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dc.contributor.authorRuiz de Miras, Juan 
dc.date.accessioned2023-09-25T06:58:31Z
dc.date.available2023-09-25T06:58:31Z
dc.date.issued2023-07-02
dc.identifier.citationRuiz 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.urihttps://hdl.handle.net/10481/84604
dc.description.abstractComplexity 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 PDes_ES
dc.description.sponsorshipProject PID2019-105145RB-I00es_ES
dc.description.sponsorshipSpanish Government (MCIN/AEI/10.13039/501100011033)es_ES
dc.description.sponsorshipItalian Ministry of Health—(Ricerca Corrente 2022–2024)es_ES
dc.description.sponsorshipNational PhD in Artificial Intelligence, XXXVII cyclees_ES
dc.description.sponsorshipHealth and life sciences, organized by Università Campus Bio-Medico di Romaes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectFractal dimensiones_ES
dc.subjectBox countinges_ES
dc.subjectEEGes_ES
dc.subjectParkinson’s diseasees_ES
dc.subjectNeurodegenerationes_ES
dc.titleSpatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Diseasees_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/ e25071017
dc.type.hasVersionVoRes_ES


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