Technology;2024-09-04

2. PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging;IEEE Journal of Biomedical and Health Informatics;2024-09

3. A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability;IEEE Sensors Journal;2024-09-01

4. Automated remote sleep monitoring needs uncertainty quantification;Journal of Sleep Research;2024-08-07

5. Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy;Journal of Clinical Sleep Medicine;2024-06

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