Graph Theoretical Analysis Of Complex Networks In The Alzheimer Brain Using Navie-Bayes Classifier: An EEG And MRI Study

Abstract

In order to investigate the changes of local brain regions and the differences of functional network and structural network in patients with Alzheimer's disease, the coherent functional network and structural network were constructed by using EEG signals and MRI images of patients with Alzheimer's disease and normal controls respectively. Then the brain was divided into five brain regions (frontal, parietal, occipital, temporal and central), and seven network topological features were extracted from each brain region. ANOVA1 statistical analysis of these features showed that EEG network and MRI network of AD brain had the same results, that is, there were significant differences in the number of features, and the two groups had significant differences in the frontal lobe region. In order to further analyze the abnormal topological changes of brain structure and functional networks, the single feature and the combination of features of brain regions were used as the input of Naive Bayes classifier. The classification results showed that compared with single feature EEG and MRI network feature combination, the classification accuracy was significantly improved, and the best accuracy was 0.9565 and 0.9621, respectively. This method can effectively distinguish AD group from control group and provide effective support for the study of AD brain.