Abnormal EEG Complexity and Alpha Oscillation of Resting State in Chronic Stroke Patients

Abstract

A valid evaluation of neurological functions after stroke may improve clinical decision-making. The aim of this study was to compare the performance of EEG-related indexes in differentiating stroke patients from control participants, and to investigate pathological EEG changes after chronic stroke. 20 stroke and 13 healthy participants were recruited, and spontaneous EEG signals were recorded during the resting state. EEG rhythms and complexity were calculated based on Fast Fourier Transform and the fuzzy approximate entropy (fApEn) algorithm. The results showed a significant difference of alpha rhythm (p = 0.019) and fApEn (p = 0.003) of EEG signals from brain area among ipsilesional, contralesion hemisphere of stroke patients and corresponding brain hemisphere of healthy participants. EEG fApEn had the best classification accuracy (84.85%), sensitivity (85.00%), and specificity (84.62%) among these EEG-related indexes. Our study provides a potential method to evaluate alterations in the properties of the injured brain, which help us to understand neurological change in chronic strokes.