EEG Fractal Analysis Reflects Brain Impairment after Stroke

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 th...

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Vydáno v:	Entropy (Basel, Switzerland) Ročník 23; číslo 5; s. 592
Hlavní autoři:	Rubega, Maria, Formaggio, Emanuela, Molteni, Franco, Guanziroli, Eleonora, Di Marco, Roberto, Baracchini, Claudio, Ermani, Mario, Ward, Nick S., Masiero, Stefano, Del Felice, Alessandra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 11.05.2021 MDPI
Témata:	Algorithms Biomarkers Brain Brain research Complexity Datasets EEG Electroencephalography Fractal analysis Fractals Hypoglycemia Impairment neurophysiology neuroplasticity Orthopedics Rehabilitation Standard deviation Stroke Tortuosity
ISSN:	1099-4300, 1099-4300
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Abstract	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.
AbstractList	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. 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.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.
Author	Rubega, Maria Molteni, Franco Masiero, Stefano Formaggio, Emanuela Baracchini, Claudio Del Felice, Alessandra Ward, Nick S. Guanziroli, Eleonora Di Marco, Roberto Ermani, Mario
AuthorAffiliation	2 Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro 17, 23845 Costa Masnaga, LC, Italy; franco56.molteni@gmail.com (F.M.); eleonora.guanziroli@gmail.com (E.G.) 4 Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK; n.ward@ucl.ac.uk 1 Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, PD, Italy; emanuela.formaggio@unipd.it (E.F.); roberto.dimarco@unipd.it (R.D.M.); stef.masiero@unipd.it (S.M.); alessandra.delfelice@unipd.it (A.D.F.) 5 Padova Neuroscience Center, University of Padova, Via Orus, 35128 Padova, PD, Italy 3 Stroke Unit and Neurosonology Laboratory, Padova University Hospital, Via Giustiniani 3, 35128 Padova, PD, Italy; claudiobaracchini@gmail.com (C.B.); mario.ermani@unipd.it (M.E.)
AuthorAffiliation_xml	– name: 3 Stroke Unit and Neurosonology Laboratory, Padova University Hospital, Via Giustiniani 3, 35128 Padova, PD, Italy; claudiobaracchini@gmail.com (C.B.); mario.ermani@unipd.it (M.E.) – name: 5 Padova Neuroscience Center, University of Padova, Via Orus, 35128 Padova, PD, Italy – name: 4 Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK; n.ward@ucl.ac.uk – name: 2 Villa Beretta Rehabilitation Center, Valduce Hospital, Via N. Sauro 17, 23845 Costa Masnaga, LC, Italy; franco56.molteni@gmail.com (F.M.); eleonora.guanziroli@gmail.com (E.G.) – name: 1 Department of Neuroscience, Section of Rehabilitation, University of Padova, Via Giustiniani 3, 35128 Padova, PD, Italy; emanuela.formaggio@unipd.it (E.F.); roberto.dimarco@unipd.it (R.D.M.); stef.masiero@unipd.it (S.M.); alessandra.delfelice@unipd.it (A.D.F.)
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Snippet	Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches...
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SubjectTerms	Algorithms Biomarkers Brain Brain research Complexity Datasets EEG Electroencephalography Fractal analysis Fractals Hypoglycemia Impairment neurophysiology neuroplasticity Orthopedics Rehabilitation Standard deviation Stroke Tortuosity
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Title	EEG Fractal Analysis Reflects Brain Impairment after Stroke
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Volume	23
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