Complexity Analysis of EEG in AD Patients with Fractional Permutation Entropy

The rapid increase in the number of people with Alzheimer's disease (AD) represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new therapies and treatments. The objective of the study reported in this paper is to develop the method of complexity characterization of EEG in AD patients in its early stages. In this paper, we propose the fractional permutation entropy (FPE) to analyze 16-channels electroencephalograph (EEG) signals from 15 AD groups and 15 age-matched control groups. FEE is a modified method based on the permutation entropy (PE). which can be used as a measurement to analyze the complexity of the EEG signals. Firstly, the simulation analysis of FEE was performed and the results show that FEE could effectively evaluate the complexity of time series. Then we calculated the FEE of real EEG series to detect the complexity abnormalities in AD. It is demonstrated that the FPE of AD patients is significantly decreased in alpha band at most EEG channels. Thus, it suggests that FEE may become a probably useful tool to analyze the complexity abnormalities of AD and some other neurologic disorders.