Next Article in Journal
Spectral and Informational Analysis of Temperature and Chemical Composition of Solfatara Fumaroles (Campi Flegrei, Italy)
Next Article in Special Issue
Why Dilated Convolutional Neural Networks: A Proof of Their Optimality
Previous Article in Journal
Multiple Observations for Secret-Key Binding with SRAM PUFs
Article

EEG Fractal Analysis Reflects Brain Impairment after Stroke

1
Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, PD, Italy
2
Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro 17, 23845 Costa Masnaga, LC, Italy
3
Stroke Unit and Neurosonology Laboratory, Padova University Hospital, Via Giustiniani 3, 35128 Padova, PD, Italy
4
Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK
5
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; https://doi.org/10.3390/e23050592
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
Show Figures

Figure 1

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. https://doi.org/10.3390/e23050592

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. https://doi.org/10.3390/e23050592

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. https://doi.org/10.3390/e23050592

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop