EEG Fractal Analysis Reflects Brain Impairment after Stroke

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 5; p. 592
• Main Authors: Rubega, Maria, Formaggio, Emanuela, Molteni, Franco, Guanziroli, Eleonora, Di Marco, Roberto, Baracchini, Claudio, Ermani, Mario, Ward, Nick S., Masiero, Stefano, Del Felice, Alessandra
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.05.2021; MDPI
• Subjects: 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
• DOI: 10.3390/e23050592

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.