IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i9p1481-d407550.html
   My bibliography  Save this article

Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals

Author

Listed:
  • Samed Jukic

    (Faculty of Engineering and Natural Sciences, International Burch University, Francuske Revolucije bb, Ilidza, 71000 Sarajevo, Bosnia and Herzegovina)

  • Muzafer Saracevic

    (Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb, 36300 Novi Pazar, Serbia)

  • Abdulhamit Subasi

    (College of Engineering, Effat University, Jeddah 21478, Saudi Arabia)

  • Jasmin Kevric

    (Faculty of Engineering and Natural Sciences, International Burch University, Francuske Revolucije bb, Ilidza, 71000 Sarajevo, Bosnia and Herzegovina)

Abstract

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

Suggested Citation

  • Samed Jukic & Muzafer Saracevic & Abdulhamit Subasi & Jasmin Kevric, 2020. "Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals," Mathematics, MDPI, vol. 8(9), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1481-:d:407550
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/9/1481/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/9/1481/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:8:y:2020:i:9:p:1481-:d:407550. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.