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Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis
BACKGROUND: Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strateg...
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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/PMC8464089/ https://www.ncbi.nlm.nih.gov/pubmed/34560859 http://dx.doi.org/10.1186/s12911-021-01631-6 |
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author | Torabi, Ali Daliri, Mohammad Reza |
author_facet | Torabi, Ali Daliri, Mohammad Reza |
author_sort | Torabi, Ali |
collection | PubMed |
description | BACKGROUND: Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. METHODS: In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). RESULTS: According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. CONCLUSION: The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification. |
format | Online Article Text |
id | pubmed-8464089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84640892021-09-27 Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis Torabi, Ali Daliri, Mohammad Reza BMC Med Inform Decis Mak Research BACKGROUND: Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. METHODS: In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). RESULTS: According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. CONCLUSION: The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification. BioMed Central 2021-09-24 /pmc/articles/PMC8464089/ /pubmed/34560859 http://dx.doi.org/10.1186/s12911-021-01631-6 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 Torabi, Ali Daliri, Mohammad Reza Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title | Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title_full | Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title_fullStr | Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title_full_unstemmed | Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title_short | Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
title_sort | applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464089/ https://www.ncbi.nlm.nih.gov/pubmed/34560859 http://dx.doi.org/10.1186/s12911-021-01631-6 |
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