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EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy)
This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the “sharp wave” pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036140/ https://www.ncbi.nlm.nih.gov/pubmed/32095119 http://dx.doi.org/10.1155/2020/8407872 |
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author | Yakovleva, Tatiana V. Kutepov, Ilya E. Karas, Antonina Yu Yakovlev, Nikolai M. Dobriyan, Vitalii V. Papkova, Irina V. Zhigalov, Maxim V. Saltykova, Olga A. Krysko, Anton V. Yaroshenko, Tatiana Yu Erofeev, Nikolai P. Krysko, Vadim A. |
author_facet | Yakovleva, Tatiana V. Kutepov, Ilya E. Karas, Antonina Yu Yakovlev, Nikolai M. Dobriyan, Vitalii V. Papkova, Irina V. Zhigalov, Maxim V. Saltykova, Olga A. Krysko, Anton V. Yaroshenko, Tatiana Yu Erofeev, Nikolai P. Krysko, Vadim A. |
author_sort | Yakovleva, Tatiana V. |
collection | PubMed |
description | This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the “sharp wave” pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been developed for their analysis using nonlinear dynamics tools: calculating the Lyapunov exponent's spectrum, multiscale entropy, and Lempel–Ziv complexity. The calculation of the first Lyapunov exponent is carried out by three methods: Wolf, Rosenstein, and Sano–Sawada, to obtain reliable results. The seven Lyapunov exponent spectra are calculated by the Sano–Sawada method. For the observed patient, studies showed that with medical treatment, his condition did not improve, and as a result, it was recommended to switch from conservative treatment to surgical. The obtained results of the patient's EEG study using the indicated nonlinear dynamics methods are in good agreement with the medical report and MRI data. The approach developed for the analysis of EEG signals by nonlinear dynamics methods can be applied for early detection of structural changes. |
format | Online Article Text |
id | pubmed-7036140 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-70361402020-02-24 EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) Yakovleva, Tatiana V. Kutepov, Ilya E. Karas, Antonina Yu Yakovlev, Nikolai M. Dobriyan, Vitalii V. Papkova, Irina V. Zhigalov, Maxim V. Saltykova, Olga A. Krysko, Anton V. Yaroshenko, Tatiana Yu Erofeev, Nikolai P. Krysko, Vadim A. ScientificWorldJournal Research Article This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the “sharp wave” pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been developed for their analysis using nonlinear dynamics tools: calculating the Lyapunov exponent's spectrum, multiscale entropy, and Lempel–Ziv complexity. The calculation of the first Lyapunov exponent is carried out by three methods: Wolf, Rosenstein, and Sano–Sawada, to obtain reliable results. The seven Lyapunov exponent spectra are calculated by the Sano–Sawada method. For the observed patient, studies showed that with medical treatment, his condition did not improve, and as a result, it was recommended to switch from conservative treatment to surgical. The obtained results of the patient's EEG study using the indicated nonlinear dynamics methods are in good agreement with the medical report and MRI data. The approach developed for the analysis of EEG signals by nonlinear dynamics methods can be applied for early detection of structural changes. Hindawi 2020-02-11 /pmc/articles/PMC7036140/ /pubmed/32095119 http://dx.doi.org/10.1155/2020/8407872 Text en Copyright © 2020 Tatiana V. Yakovleva et al. http://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 Yakovleva, Tatiana V. Kutepov, Ilya E. Karas, Antonina Yu Yakovlev, Nikolai M. Dobriyan, Vitalii V. Papkova, Irina V. Zhigalov, Maxim V. Saltykova, Olga A. Krysko, Anton V. Yaroshenko, Tatiana Yu Erofeev, Nikolai P. Krysko, Vadim A. EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title | EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title_full | EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title_fullStr | EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title_full_unstemmed | EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title_short | EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel–Ziv Complexity, and Multiscale Entropy) |
title_sort | eeg analysis in structural focal epilepsy using the methods of nonlinear dynamics (lyapunov exponents, lempel–ziv complexity, and multiscale entropy) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7036140/ https://www.ncbi.nlm.nih.gov/pubmed/32095119 http://dx.doi.org/10.1155/2020/8407872 |
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