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Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction
The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features h...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195594/ https://www.ncbi.nlm.nih.gov/pubmed/30341370 http://dx.doi.org/10.1038/s41598-018-33969-9 |
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author | Bou Assi, Elie Gagliano, Laura Rihana, Sandy Nguyen, Dang K. Sawan, Mohamad |
author_facet | Bou Assi, Elie Gagliano, Laura Rihana, Sandy Nguyen, Dang K. Sawan, Mohamad |
author_sort | Bou Assi, Elie |
collection | PubMed |
description | The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%. |
format | Online Article Text |
id | pubmed-6195594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61955942018-10-24 Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction Bou Assi, Elie Gagliano, Laura Rihana, Sandy Nguyen, Dang K. Sawan, Mohamad Sci Rep Article The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%. Nature Publishing Group UK 2018-10-19 /pmc/articles/PMC6195594/ /pubmed/30341370 http://dx.doi.org/10.1038/s41598-018-33969-9 Text en © The Author(s) 2018 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 Bou Assi, Elie Gagliano, Laura Rihana, Sandy Nguyen, Dang K. Sawan, Mohamad Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title | Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title_full | Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title_fullStr | Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title_full_unstemmed | Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title_short | Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction |
title_sort | bispectrum features and multilayer perceptron classifier to enhance seizure prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195594/ https://www.ncbi.nlm.nih.gov/pubmed/30341370 http://dx.doi.org/10.1038/s41598-018-33969-9 |
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