EEG Fractal Analysis Reflects Brain Impairment after Stroke

Area of Science:

• Neuroscience
• Biomedical Engineering
• Clinical Neurology

Background:

• Stroke is a leading cause of long-term disability, necessitating better understanding of recovery mechanisms.
• Fractal analysis offers a method to quantify the complexity of physiological signal fluctuations.
• Electroencephalography (EEG) measures brain activity, and its complexity may be altered by stroke.

Purpose of the Study:

• To investigate if fractal algorithms applied to resting-state EEG signals can detect brain impairment in stroke survivors.
• To assess the potential of fractal measures as biomarkers for tracking stroke recovery and informing neurorehabilitation.

Main Methods:

• Studied 16 stroke survivors in hyperacute/acute phases and 35 in the early subacute phase.
• Compared resting-state EEG fractal measures (Higuchi Index, Tortuosity) between stroke survivors and 11 healthy controls.
• Analyzed EEG data from <48 hours, ~1 week, 8-32 days, and ~2 months post-stroke.

Main Results:

• Both Higuchi Index and Tortuosity values were significantly lower in stroke survivors compared to healthy controls.
• This reduction in fractal indices persisted throughout the acute and early subacute stages post-stroke.
• Lower complexity in EEG signals indicates significant brain impairment after stroke.

Conclusions:

• Fractal analysis of EEG signals provides a sensitive metric for detecting reduced brain complexity post-stroke.
• Fractal indices like Higuchi Index and Tortuosity show promise as reliable biomarkers for early stroke assessment.
• These findings could aid in tailoring neurorehabilitation pathways for stroke survivors.