Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks

Scalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It is widely used in several applications including cognitive tasks, sleep stage detection, and seizure prediction. When recorded over several hours, this signal is usually corrupted by noisy disturbances such as experimen...

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Autores principales:	Fabio Lopes, Adriana Leal, Julio Medeiros, Mauro F. Pinto, Antonio Dourado, Matthias Dumpelmann, Cesar Teixeira
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Artifact removal automatic reconstruction deep convolutional neural networks electroencephalogram preprocessing Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c66a76a4430f41249d8757fdbf5d62da
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id	oai:doaj.org-article:c66a76a4430f41249d8757fdbf5d62da
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spelling	oai:doaj.org-article:c66a76a4430f41249d8757fdbf5d62da2021-11-18T00:01:25ZAutomatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks2169-353610.1109/ACCESS.2021.3125728https://doaj.org/article/c66a76a4430f41249d8757fdbf5d62da2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605576/https://doaj.org/toc/2169-3536Scalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It is widely used in several applications including cognitive tasks, sleep stage detection, and seizure prediction. When recorded over several hours, this signal is usually corrupted by noisy disturbances such as experimental errors, environmental interferences, and physiological artifacts. These may generate confounding factors and, therefore, lead to false results. Models able to minimise EEG artifacts are then necessary for improving further analysis and application. In this work, we developed an EEG artifact removal model based on deep convolutional neural networks. The proposed approach was applied on long-term EEGs, acquired from epileptic patients, available in the EPILEPSIAE database. The main goal of our work is to develop a model able to automatically and quickly remove artifacts from EEGs. To develop it, we used EEG segments, manually preprocessed by experts and named target EEG segments. Our approach was evaluated comparing denoised segments with the target segments. Furthermore, we compared our approach with other artifact removal models. Results show that the developed model was able to attenuate the influence of artifacts, present in long-term EEG signals, in a similar way to that performed by experts. Additionally, results evidence that our approach performs better than other artifact removal models, combining a minor reconstruction error with a fast processing. Being a fully automatic and fast model that does not require reference artifact templates, turns it suitable, for example, for continuous preprocessing of long-term electroencephalogram for sleep staging or seizure prediction.Fabio LopesAdriana LealJulio MedeirosMauro F. PintoAntonio DouradoMatthias DumpelmannCesar TeixeiraIEEEarticleArtifact removalautomatic reconstructiondeep convolutional neural networkselectroencephalogrampreprocessingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149955-149970 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artifact removal automatic reconstruction deep convolutional neural networks electroencephalogram preprocessing Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artifact removal automatic reconstruction deep convolutional neural networks electroencephalogram preprocessing Electrical engineering. Electronics. Nuclear engineering TK1-9971 Fabio Lopes Adriana Leal Julio Medeiros Mauro F. Pinto Antonio Dourado Matthias Dumpelmann Cesar Teixeira Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
description	Scalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It is widely used in several applications including cognitive tasks, sleep stage detection, and seizure prediction. When recorded over several hours, this signal is usually corrupted by noisy disturbances such as experimental errors, environmental interferences, and physiological artifacts. These may generate confounding factors and, therefore, lead to false results. Models able to minimise EEG artifacts are then necessary for improving further analysis and application. In this work, we developed an EEG artifact removal model based on deep convolutional neural networks. The proposed approach was applied on long-term EEGs, acquired from epileptic patients, available in the EPILEPSIAE database. The main goal of our work is to develop a model able to automatically and quickly remove artifacts from EEGs. To develop it, we used EEG segments, manually preprocessed by experts and named target EEG segments. Our approach was evaluated comparing denoised segments with the target segments. Furthermore, we compared our approach with other artifact removal models. Results show that the developed model was able to attenuate the influence of artifacts, present in long-term EEG signals, in a similar way to that performed by experts. Additionally, results evidence that our approach performs better than other artifact removal models, combining a minor reconstruction error with a fast processing. Being a fully automatic and fast model that does not require reference artifact templates, turns it suitable, for example, for continuous preprocessing of long-term electroencephalogram for sleep staging or seizure prediction.
format	article
author	Fabio Lopes Adriana Leal Julio Medeiros Mauro F. Pinto Antonio Dourado Matthias Dumpelmann Cesar Teixeira
author_facet	Fabio Lopes Adriana Leal Julio Medeiros Mauro F. Pinto Antonio Dourado Matthias Dumpelmann Cesar Teixeira
author_sort	Fabio Lopes
title	Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
title_short	Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
title_full	Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
title_fullStr	Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
title_full_unstemmed	Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks
title_sort	automatic electroencephalogram artifact removal using deep convolutional neural networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c66a76a4430f41249d8757fdbf5d62da
work_keys_str_mv	AT fabiolopes automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT adrianaleal automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT juliomedeiros automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT maurofpinto automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT antoniodourado automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT matthiasdumpelmann automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks AT cesarteixeira automaticelectroencephalogramartifactremovalusingdeepconvolutionalneuralnetworks
_version_	1718425250641739776

Automatic Electroencephalogram Artifact Removal Using Deep Convolutional Neural Networks

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