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Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed...

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Detalles Bibliográficos
Autores principales: Chen, Xiaobi, Xu, Guanghua, Du, Chenghang, Zhang, Sicong, Zhang, Xun, Teng, Zhicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415957/
https://www.ncbi.nlm.nih.gov/pubmed/36016044
http://dx.doi.org/10.3390/s22166283
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author Chen, Xiaobi
Xu, Guanghua
Du, Chenghang
Zhang, Sicong
Zhang, Xun
Teng, Zhicheng
author_facet Chen, Xiaobi
Xu, Guanghua
Du, Chenghang
Zhang, Sicong
Zhang, Xun
Teng, Zhicheng
author_sort Chen, Xiaobi
collection PubMed
description As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.
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spelling pubmed-94159572022-08-27 Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series Chen, Xiaobi Xu, Guanghua Du, Chenghang Zhang, Sicong Zhang, Xun Teng, Zhicheng Sensors (Basel) Article As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis. MDPI 2022-08-21 /pmc/articles/PMC9415957/ /pubmed/36016044 http://dx.doi.org/10.3390/s22166283 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Xiaobi
Xu, Guanghua
Du, Chenghang
Zhang, Sicong
Zhang, Xun
Teng, Zhicheng
Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title_full Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title_fullStr Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title_full_unstemmed Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title_short Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series
title_sort poincaré plot nonextensive distribution entropy: a new method for electroencephalography (eeg) time series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415957/
https://www.ncbi.nlm.nih.gov/pubmed/36016044
http://dx.doi.org/10.3390/s22166283
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