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

Laurent Chanel Djoufack Nkengfack; Daniel Tchiotsop; Romain Atangana; Beaudelaire Saha Tchinda; Valérie Louis-Door; Didier Wolf

doi:doi:10.11648/j.cssp.20211002.11

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

Laurent Chanel Djoufack Nkengfack, Daniel Tchiotsop, Romain Atangana, Beaudelaire Saha Tchinda, Valérie Louis-Door, Didier Wolf

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 10, Issue 2)

Received: 14 July 2021 Accepted: 23 July 2021 Published: 18 August 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.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 10, Issue 2)
DOI	10.11648/j.cssp.20211002.11
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

References

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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://article.sciencepublishinggroup.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

Laurent Chanel Djoufack Nkengfack

Department of Physics, University of Dschang, Dschang, Cameroon
Daniel Tchiotsop

University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon
Romain Atangana

Higher Teacher Training College (HTTC) of Bertoua, University of Ngaoundéré, Bertoua, Cameroon
Beaudelaire Saha Tchinda

University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon
Valérie Louis-Door

ENSEM de Lorraine, University of Lorraine, Nancy, France
Didier Wolf

ENSEM de Lorraine, University of Lorraine, Nancy, France

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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

Copy | Download

@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://article.sciencepublishinggroup.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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