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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2021. Published by Science Publishing Group

Keywords

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

References

[1]	Acharya U. R., Swapna G., Martis R. J., Suri J. S., ‘‘Automated EEG Analysis of Epilepsy: A Review’’, Knowledge Based System, No. 45, pp. 147-165, 2013.
[2]	Pati S., Alexopoulos A. V, ‘‘Pharmacoresistant Epilepsy: From Pathogenesis to Current and Emerging Therapies’’, Cleve Clin. J. Med., No. 77, pp. 457-467; 2010.
[3]	Rajeev Sharma, Ram Bilas Pachori, U. Rajendra Acharya, ‘‘Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of F Electroencephalogram Signals’’, Entropy, Vol. 17, pp. 669-691, 2015.
[4]	Rajeev Sharma, Ram Bilas Pachori, U. Rajendra Acharya, ‘‘An Integrated Index for the Identification of F Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures’’, Entropy, Vol. 17, No. 8, pp. 5218-5240, 2015. DOI: 10.3390/e17085218.
[5]	Manish Sharma, Abhinav Dhere, Ram Bilas Pachori, U. Rajendra Acharya, ‘‘An Automatic Detection of F EEG Signals Using New Class of Time-Frequency Localized Orthogonal Wavelet Filter Banks’’, Knowledge-Based Systems, 2016. DOI: 10.1016/j.knosys.2016.11.024.
[6]	Abhijit Bhattacharyya, Ram Bilas Pachori, U. Rajendra Acharya, ‘‘Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to F EEG Signal Analysis’’, Entropy, Vol. 19, No. 99, 2017. DOI: 10.3390/e19030099.
[7]	Pushpendra Singh and Ram Bilas Pachori, ‘‘Classification of F and NF EEG Signals Using Features Derived From Fourier-Based Rhythms’’, Journal of Mechanics in Medicine and Biology, 2017. DOI: 10.1142/S0219519417400024.
[8]	Arunkumar N., Ramkumar K., Venkatraman V., Enas Abdulhay, Steven Lawrence Fernandes, Seifeedine Kadry, Sophia Segal, ‘‘Classification of F and Non F EEG Using Entropies’’, Pattern Recognition Letters, No. 000, pp. 1-6, 2017.
[9]	Acharya U. Rajendra, Y. Hagiwara, S. N. Deshpande, S. Suren, J. E. W. Koh, S. L. Oh, N. Arunkumar, E. J. Ciaccio, C. M. Lim, ‘‘Characterization of Focal EEG Signals: A Review’’, Future Generation Computer Systems, 2018.
[10]	Gupta Varun and Ram Bilas Pachori, ‘‘A New Method for Classification of Focal and Non-Focal EEG Signals’’, Machine Intelligence and Signal Analysis, Springer, pp. 235-246, 2019.
[11]	Gupta Vipin and Pachori Ram Bilas, ‘‘Classification of Focal EEG Signals Using FBSE Based Flexible Time-Frequency Coverage Wavelet Transform’’, Biomedical Signal Processing and Control, 2020.
[12]	Djoufack Nkengfack Laurent Chanel, Tchiotsop Daniel, Atangana Romain, Louis-Door Valérie, Wolf Didier, ‘‘EEG Signals Analysis for Epileptic Seizures Detection Using Polynomial Transforms, Linear Discriminant Analysis and Support Vector Machines’’, Biomedical Signal Processing and Control, Vol. 62, 2020. https://doi.org/10.1016/j.bspc.2020.102141.
[13]	Djoufack Nkengfack Laurent Chanel, Tchiotsop Daniel, Atangana Romain, Louis-Door Valérie, Wolf Didier, ‘‘Classification of EEG Signals for Epileptic Seizures Detection and Eye States Identification Using Jacobi Polynomial Transforms-Based Measures of Complexity and Least-Square Support Vector Machine’’, Informatics in Medicine Unlocked, Vol., 2021. https://doi.org/10.1016/j.imu.2021.100536.
[14]	Andrzejak R. G., Schindler K., Rummel C., “Nonrandomness, Nonlinear Dependence and Nonstationarity of Electroencephalographic Recordings FromEpilepsy Patients”, Physical Review E, Vol. 86, p. 046206, 2012.
[15]	Pincus S., ‘‘Approximate Entropy as a Measure of System-Complexity’’. Proc. Natl. Acad. Sci. U.S.A., Vol. 88, pp. 2297-2301, 1991.
[16]	Pincus S. and Huang W., ‘‘Approximate Entropy: Statistical Properties and Applications’’, Commun. Stat. Theory Methods, Vol. 21, pp. 3061-3077, 1992.
[17]	Pincus S., ‘‘Approximate Entropy (ApEn) as a Complexity Measure’’, Chaos, Vol. 5, pp. 110-117, 1995.
[18]	Richman Joshua S., Moorman Randall J., ‘‘Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy’’, Am J Physiol Heart CircPhysiol, 278: H2039–H2049, 2000.
[19]	Bandt C. and Pompe B., ‘‘Permutation Entropy: A Natural Complexity Measure for Time Series’’, Phys. Rev. Lett., Vol. 88, pp. 174102-1–174102-4, 2002.
[20]	Bandt C., ‘‘Ordinal Time Series Analysis’’, Ecol. Modell., Vol. 182, pp. 229-238, 2005.
