Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson's Disease.
EEG
Parkinson’s disease
box-counting
fractal dimension
neurodegeneration
Journal
Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874
Informations de publication
Date de publication:
02 Jul 2023
02 Jul 2023
Historique:
received:
15
05
2023
revised:
01
07
2023
accepted:
02
07
2023
medline:
29
7
2023
pubmed:
29
7
2023
entrez:
29
7
2023
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 37509964
pii: e25071017
doi: 10.3390/e25071017
pmc: PMC10377880
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Spanish Government
ID : PID2019-105145RB-I00
Organisme : Italian Ministry of Health
ID : Ricerca Corrente 2022-2024
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