Journal of Modeling in Engineering

Journal of Modeling in Engineering

Introducing a Non-Linear Feature Extraction Based on DT-CWT Coefficients of EEG Signals for Detecting Epileptic Seizures

Document Type : Research Paper

Authors
Afshin Koleiji 1
Nematollah Ezzati 1
Mohsen Bahonar 2
Abdollah Jafari Chashmi 2
Ehsan Mostafapour 1
1 Faculty of Electrical And Computer Engineering, Urmia University of Technology, Urmia, Iran
2 Islamic Azad University, Mehdi Shahr Branch, Mehdi Shahr, Iran
10.22075/jme.2025.30724.2461
Abstract
Epilepsy is a type of brain disease that can be diagnosed by observing EEG signals. This disease often occurs in children, but some cases are also observed in adults. Early diagnosis of this disease is a challenging task for doctors. In this study, the authors have classified epileptic and normal EEG signals by adopting a deep learning approach. To obtain efficient features, the Dual-Tree Complex Wavelet Transform (DTCWT) is considered. Then, the wavelet coefficients are decomposed to extract nonlinear features. These features are used as input to the Radial Basis Function (RBF) hybrid base classifier. Using the proposed method, approximately 99% classification accuracy is achieved, which requires a significant improvement compared to previous proposed algorithms. This is the first time that nonlinear feature extraction from DT-CWT coefficients of an EEG signal is used for epilepsy diagnosis.
Keywords
Epilepsy
K-Means algorithm
Non-linear features
Radial basis function networks
Brain EEG classification
Feature reduction
Subjects
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Journal of Modeling in Engineering
Volume 24, Issue 84
Winter 2026
Pages 87-99

  • Receive Date 23 May 2023
  • Revise Date 12 February 2024
  • Accept Date 12 March 2025