Search

In this paper, the complexity and approximate entropy algorithm are used to extract the features of electroencephalogram(EEG) in patients with Alzheimer's disease(AD) and normal people under open-eye resting state, and these features are used as indicators to analyze the early EEG characteristics of AD. The EEG data are collected from 15 AD patients and 15 normal subjects under resting open-eye state and the complexity and approximate entropy were calculated based on EEG signal. The results shows that the features of EEG in AD group are lower than those in normal group, and the values of approximate entropy and complexity will decreased with the increase of AD grade. The AD early assessment system was designed based on this two features analysis and combined with the scale assessment, this system includes the software on PC and the hardware device used to collect EEG data. The application of this system will provides supports for AD early assessment.