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Epilepsy Detection Based on Riemann Potato in Noisy Environment
Epilepsy detection based on electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. The existing feature extraction method not only consumes a lot of time but also leads to epilepsy information loss because of nonideal denoising. Therefore, the paper proposes to use nois...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192297/ https://www.ncbi.nlm.nih.gov/pubmed/35706511 http://dx.doi.org/10.1155/2022/8311249 |
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author | Ru, Yandong Li, Jinbai Wei, Zheng |
author_facet | Ru, Yandong Li, Jinbai Wei, Zheng |
author_sort | Ru, Yandong |
collection | PubMed |
description | Epilepsy detection based on electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. The existing feature extraction method not only consumes a lot of time but also leads to epilepsy information loss because of nonideal denoising. Therefore, the paper proposes to use noisy EEG signals to detect epilepsy. The original EEG signal is divided into normal signal and abnormal signal by Riemann potato, and the epilepsy detection model is established based on the normal signal and abnormal signal, respectively. Finally, the 2 detection results are combined as a final result. The detection performance of 94.84%, 83.03% sensitivity, and 97.67% specificity is achieved. The experimental results show that the original noisy signal which is separated by the Riemann potato can have high epilepsy detection performance. |
format | Online Article Text |
id | pubmed-9192297 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91922972022-06-14 Epilepsy Detection Based on Riemann Potato in Noisy Environment Ru, Yandong Li, Jinbai Wei, Zheng Appl Bionics Biomech Research Article Epilepsy detection based on electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. The existing feature extraction method not only consumes a lot of time but also leads to epilepsy information loss because of nonideal denoising. Therefore, the paper proposes to use noisy EEG signals to detect epilepsy. The original EEG signal is divided into normal signal and abnormal signal by Riemann potato, and the epilepsy detection model is established based on the normal signal and abnormal signal, respectively. Finally, the 2 detection results are combined as a final result. The detection performance of 94.84%, 83.03% sensitivity, and 97.67% specificity is achieved. The experimental results show that the original noisy signal which is separated by the Riemann potato can have high epilepsy detection performance. Hindawi 2022-06-06 /pmc/articles/PMC9192297/ /pubmed/35706511 http://dx.doi.org/10.1155/2022/8311249 Text en Copyright © 2022 Yandong Ru 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 Ru, Yandong Li, Jinbai Wei, Zheng Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title | Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title_full | Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title_fullStr | Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title_full_unstemmed | Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title_short | Epilepsy Detection Based on Riemann Potato in Noisy Environment |
title_sort | epilepsy detection based on riemann potato in noisy environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192297/ https://www.ncbi.nlm.nih.gov/pubmed/35706511 http://dx.doi.org/10.1155/2022/8311249 |
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