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...

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Autores principales:	Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	depth of anesthesia convolutional neural network electroencephalography empirical mode decomposition ensemble empirical mode decomposition Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/eaf41bbfae364d2fbdb77b6c79ceb3ab
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Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition

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