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Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States
This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821856/ https://www.ncbi.nlm.nih.gov/pubmed/31666621 http://dx.doi.org/10.1038/s41598-019-52152-2 |
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author | Gagliano, Laura Bou Assi, Elie Nguyen, Dang K. Sawan, Mohamad |
author_facet | Gagliano, Laura Bou Assi, Elie Nguyen, Dang K. Sawan, Mohamad |
author_sort | Gagliano, Laura |
collection | PubMed |
description | This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction. |
format | Online Article Text |
id | pubmed-6821856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68218562019-11-05 Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States Gagliano, Laura Bou Assi, Elie Nguyen, Dang K. Sawan, Mohamad Sci Rep Article This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction. Nature Publishing Group UK 2019-10-30 /pmc/articles/PMC6821856/ /pubmed/31666621 http://dx.doi.org/10.1038/s41598-019-52152-2 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Gagliano, Laura Bou Assi, Elie Nguyen, Dang K. Sawan, Mohamad Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title | Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title_full | Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title_fullStr | Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title_full_unstemmed | Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title_short | Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States |
title_sort | bispectrum and recurrent neural networks: improved classification of interictal and preictal states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821856/ https://www.ncbi.nlm.nih.gov/pubmed/31666621 http://dx.doi.org/10.1038/s41598-019-52152-2 |
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