Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition

Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth o...

Full description

Saved in:
Bibliographic Details
Main Authors: Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh
Format: article
Language:EN
Published: AIMS Press 2021
Subjects:
depth of anesthesia
convolutional neural network
electroencephalography
empirical mode decomposition
ensemble empirical mode decomposition
Biotechnology
TP248.13-248.65
Mathematics
QA1-939
Online Access:https://doaj.org/article/eaf41bbfae364d2fbdb77b6c79ceb3ab
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doaj.org/article/eaf41bbfae364d2fbdb77b6c79ceb3ab

Similar Items