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Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN
BACKGROUND: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent ye...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323263/ https://www.ncbi.nlm.nih.gov/pubmed/34330248 http://dx.doi.org/10.1186/s12911-021-01438-5 |
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author | Ma, Mengnan Cheng, Yinlin Wei, Xiaoyan Chen, Ziyi Zhou, Yi |
author_facet | Ma, Mengnan Cheng, Yinlin Wei, Xiaoyan Chen, Ziyi Zhou, Yi |
author_sort | Ma, Mengnan |
collection | PubMed |
description | BACKGROUND: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed. METHODS: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results. RESULTS: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance. CONCLUSION: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG. |
format | Online Article Text |
id | pubmed-8323263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83232632021-07-30 Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN Ma, Mengnan Cheng, Yinlin Wei, Xiaoyan Chen, Ziyi Zhou, Yi BMC Med Inform Decis Mak Research BACKGROUND: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed. METHODS: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results. RESULTS: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance. CONCLUSION: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG. BioMed Central 2021-07-30 /pmc/articles/PMC8323263/ /pubmed/34330248 http://dx.doi.org/10.1186/s12911-021-01438-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ma, Mengnan Cheng, Yinlin Wei, Xiaoyan Chen, Ziyi Zhou, Yi Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title | Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title_full | Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title_fullStr | Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title_full_unstemmed | Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title_short | Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN |
title_sort | research on epileptic eeg recognition based on improved residual networks of 1-d cnn and indrnn |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323263/ https://www.ncbi.nlm.nih.gov/pubmed/34330248 http://dx.doi.org/10.1186/s12911-021-01438-5 |
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