Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness
Author:
Affiliation:
1. School of Software, South China Normal University, Foshan, Guangdong Province, China
2. Guangdong Provincial Work Injury Rehabilitation Hospital, Guangzhou, Guangdong Province, China
3. Pazhou Lab, Guangzhou, Guangdong Province, China
Funder
STI 2030-Major Projects
National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7274989/10633860/10480290.pdf?arnumber=10480290
Reference64 articles.
1. The JFK coma recovery scale—revised
2. The Vegetative State: Prevalence, Misdiagnosis, and Treatment Limitations
3. Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment
4. Hybrid asynchronous brain–computer interface for yes/no communication in patients with disorders of consciousness
5. Detecting awareness in patients with disorders of consciousness using a hybrid brain–computer interface
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1. Prognosis of patients with prolonged disorders of consciousness after brain injury: a longitudinal cohort study;Frontiers in Public Health;2024-07-24
2. Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI;International Journal of Electrical and Electronics Research;2024-06-25
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