Variation Trends of Fractal Dimension in Epileptic EEG Signals

Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (<i>D</i>) were opposite in the literature, i.e., both <i>D</i>...

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Autores principales:	Zhiwei Li, Jun Li, Yousheng Xia, Pingfa Feng, Feng Feng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electroencephalography (EEG) signal Seizure detection fractal dimension Higuchi algorithm roughness scaling extraction Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/207355dfd6274bca8f97ef357f577073
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spelling	oai:doaj.org-article:207355dfd6274bca8f97ef357f5770732021-11-25T16:13:07ZVariation Trends of Fractal Dimension in Epileptic EEG Signals10.3390/a141103161999-4893https://doaj.org/article/207355dfd6274bca8f97ef357f5770732021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/316https://doaj.org/toc/1999-4893Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (<i>D</i>) were opposite in the literature, i.e., both <i>D</i> decreasing and increasing were reported in previous studies during seizure status relative to the normal status, undermining the feasibility of fractal algorithms for EEG analysis to detect epileptic seizures. In this study, two algorithms with high accuracy in the <i>D</i> calculation, Higuchi and roughness scaling extraction (RSE), were used to study <i>D</i> variation of EEG signals with seizures. It was found that the denoising operation had an important influence on <i>D</i> variation trend. Moreover, the <i>D</i> variation obtained by RSE algorithm was larger than that by Higuchi algorithm, because the non-fractal nature of EEG signals during normal status could be detected and quantified by RSE algorithm. The above findings in this study could be promising to make more understandings of the nonlinear nature and scaling behaviors of EEG signals.Zhiwei LiJun LiYousheng XiaPingfa FengFeng FengMDPI AGarticleelectroencephalography (EEG) signalSeizure detectionfractal dimensionHiguchi algorithmroughness scaling extractionIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 316, p 316 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electroencephalography (EEG) signal Seizure detection fractal dimension Higuchi algorithm roughness scaling extraction Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	electroencephalography (EEG) signal Seizure detection fractal dimension Higuchi algorithm roughness scaling extraction Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Zhiwei Li Jun Li Yousheng Xia Pingfa Feng Feng Feng Variation Trends of Fractal Dimension in Epileptic EEG Signals
description	Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (<i>D</i>) were opposite in the literature, i.e., both <i>D</i> decreasing and increasing were reported in previous studies during seizure status relative to the normal status, undermining the feasibility of fractal algorithms for EEG analysis to detect epileptic seizures. In this study, two algorithms with high accuracy in the <i>D</i> calculation, Higuchi and roughness scaling extraction (RSE), were used to study <i>D</i> variation of EEG signals with seizures. It was found that the denoising operation had an important influence on <i>D</i> variation trend. Moreover, the <i>D</i> variation obtained by RSE algorithm was larger than that by Higuchi algorithm, because the non-fractal nature of EEG signals during normal status could be detected and quantified by RSE algorithm. The above findings in this study could be promising to make more understandings of the nonlinear nature and scaling behaviors of EEG signals.
format	article
author	Zhiwei Li Jun Li Yousheng Xia Pingfa Feng Feng Feng
author_facet	Zhiwei Li Jun Li Yousheng Xia Pingfa Feng Feng Feng
author_sort	Zhiwei Li
title	Variation Trends of Fractal Dimension in Epileptic EEG Signals
title_short	Variation Trends of Fractal Dimension in Epileptic EEG Signals
title_full	Variation Trends of Fractal Dimension in Epileptic EEG Signals
title_fullStr	Variation Trends of Fractal Dimension in Epileptic EEG Signals
title_full_unstemmed	Variation Trends of Fractal Dimension in Epileptic EEG Signals
title_sort	variation trends of fractal dimension in epileptic eeg signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/207355dfd6274bca8f97ef357f577073
work_keys_str_mv	AT zhiweili variationtrendsoffractaldimensioninepilepticeegsignals AT junli variationtrendsoffractaldimensioninepilepticeegsignals AT youshengxia variationtrendsoffractaldimensioninepilepticeegsignals AT pingfafeng variationtrendsoffractaldimensioninepilepticeegsignals AT fengfeng variationtrendsoffractaldimensioninepilepticeegsignals
_version_	1718413243971534848

Variation Trends of Fractal Dimension in Epileptic EEG Signals

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