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Choosing strategies to deal with artifactual eeg data in children with cognitive impairment

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10.3390/e23081030
 
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hdl:2117/351949

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Tost Abadías, AnaMés informació
Migliorelli Falcone, Carolina MercedesMés informació
Bachiller Matarranz, AlejandroMés informacióMés informacióMés informació
Medina Rivera, Inés
Romero Lafuente, SergioMés informacióMés informacióMés informació
García Cazorla, Àngels
Mañanas Villanueva, Miguel ÁngelMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-08-11
Rights accessOpen Access
Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain
ProjectANALISIS MULTIMODAL PARA LA EVALUACION Y REHABILITACION DE TRASTORNOS NEUROLOGICOS DISCAPACITANTES (AEI-DPI2017-83989-R)
Abstract
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.
CitationTost, A. [et al.]. Choosing strategies to deal with artifactual eeg data in children with cognitive impairment. "Entropy", 11 Agost 2021, vol. 23, núm. 8, p. 1030:1-1030:18. 
URIhttp://hdl.handle.net/2117/351949
DOI10.3390/e23081030
ISSN10994300
Publisher versionhttps://www.mdpi.com/1099-4300/23/8/1030
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  • Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Articles de revista [1.323]
  • BIOART - BIOsignal Analysis for Rehabilitation and Therapy - Articles de revista [76]
  • Doctorat en Enginyeria Biomèdica - Articles de revista [90]
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