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Brain Network Regional Synchrony Analysis in Deafness

Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantati...

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Autores principales: Xu, Lei, Wang, Chang-Dong, Liang, Mao-Jin, Cai, Yue-Xin, Zheng, Yi-Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949203/
https://www.ncbi.nlm.nih.gov/pubmed/29854776
http://dx.doi.org/10.1155/2018/6547848
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author Xu, Lei
Wang, Chang-Dong
Liang, Mao-Jin
Cai, Yue-Xin
Zheng, Yi-Qing
author_facet Xu, Lei
Wang, Chang-Dong
Liang, Mao-Jin
Cai, Yue-Xin
Zheng, Yi-Qing
author_sort Xu, Lei
collection PubMed
description Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method.
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spelling pubmed-59492032018-05-31 Brain Network Regional Synchrony Analysis in Deafness Xu, Lei Wang, Chang-Dong Liang, Mao-Jin Cai, Yue-Xin Zheng, Yi-Qing Biomed Res Int Research Article Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method. Hindawi 2018-04-29 /pmc/articles/PMC5949203/ /pubmed/29854776 http://dx.doi.org/10.1155/2018/6547848 Text en Copyright © 2018 Lei Xu et al. https://creativecommons.org/licenses/by/4.0/ 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
Xu, Lei
Wang, Chang-Dong
Liang, Mao-Jin
Cai, Yue-Xin
Zheng, Yi-Qing
Brain Network Regional Synchrony Analysis in Deafness
title Brain Network Regional Synchrony Analysis in Deafness
title_full Brain Network Regional Synchrony Analysis in Deafness
title_fullStr Brain Network Regional Synchrony Analysis in Deafness
title_full_unstemmed Brain Network Regional Synchrony Analysis in Deafness
title_short Brain Network Regional Synchrony Analysis in Deafness
title_sort brain network regional synchrony analysis in deafness
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5949203/
https://www.ncbi.nlm.nih.gov/pubmed/29854776
http://dx.doi.org/10.1155/2018/6547848
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