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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id	oai:doaj.org-article:eaf41bbfae364d2fbdb77b6c79ceb3ab
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spelling	oai:doaj.org-article:eaf41bbfae364d2fbdb77b6c79ceb3ab2021-11-08T03:08:21ZDepth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition10.3934/mbe.20212571551-0018https://doaj.org/article/eaf41bbfae364d2fbdb77b6c79ceb3ab2021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021257?viewType=HTMLhttps://doaj.org/toc/1551-0018According 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 of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.Ravichandra MadanuFarhan RahmanMaysam F. Abbod Shou-Zen FanJiann-Shing ShiehAIMS Pressarticledepth of anesthesiaconvolutional neural networkelectroencephalographyempirical mode decompositionensemble empirical mode decompositionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5047-5068 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	depth of anesthesia convolutional neural network electroencephalography empirical mode decomposition ensemble empirical mode decomposition Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	depth of anesthesia convolutional neural network electroencephalography empirical mode decomposition ensemble empirical mode decomposition Biotechnology TP248.13-248.65 Mathematics QA1-939 Ravichandra Madanu Farhan Rahman Maysam F. Abbod Shou-Zen Fan Jiann-Shing Shieh Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
description	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 of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.
format	article
author	Ravichandra Madanu Farhan Rahman Maysam F. Abbod Shou-Zen Fan Jiann-Shing Shieh
author_facet	Ravichandra Madanu Farhan Rahman Maysam F. Abbod Shou-Zen Fan Jiann-Shing Shieh
author_sort	Ravichandra Madanu
title	Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
title_short	Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
title_full	Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
title_fullStr	Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
title_full_unstemmed	Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition
title_sort	depth of anesthesia prediction via eeg signals using convolutional neural network and ensemble empirical mode decomposition
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/eaf41bbfae364d2fbdb77b6c79ceb3ab
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_version_	1718443052410863616

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

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