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Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had t...

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Detalles Bibliográficos
Autores principales: Yang, Zhixian, Wang, Yinghua, Ouyang, Gaoxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984772/
https://www.ncbi.nlm.nih.gov/pubmed/24790547
http://dx.doi.org/10.1155/2014/140863
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author Yang, Zhixian
Wang, Yinghua
Ouyang, Gaoxiang
author_facet Yang, Zhixian
Wang, Yinghua
Ouyang, Gaoxiang
author_sort Yang, Zhixian
collection PubMed
description Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
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spelling pubmed-39847722014-04-30 Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls Yang, Zhixian Wang, Yinghua Ouyang, Gaoxiang ScientificWorldJournal Research Article Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. Hindawi Publishing Corporation 2014-03-25 /pmc/articles/PMC3984772/ /pubmed/24790547 http://dx.doi.org/10.1155/2014/140863 Text en Copyright © 2014 Zhixian Yang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yang, Zhixian
Wang, Yinghua
Ouyang, Gaoxiang
Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title_full Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title_fullStr Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title_full_unstemmed Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title_short Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
title_sort adaptive neuro-fuzzy inference system for classification of background eeg signals from eses patients and controls
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984772/
https://www.ncbi.nlm.nih.gov/pubmed/24790547
http://dx.doi.org/10.1155/2014/140863
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