Combining Nonlinear Features of EEG and MRI to Diagnose Alzheimer’s Disease

• Published in: Annals of data science Vol. 12; no. 1; pp. 95 - 116
• Main Authors: Rad, Elias Mazrooei, Azarnoosh, Mahdi, Ghoshuni, Majid, Khalilzadeh, Mohammad Mahdi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025; Springer Nature B.V
• Subjects: Accuracy; Alzheimer's disease; Artificial Intelligence; Atrophy; Brain; Business and Management; Economics; Electroencephalography; Feature extraction; Finance; Fluid filters; Image segmentation; Insurance; Liapunov exponents; Magnetic resonance imaging; Management; Medical imaging; Neural networks; Nonlinear dynamics; Statistics for Business; Support vector machines; SVM; Alzheimer’s disease; Variance analysis; MRI images; EEG brain signal; Elman neural network
• ISSN: 2198-5804; 2198-5812
• DOI: 10.1007/s40745-024-00533-4

This article, a new method for the diagnosis of Alzheimer’s disease in the mild stage is presented according to combining the characteristics of EEG signal and MRI images. The brain signal is recorded in four modes of closed-eyes, open eye, reminder, and stimulation from three channels Pz, Cz, and Fz of 90 participants in three groups of healthy subjects, mild, and severe Alzheimer’s disease (AD) patients.In addition, MRI images are taken with at least 3 Tesla and the thickness of 3 mm so it can be examined the senile plaques and neurofibrillary tangles. Proper image segmentation, mask, and sharp filters are used for preprocessing. Then proper features of brain signals extracted according to the nonlinear and chaotic nature of the brain such as Lyapunov exponent, correlation dimension, and entropy. Results: These features combined with brain MRI images properties including Medial Temporal Lobe Atrophy (MTA), Cerebral Spinal Fluid (CSF), Gray Matter (GM), Index Asymmetry (IA) and White Matter (WM) to diagnose the disease. Then two classifiers, the support vector machine, and Elman neural network are used with the optimal combined features extracted by analysis of variance. Results showed that between the three brain signals, and between the four modes of evaluation, the accuracy of the Pz channel and excitation mode was more than the others. Conclusions: Finally, by using neural network dynamics because of the nonlinear properties studied and due to the nonlinear dynamics of the EEG signal, the Elman neural network is used. However, it is the important to note that, by the way of analyzing medical images, we can determine the most effective channel for recording brain signals. 3D segmentation of MRI images further helps researchers diagnose Alzheimer’s disease and obtain important information. The accuracy of the results in Elman neural network with the combination of brain signal features and medical images is 94.4% and in the case without combining the signal and image features, the accuracy of the results is 92.2%. The use of nonlinear classifiers is more appropriate than other classification methods due to the nonlinear dynamics of the brain signal. The accuracy of the results in the support vector machine with RBF core with the combination of brain signal features and medical images is 75.5% and in the case without combining the signal and image features, the accuracy of the results is 76.8%.