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Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease

Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very li...

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Autores principales: Ruiz de Miras, Juan, Derchi, Chiara-Camilla, Atzori, Tiziana, Mazza, Alice, Arcuri, Pietro, Salvatore, Anna, Navarro, Jorge, Saibene, Francesca Lea, Meloni, Mario, Comanducci, Angela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377880/
https://www.ncbi.nlm.nih.gov/pubmed/37509964
http://dx.doi.org/10.3390/e25071017
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author Ruiz de Miras, Juan
Derchi, Chiara-Camilla
Atzori, Tiziana
Mazza, Alice
Arcuri, Pietro
Salvatore, Anna
Navarro, Jorge
Saibene, Francesca Lea
Meloni, Mario
Comanducci, Angela
author_facet Ruiz de Miras, Juan
Derchi, Chiara-Camilla
Atzori, Tiziana
Mazza, Alice
Arcuri, Pietro
Salvatore, Anna
Navarro, Jorge
Saibene, Francesca Lea
Meloni, Mario
Comanducci, Angela
author_sort Ruiz de Miras, Juan
collection PubMed
description Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD.
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spelling pubmed-103778802023-07-29 Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease Ruiz de Miras, Juan Derchi, Chiara-Camilla Atzori, Tiziana Mazza, Alice Arcuri, Pietro Salvatore, Anna Navarro, Jorge Saibene, Francesca Lea Meloni, Mario Comanducci, Angela Entropy (Basel) Article Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD. MDPI 2023-07-02 /pmc/articles/PMC10377880/ /pubmed/37509964 http://dx.doi.org/10.3390/e25071017 Text en © 2023 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
Ruiz de Miras, Juan
Derchi, Chiara-Camilla
Atzori, Tiziana
Mazza, Alice
Arcuri, Pietro
Salvatore, Anna
Navarro, Jorge
Saibene, Francesca Lea
Meloni, Mario
Comanducci, Angela
Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title_full Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title_fullStr Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title_full_unstemmed Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title_short Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease
title_sort spatio-temporal fractal dimension analysis from resting state eeg signals in parkinson’s disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377880/
https://www.ncbi.nlm.nih.gov/pubmed/37509964
http://dx.doi.org/10.3390/e25071017
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