Science Journal of Circuits, Systems and Signal Processing

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Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines

Received: Jul. 14, 2021    Accepted: Jul. 23, 2021    Published: Aug. 18, 2021
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Abstract

Electroencephalogram (EEG) remains the primary technique in the diagnosis and localization of partial epilepsy seizures. Despite the advent of modern neuroimaging techniques, the use of EEG signals for locating epilepsy-affected brain areas is still convenient. That is why during these last decades, several computer-aided detection (CAD) methodologies have been proposed to detect and discriminate focal (F) EEG signals, and hence locate epileptogenic foci. In this impetus, this paper applied Jacobi polynomial transforms (JPTs)-based entropy measures to analyze the complexity and discriminate the bivariate focal (F) and non-focal (NF) EEG signals. Jacobi polynomial transforms namely discrete Legendre transform (DLT) and discrete Chebyshev transform (DChT) are applied to separate F and NF EEG signals into their different rhythms. Furthermore, entropy measures like approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn), fuzzy entropy (FuzzyEn) and increment entropy (IncrEn) are extracted. For direct discrimination between F and NF EEG signals, extracted entropies are combined to define different features vectors that are fed as inputs of two kernel machines namely the least-squares support vector machine (LS-SVM) and simple multi-layer perceptron neural network (sMLPNN). Experimental results demonstrated that our methodology achieved the highest performance of 98.33% sensitivity, 98.00% specificity, and 98.17% accuracy in discriminating F and NF EEG signals with sMLPNN classifier. In addition, our methodology will be useful to clinicians in providing an accurate and objective paradigm for locating epilepsy-affected brain areas.

DOI 10.11648/j.cssp.20211002.11
Published in Science Journal of Circuits, Systems and Signal Processing ( Volume 10, Issue 2, December 2021 )
Page(s) 25-37
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Electroencephalogram (EEG) Signals, Jacobi Polynomial Transforms (JPTs), Entropy Measures, Bivariate Focal (F) EEG, Epileptogenic Focus, Kernel Machines

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Cite This Article
  • APA Style

    Laurent Chanel Djoufack Nkengfack, Daniel Tchiotsop, Romain Atangana, Beaudelaire Saha Tchinda, Valérie Louis-Door, et al. (2021). Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines. Science Journal of Circuits, Systems and Signal Processing, 10(2), 25-37. https://doi.org/10.11648/j.cssp.20211002.11

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    ACS Style

    Laurent Chanel Djoufack Nkengfack; Daniel Tchiotsop; Romain Atangana; Beaudelaire Saha Tchinda; Valérie Louis-Door, et al. Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines. Sci. J. Circuits Syst. Signal Process. 2021, 10(2), 25-37. doi: 10.11648/j.cssp.20211002.11

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    AMA Style

    Laurent Chanel Djoufack Nkengfack, Daniel Tchiotsop, Romain Atangana, Beaudelaire Saha Tchinda, Valérie Louis-Door, et al. Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines. Sci J Circuits Syst Signal Process. 2021;10(2):25-37. doi: 10.11648/j.cssp.20211002.11

