Assessment of EEG dynamical complexity in Alzheimer's disease using multiscale entropy.

OBJECTIVE Multiscale entropy (MSE) is a recently proposed entropy-based index of physiological complexity, evaluating signals at multiple temporal scales. To test this method as an aid to elucidating the pathophysiology of Alzheimer's disease (AD), we examined MSE in resting state EEG activity in comparison with traditional EEG analysis. METHODS We recorded EEG in medication-free 15 presenile AD patients and 18 age- and sex-matched healthy control (HC) subjects. MSE was calculated for continuous 60-s epochs for each group, concurrently with power analysis. RESULTS The MSE results from smaller and larger scales were associated with higher and lower frequencies of relative power, respectively. Group analysis demonstrated that the AD group had less complexity at smaller scales in more frontal areas, consistent with previous findings. In contrast, higher complexity at larger scales was observed across brain areas in AD group and this higher complexity was significantly correlated with cognitive decline. CONCLUSIONS MSE measures identified an abnormal complexity profile across different temporal scales and their relation to the severity of AD. SIGNIFICANCE These findings indicate that entropy-based analytic methods with applied at temporal scales may serve as a complementary approach for characterizing and understanding abnormal cortical dynamics in AD.