Cargando…
Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network
The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a de...
Autores principales: | Shi, Meng, Huang, Ziyu, Xiao, Guowen, Xu, Bowen, Ren, Quansheng, Zhao, Hong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865536/ https://www.ncbi.nlm.nih.gov/pubmed/36679805 http://dx.doi.org/10.3390/s23021008 |
Ejemplares similares
-
DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion
por: Wang, Hongfeng, et al.
Publicado: (2022) -
Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network
por: Zhang, Shengli, et al.
Publicado: (2021) -
Image Motion Deblurring Based on Deep Residual Shrinkage and Generative Adversarial Networks
por: Jiang, Wenbo, et al.
Publicado: (2022) -
Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification
por: Salimy, Alireza, et al.
Publicado: (2022) -
Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference
por: Wu, Xiaojun, et al.
Publicado: (2023)