Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning-Reference-Cited by-同舟云学术

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Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning

Published:2021 Issue: Volume: Page:1-1
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Zheng Wei-Long,Amorim Edilberto,Jing Jin,Wu Ona,Ghassemi Mohammad,Lee Jong Woo,Sivaraju Adithya,Pang Trudy,Herman Susan T.,Gaspard Nicolas,Ruijter Barry J.,Tjepkema-Cloostermans Marleen C.,Hofmeijer Jeannette,Van Putten Michel,Westover Brandon

Funder

NIH-NINDS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/4359967/09664244.pdf?arnumber=9664244

Cited by 13 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. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest;Resuscitation;2024-08

2. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients;Seminars in Neurology;2024-04-03

3. Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review;Resuscitation;2024-01

4. A Review on Heart Diseases Using Machine Learning and Deep Learning Techniques;Lecture Notes in Networks and Systems;2024

5. Prognosis of comatose patients with reduced EEG montage by combining quantitative EEG features in various domains;Frontiers in Neuroscience;2023-12-08

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