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Frequency spectrum recurrence analysis
In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by compa...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718872/ https://www.ncbi.nlm.nih.gov/pubmed/33277526 http://dx.doi.org/10.1038/s41598-020-77903-4 |
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author | Ladeira, Guênia Marwan, Norbert Destro-Filho, João-Batista Davi Ramos, Camila Lima, Gabriela |
author_facet | Ladeira, Guênia Marwan, Norbert Destro-Filho, João-Batista Davi Ramos, Camila Lima, Gabriela |
author_sort | Ladeira, Guênia |
collection | PubMed |
description | In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system. |
format | Online Article Text |
id | pubmed-7718872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77188722020-12-08 Frequency spectrum recurrence analysis Ladeira, Guênia Marwan, Norbert Destro-Filho, João-Batista Davi Ramos, Camila Lima, Gabriela Sci Rep Article In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system. Nature Publishing Group UK 2020-12-04 /pmc/articles/PMC7718872/ /pubmed/33277526 http://dx.doi.org/10.1038/s41598-020-77903-4 Text en © The Author(s) 2020 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 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/. |
spellingShingle | Article Ladeira, Guênia Marwan, Norbert Destro-Filho, João-Batista Davi Ramos, Camila Lima, Gabriela Frequency spectrum recurrence analysis |
title | Frequency spectrum recurrence analysis |
title_full | Frequency spectrum recurrence analysis |
title_fullStr | Frequency spectrum recurrence analysis |
title_full_unstemmed | Frequency spectrum recurrence analysis |
title_short | Frequency spectrum recurrence analysis |
title_sort | frequency spectrum recurrence analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718872/ https://www.ncbi.nlm.nih.gov/pubmed/33277526 http://dx.doi.org/10.1038/s41598-020-77903-4 |
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