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  • @article{10.11648/j.cssp.20211002.11,
      author = {Laurent Chanel Djoufack Nkengfack and Daniel Tchiotsop and Romain Atangana and Beaudelaire Saha Tchinda and Valérie Louis-Door and Didier Wolf},
      title = {Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines},
      journal = {Science Journal of Circuits, Systems and Signal Processing},
      volume = {10},
      number = {2},
      pages = {25-37},
      doi = {10.11648/j.cssp.20211002.11},
      url = {https://doi.org/10.11648/j.cssp.20211002.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.cssp.20211002.11},
      abstract = {Electroencephalogram (EEG) remains the primary technique in the diagnosis and localization of partial epilepsy seizures. Despite the advent of modern neuroimaging techniques, the use of EEG signals for locating epilepsy-affected brain areas is still convenient. That is why during these last decades, several computer-aided detection (CAD) methodologies have been proposed to detect and discriminate focal (F) EEG signals, and hence locate epileptogenic foci. In this impetus, this paper applied Jacobi polynomial transforms (JPTs)-based entropy measures to analyze the complexity and discriminate the bivariate focal (F) and non-focal (NF) EEG signals. Jacobi polynomial transforms namely discrete Legendre transform (DLT) and discrete Chebyshev transform (DChT) are applied to separate F and NF EEG signals into their different rhythms. Furthermore, entropy measures like approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn), fuzzy entropy (FuzzyEn) and increment entropy (IncrEn) are extracted. For direct discrimination between F and NF EEG signals, extracted entropies are combined to define different features vectors that are fed as inputs of two kernel machines namely the least-squares support vector machine (LS-SVM) and simple multi-layer perceptron neural network (sMLPNN). Experimental results demonstrated that our methodology achieved the highest performance of 98.33% sensitivity, 98.00% specificity, and 98.17% accuracy in discriminating F and NF EEG signals with sMLPNN classifier. In addition, our methodology will be useful to clinicians in providing an accurate and objective paradigm for locating epilepsy-affected brain areas.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Jacobi Polynomial Transforms-Based Entropy Measures for Focal and Non-Focal EEG Signals Discrimination Using Kernel Machines
    AU  - Laurent Chanel Djoufack Nkengfack
    AU  - Daniel Tchiotsop
    AU  - Romain Atangana
    AU  - Beaudelaire Saha Tchinda
    AU  - Valérie Louis-Door
    AU  - Didier Wolf
    Y1  - 2021/08/18
    PY  - 2021
    N1  - https://doi.org/10.11648/j.cssp.20211002.11
    DO  - 10.11648/j.cssp.20211002.11
    T2  - Science Journal of Circuits, Systems and Signal Processing
    JF  - Science Journal of Circuits, Systems and Signal Processing
    JO  - Science Journal of Circuits, Systems and Signal Processing
    SP  - 25
    EP  - 37
    PB  - Science Publishing Group
    SN  - 2326-9073
    UR  - https://doi.org/10.11648/j.cssp.20211002.11
    AB  - Electroencephalogram (EEG) remains the primary technique in the diagnosis and localization of partial epilepsy seizures. Despite the advent of modern neuroimaging techniques, the use of EEG signals for locating epilepsy-affected brain areas is still convenient. That is why during these last decades, several computer-aided detection (CAD) methodologies have been proposed to detect and discriminate focal (F) EEG signals, and hence locate epileptogenic foci. In this impetus, this paper applied Jacobi polynomial transforms (JPTs)-based entropy measures to analyze the complexity and discriminate the bivariate focal (F) and non-focal (NF) EEG signals. Jacobi polynomial transforms namely discrete Legendre transform (DLT) and discrete Chebyshev transform (DChT) are applied to separate F and NF EEG signals into their different rhythms. Furthermore, entropy measures like approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PermEn), fuzzy entropy (FuzzyEn) and increment entropy (IncrEn) are extracted. For direct discrimination between F and NF EEG signals, extracted entropies are combined to define different features vectors that are fed as inputs of two kernel machines namely the least-squares support vector machine (LS-SVM) and simple multi-layer perceptron neural network (sMLPNN). Experimental results demonstrated that our methodology achieved the highest performance of 98.33% sensitivity, 98.00% specificity, and 98.17% accuracy in discriminating F and NF EEG signals with sMLPNN classifier. In addition, our methodology will be useful to clinicians in providing an accurate and objective paradigm for locating epilepsy-affected brain areas.
    VL  - 10
    IS  - 2
    ER  - 

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Author Information
  • Department of Physics, University of Dschang, Dschang, Cameroon; University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon

  • University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon

  • Higher Teacher Training College (HTTC) of Bertoua, University of Ngaoundéré, Bertoua, Cameroon

  • University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon

  • ENSEM de Lorraine, University of Lorraine, Nancy, France

  • ENSEM de Lorraine, University of Lorraine, Nancy, France

  • Section