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  • ISSN 1674-8301
  • CN 32-1810/R
Volume 34 Issue 3
May  2020
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Article Contents
Abstract
References
Slimen Itaf Ben, Boubchir Larbi, Mbarki Zouhair, Seddik Hassene. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms[J]. The Journal of Biomedical Research, 2020, 34(3): 151-161. DOI: 10.7555/JBR.34.20190026
Citation: Slimen Itaf Ben, Boubchir Larbi, Mbarki Zouhair, Seddik Hassene. EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms[J]. The Journal of Biomedical Research, 2020, 34(3): 151-161. DOI: 10.7555/JBR.34.20190026
shu

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

  • 1.

    Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008, Tunisia

  • 2.

    Laboratoire d'Informatique Avancée de Saint-Denis Research Lab., University of Paris 8, Saint-Denis, Cedex 93526, France

More Information
  • Corresponding author:

    Itaf Ben Slimen, Electric Engineering department, Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, street Taha Hussein Montfleury, Tunis 1008, Tunisia. Tel: +216-27-542-572, E-mail: itafslimen@gmail.com

  • Received Date: February 14, 2019
  • Revised Date: November 29, 2019
  • Accepted Date: February 10, 2020
  • Available Online: April 23, 2020
    Graphical Abstract
    • Abstract
  • The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography (EEG) is an oversensitive operation and prone to errors, which has motivated the researchers to develop effective automated seizure detection methods. This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases. The proposed method consists of three steps: (i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis (MSPCA), (ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition (EMD), discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals, and (iii) allocate the feature vector to the relevant class (i.e., seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA). The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process. The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.
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    • References (40)
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