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Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection st...

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Autores principales: Tost, Ana, Migliorelli, Carolina, Bachiller, Alejandro, Medina-Rivera, Inés, Romero, Sergio, García-Cazorla, Ángeles, Mañanas, Miguel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392530/
https://www.ncbi.nlm.nih.gov/pubmed/34441170
http://dx.doi.org/10.3390/e23081030
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author Tost, Ana
Migliorelli, Carolina
Bachiller, Alejandro
Medina-Rivera, Inés
Romero, Sergio
García-Cazorla, Ángeles
Mañanas, Miguel A.
author_facet Tost, Ana
Migliorelli, Carolina
Bachiller, Alejandro
Medina-Rivera, Inés
Romero, Sergio
García-Cazorla, Ángeles
Mañanas, Miguel A.
author_sort Tost, Ana
collection PubMed
description Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.
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spelling pubmed-83925302021-08-28 Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment Tost, Ana Migliorelli, Carolina Bachiller, Alejandro Medina-Rivera, Inés Romero, Sergio García-Cazorla, Ángeles Mañanas, Miguel A. Entropy (Basel) Article Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed. MDPI 2021-08-11 /pmc/articles/PMC8392530/ /pubmed/34441170 http://dx.doi.org/10.3390/e23081030 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tost, Ana
Migliorelli, Carolina
Bachiller, Alejandro
Medina-Rivera, Inés
Romero, Sergio
García-Cazorla, Ángeles
Mañanas, Miguel A.
Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title_full Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title_fullStr Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title_full_unstemmed Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title_short Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment
title_sort choosing strategies to deal with artifactual eeg data in children with cognitive impairment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392530/
https://www.ncbi.nlm.nih.gov/pubmed/34441170
http://dx.doi.org/10.3390/e23081030
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