dc.contributor.author | Ruiz de Miras, Juan | |
dc.date.accessioned | 2023-09-25T06:58:31Z | |
dc.date.available | 2023-09-25T06:58:31Z | |
dc.date.issued | 2023-07-02 | |
dc.identifier.citation | Ruiz de Miras, J.; Derchi, C.-C.; Atzori, T.; Mazza, A.; Arcuri, P.; Salvatore, A.; Navarro, J.; Saibene, F.L.; Meloni, M.; Comanducci, A. Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease. Entropy 2023, 25, 1017. [https://doi.org/10.3390/e25071017] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/84604 | |
dc.description.abstract | 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 | es_ES |
dc.description.sponsorship | Project PID2019-105145RB-I00 | es_ES |
dc.description.sponsorship | Spanish
Government (MCIN/AEI/10.13039/501100011033) | es_ES |
dc.description.sponsorship | Italian
Ministry of Health—(Ricerca Corrente 2022–2024) | es_ES |
dc.description.sponsorship | National
PhD in Artificial Intelligence, XXXVII cycle | es_ES |
dc.description.sponsorship | Health and life sciences, organized by
Università Campus Bio-Medico di Roma | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Fractal dimension | es_ES |
dc.subject | Box counting | es_ES |
dc.subject | EEG | es_ES |
dc.subject | Parkinson’s disease | es_ES |
dc.subject | Neurodegeneration | es_ES |
dc.title | Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3390/ e25071017 | |
dc.type.hasVersion | VoR | es_ES |