Early Detection of Alzheimer’s Disease With Nonlinear Features of EEG Signal and MRI Images by Convolutional Neural Network

Background: The main purpose of this study is to provide a method for early diagnosis of Alzheimer’s disease. This disease reduces memory function by destroying neurons in the nervous system and reducing connections and neural interactions. Alzheimer’s disease is on the rise and there is no cure for it. With the help of medical image processing, Alzheimer’s disease is determined and the similarity of the characteristics of brain signals with medical images is determined.
Methods: Then, by presenting the characteristics of effective brain signals, the mild Alzheimer’s group is determined. The level of this disease should be diagnosed according to the relationship between this disease and different features in the brain signal and medical images.
Results: For 40 participants brain signals and MRI images were recorded during 4 phase protocol and after appropriate preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy, and Lyapunov exponential are extracted and classification is done using a convolutional neural network (CNN). The use of this deep learning method can have more appropriate and accurate results among other classification methods.
Conclusions: The accuracy of the results in the reminding phase is 97.5% for the brain signal and 99% for the MRI images, which is an acceptable result.