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

Bibliographic Details
Main Authors: Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh
Format: Article
Language:English
Published: AIMS Press 2021-06-01
Series:Mathematical Biosciences and Engineering
Subjects:
depth of anesthesia
convolutional neural network
electroencephalography
empirical mode decomposition
ensemble empirical mode decomposition
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2021257?viewType=HTML

Similar Items

Similar Items