Diagnosis of Alzheimer’s disease based on particle swarm optimization EEG signal channel selection and gated recurrent unit

Electroencephalogram (EEG) reflect changes in the electrophysiological activity of the brain and can be used in the diagnosis of Alzheimer's disease (AD). Each EEG channel provides real-time information about the brain, while different EEG channels contain different information about the brain. Using all EEG channel data for AD diagnosis contains redundancy data, leading to increased computation and reduced data analysis accuracy. In this paper, a diagnostic method for AD based on Particle Swarm Optimization (PSO) EEG channel selection and Gated Recurrent Unit (GRU) is proposed. Using EEG energy as the fitness function and PSO to select EEG channels, the redundant information in EEG data is reduced and the accuracy of EEG data analysis is improved. GRU is a special kind of recurrent neural network (RNN) structure. It uses EEG data extracted by the principal component analysis (PCA) feature based on singular value decomposition (SVD) as input to the model. And it has a good advantage in analyzing the time series of EEG. The results show that the classification accuracy of the method in this paper reaches 0.9487, which is higher than the performance of other proposed methods. Compared to the results of using all EEG channel data analysis, the classification accuracy of this method was improved by 0.0757. It shows that the method proposed in this paper can improve the classification accuracy of EEG in AD classification tasks and can be applied to related classification tasks.