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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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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. |
format | Online Article Text |
id | pubmed-3984772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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