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EEG Fractal Analysis Reflects Brain Impairment after Stroke

Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, PD, Italy
Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro 17, 23845 Costa Masnaga, LC, Italy
Stroke Unit and Neurosonology Laboratory, Padova University Hospital, Via Giustiniani 3, 35128 Padova, PD, Italy
Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK
Padova Neuroscience Center, University of Padova, Via Orus, 35128 Padova, PD, Italy
Author to whom correspondence should be addressed.
Academic Editor: Rafał Rak
Entropy 2021, 23(5), 592;
Received: 31 March 2021 / Revised: 30 April 2021 / Accepted: 7 May 2021 / Published: 11 May 2021
(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks)
Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways. View Full-Text
Keywords: neurophysiology; stroke; EEG; neuroplasticity; fractal analysis neurophysiology; stroke; EEG; neuroplasticity; fractal analysis
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MDPI and ACS Style

Rubega, M.; Formaggio, E.; Molteni, F.; Guanziroli, E.; Di Marco, R.; Baracchini, C.; Ermani, M.; Ward, N.S.; Masiero, S.; Del Felice, A. EEG Fractal Analysis Reflects Brain Impairment after Stroke. Entropy 2021, 23, 592.

AMA Style

Rubega M, Formaggio E, Molteni F, Guanziroli E, Di Marco R, Baracchini C, Ermani M, Ward NS, Masiero S, Del Felice A. EEG Fractal Analysis Reflects Brain Impairment after Stroke. Entropy. 2021; 23(5):592.

Chicago/Turabian Style

Rubega, Maria, Emanuela Formaggio, Franco Molteni, Eleonora Guanziroli, Roberto Di Marco, Claudio Baracchini, Mario Ermani, Nick S. Ward, Stefano Masiero, and Alessandra Del Felice. 2021. "EEG Fractal Analysis Reflects Brain Impairment after Stroke" Entropy 23, no. 5: 592.

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