Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks

Missing values are very prevalent in real world; they are caused by various reasons such as user mistakes or device failures. They often cause critical problems especially in medical and healthcare application since they can lead to incorrect diagnosis or even cause system failure. Many of recent im...

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Autores principales:	Woonghee Lee, Jaeyoung Lee, Younghoon Kim
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Missing data imputation electroencephalogram (EEG) generated adversarial network (GAN) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/91c88ccd9a2f47b78b39c270b2c45d4b
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id	oai:doaj.org-article:91c88ccd9a2f47b78b39c270b2c45d4b
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spelling	oai:doaj.org-article:91c88ccd9a2f47b78b39c270b2c45d4b2021-11-17T00:00:59ZContextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks2169-353610.1109/ACCESS.2021.3126345https://doaj.org/article/91c88ccd9a2f47b78b39c270b2c45d4b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606711/https://doaj.org/toc/2169-3536Missing values are very prevalent in real world; they are caused by various reasons such as user mistakes or device failures. They often cause critical problems especially in medical and healthcare application since they can lead to incorrect diagnosis or even cause system failure. Many of recent imputation techniques have adopted machine learning-based generative methods such as generative adversarial networks (GANs) to deal with missing values in medical data. They are, however, incapable of reproducing realistic time-series signals preserving important latent features such as sleep stages that are important context in many medical applications using electroencephalogram (EEG). In this study, we propose a novel GAN-based technique generating realistic EEG signal sequences which are not only shown natural but also correctly classified with sleep stages by implanting the latent features in the synthetic sequence. By experiments, we confirm that our model generates not only more realistic EEG signals than a recent GAN-based model but also preserve auxiliary information such as sleep stages. Furthermore, we demonstrate that existing machine learning methods based on EEG data still work well without sacrificing performance using the imputed data by using our method.Woonghee LeeJaeyoung LeeYounghoon KimIEEEarticleMissing data imputationelectroencephalogram (EEG)generated adversarial network (GAN)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151753-151765 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Missing data imputation electroencephalogram (EEG) generated adversarial network (GAN) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Missing data imputation electroencephalogram (EEG) generated adversarial network (GAN) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Woonghee Lee Jaeyoung Lee Younghoon Kim Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
description	Missing values are very prevalent in real world; they are caused by various reasons such as user mistakes or device failures. They often cause critical problems especially in medical and healthcare application since they can lead to incorrect diagnosis or even cause system failure. Many of recent imputation techniques have adopted machine learning-based generative methods such as generative adversarial networks (GANs) to deal with missing values in medical data. They are, however, incapable of reproducing realistic time-series signals preserving important latent features such as sleep stages that are important context in many medical applications using electroencephalogram (EEG). In this study, we propose a novel GAN-based technique generating realistic EEG signal sequences which are not only shown natural but also correctly classified with sleep stages by implanting the latent features in the synthetic sequence. By experiments, we confirm that our model generates not only more realistic EEG signals than a recent GAN-based model but also preserve auxiliary information such as sleep stages. Furthermore, we demonstrate that existing machine learning methods based on EEG data still work well without sacrificing performance using the imputed data by using our method.
format	article
author	Woonghee Lee Jaeyoung Lee Younghoon Kim
author_facet	Woonghee Lee Jaeyoung Lee Younghoon Kim
author_sort	Woonghee Lee
title	Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
title_short	Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
title_full	Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
title_fullStr	Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
title_full_unstemmed	Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
title_sort	contextual imputation with missing sequence of eeg signals using generative adversarial networks
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/91c88ccd9a2f47b78b39c270b2c45d4b
work_keys_str_mv	AT woongheelee contextualimputationwithmissingsequenceofeegsignalsusinggenerativeadversarialnetworks AT jaeyounglee contextualimputationwithmissingsequenceofeegsignalsusinggenerativeadversarialnetworks AT younghoonkim contextualimputationwithmissingsequenceofeegsignalsusinggenerativeadversarialnetworks
_version_	1718426063088910336

Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks

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