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Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve t...

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Detalles Bibliográficos
Autores principales: Zhou, Dongmei, Li, Xuemei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324866/
https://www.ncbi.nlm.nih.gov/pubmed/32655355
http://dx.doi.org/10.3389/fnins.2020.00606
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author Zhou, Dongmei
Li, Xuemei
author_facet Zhou, Dongmei
Li, Xuemei
author_sort Zhou, Dongmei
collection PubMed
description Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.
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spelling pubmed-73248662020-07-10 Epilepsy EEG Signal Classification Algorithm Based on Improved RBF Zhou, Dongmei Li, Xuemei Front Neurosci Neuroscience Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate. Frontiers Media S.A. 2020-06-23 /pmc/articles/PMC7324866/ /pubmed/32655355 http://dx.doi.org/10.3389/fnins.2020.00606 Text en Copyright © 2020 Zhou and Li. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhou, Dongmei
Li, Xuemei
Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title_full Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title_fullStr Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title_full_unstemmed Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title_short Epilepsy EEG Signal Classification Algorithm Based on Improved RBF
title_sort epilepsy eeg signal classification algorithm based on improved rbf
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324866/
https://www.ncbi.nlm.nih.gov/pubmed/32655355
http://dx.doi.org/10.3389/fnins.2020.00606
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