IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v12y2025i1d10.1007_s40745-024-00533-4.html
   My bibliography  Save this article

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

Author

Listed:
  • Elias Mazrooei Rad

    (Khavaran Institute of Higher Education)

  • Mahdi Azarnoosh

    (Islamic Azad University)

  • Majid Ghoshuni

    (Islamic Azad University)

  • Mohammad Mahdi Khalilzadeh

    (Islamic Azad University)

Abstract

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%.

Suggested Citation

  • Elias Mazrooei Rad & Mahdi Azarnoosh & Majid Ghoshuni & Mohammad Mahdi Khalilzadeh, 2025. "Combining Nonlinear Features of EEG and MRI to Diagnose Alzheimer’s Disease," Annals of Data Science, Springer, vol. 12(1), pages 95-116, February.
  • Handle: RePEc:spr:aodasc:v:12:y:2025:i:1:d:10.1007_s40745-024-00533-4
    DOI: 10.1007/s40745-024-00533-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-024-00533-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40745-024-00533-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:12:y:2025:i:1:d:10.1007_s40745-024-00533-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.