A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection with Limited EEG Channels-Reference-Cited by-同舟云学术

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A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection with Limited EEG Channels

Published:2021 Issue: Volume: Page:1-1
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Ansari Amir Hossein,Pillay Kirubin,Dereymaeker Anneleen,Jansen Katrien,Van Huffel Sabine,Naulaers Gunnar,De Vos Maarten

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/6221020/6363502/09502541.pdf?arnumber=9502541

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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

2. Free access via computational cloud to deep learning-based EEG assessment in neonatal hypoxic-ischemic encephalopathy: revolutionary opportunities to overcome health disparities;Pediatric Research;2024-08-06

3. Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome;Clinical Neurophysiology;2024-07

4. Effectiveness of Adaptive Attention-Based Network for Situation Awareness Recognition;IEEE Sensors Journal;2024-06-15

5. Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia;Clinical Neurophysiology;2024-06

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