EEG Recognition of Alzheimer's Disease Based on PCA and Latent Energy

Aiming at the characteristics of abnormally slow and diffuse electroencephalogram (EEG) signals of Alzheimer's disease (AD), based on the latent variable analysis (LVA) theory, this study proposed an EEG diagnosis method based on principal component analysis (PCA) and energy feature recognition of potential signals. Firstly, the original electroencephalogram (EEG) signal was reduced into potential signal by principal component analysis (PCA), and then the potential signal was divided into delta, theta, alpha and beta frequency bands by fast Fourier transform (FFT), and then the energy features of the potential signal were extracted. By comparing the results of AD and control group, the distribution of EEG potential signals in AD group was scattered, and the AD rhythm was slowed down in low dimensional space (manifested as the main activity shifting from high frequency to low frequency). Secondly, support vector machine (SVM) classification analysis was performed combined with the energy characteristics of the potential signals, and it was found that the potential signals could effectively identify AD patients, and the recognition effect was better than that of the original EEG signals. In addition, increasing the number of latent signals can improve the SVM classification performance, for example, in the θ-band, the joint features can achieve 94.12% classification accuracy. It is shown that the proposed method preserves the valid information of the original EEG while eliminating the redundant information between EEG channels, which ultimately improves the classification accuracy.