[21]	Chen W., Wang Z., Xie H., Yu W., ‘‘Characterization of Surface EMG Signal Based on Fuzzy Entropy’’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 15, No. 2, pp. 266-272, 2007.
[22]	Chen Weiting, Zhuang Jun, Wangxin, Wang Zhizhong, ‘‘Measuring of Complexity Using FuzzyEn, ApEn, and SampEn’’, Medical Engineering & Physics, Vol. 30, pp. 61-68, 2009.
[23]	Xiaofeng Liu, Aimin Jiang, Ning Xu and Jianru Xue, ‘‘Increment Entropy as a Measure of Complexity for Time Series’’, Entropy, Vol. 18, No. 22; 2016. DOI: 10.3390/e18010022.
[24]	Suykens J. A., Vandewalle J.,‘‘Least Squares Support Vector Machine Classifiers’’, Neural Process. Lett, Vol. 9, pp. 293-300, 1999.
[25]	Suykens J. A., De Brabanter J., Vandewalle J., Van Gestel T.,‘‘Least Squares Support Vector Machines’’, World Scientific, Vol. 4, 2002.
[26]	Miller A. S., Blott B. H. and Hames T. K., “Review of Neural Network Applications in Medical Imaging and Signal Processing”, Medical and Biological Engineering and Computing, Vol. 30, pp. 449-464, 1992.
[27]	Fausett L., “Fundamentals of Neural Networks Architectures, Algorithms, and Applications”, Prentice Hall, Englewood Cliffs, NJ, 1994.
[28]	Umut Orhan, Mahmut Hekim, Mahmut Ozer, “EEG Signals Classification Using the K-Means Clustering and a Multilayer Perceptron Neural Network Model”, Expert Systems with Applications, Vol. 38, pp. 13475-13481 2011.
[29]	Atangana Romain, Tchiotsop Daniel, Kenne Godpromesse, Djoufack Nkengfack Laurent Chanel, ‘‘EEG Signal Classification Using LDA and MLP Classifier’’, Health Informatics - An International Journal (HIIJ), Vol. 9, No. 1, pp. 14-32, February 2020. DOI: 10.5121/hiij.2020.9102.
[30]	Zhu G., Li Y., Wen P. Paul, Wang S., Xi M., ‘‘Epileptogenic Focus Detection in Intracranial EEG Based on Delay Permutation Entropy’’, Conference Proceedings, American Institute of Physics, Vol. 1559, pp. 31-36, 2013.
[31]	Das A. B., Imamul M., Bhuiyan H., ‘‘Discrimination and Classification of NFC and FC EEG Signals Using Entropy-Based Features in the EMD-DWT Domain’’, Biomedical Signal Processing and Control, Vol. 29, pp. 11-21, 2016.
[32]	Vipin Gupta, Tanvi Priya, Abhishek Kumar Yadava, Ram Bilas Pachoria, U. Rajendra Acharya, ‘‘Automated Detection of Focal EEG Signals Using Features Extracted from Flexible Analytic Wavelet Transform’’, Pattern Recognition Letters, Vol., No., 2017. DOI: 10.1016/j.patrec.2017.03.017.
[33]	Rajeev Sharma, Varshney P., Ram Bilas Pachori, S. K. Vishvakarma, ‘‘Automated System for Epileptic EEG Detection Using Iterative Filtering’’, IEEE Sensors Letters, Vol. 2, No. 4, pp. 1-4, 2018.
[34]	Abhijit Bhattacharyya, Manish Sharma, Ram Bilas Pachori, Pradip Sircar, U. Rajendra Acharya, ‘‘A Novel Approach for Automated Detection of Focal EEG Using Empirical Wavelet Transform’’, Neural Computing and Applications, Vol. 29, No. 8, pp. 47-57, 2018. DOI: https://doi.org/10.1007/s00521-016-2646-4.
[35]	Gupta S., Krishna K. H., Pachori R. B., Tanveer M., ‘‘Fourier-Bessel Series Expansion Based Technique for Automated Classification of Focal and Non-Focal EEG Signals’’, 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 2018.
[36]	Subasi A., Jukic S., Kevric J., ‘‘Comparison of EMD, DWT and WPD for the Localization of Epileptogenic Foci Using Random Forest Classifier’’, Measurement, Vol. 146, pp. 846-855, 2019.
[37]	Dalal M., Tanveer M., Pachori R. B., ‘‘Automated Identification System for Focal EEG Signals Using Fractal Dimension of FAWT-Based Sub-bands Signals’’, Machine Intelligence and Signal Analysis; Springer: Singapore, Vol. 748, pp. 583-596, 2019.
[38]	Fasil O. K., Rajesh R., ‘‘Time-Domain Exponential Energy for Epileptic EEG Signal Classification’’, Neurosci. Lett., Vol. 694, pp. 1-8, 2019.
[39]	Chen Z., Lu G., Xie Z., Shang W., ‘‘A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis’’, IEEE Access, Vol. 8, pp. 20080-20092, 2020.
[40]	Jukic Samed, Saracevic Muzafer, Subasi Abdulhamit, Kevric Jasmin, ‘‘Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals’’, Mathematics, Vol. 8, No. 1481, 2020. DOI: 10.3390/math8091481.
[41]	Sharma Rahul, Pradip Sircar, Ram Bilas Pachori, ‘‘Automated Focal EEG Signal Detection Based on Third Order Cumulant Function’’, Biomedical Signal Processing and Control, Vol. 58, 2020. DOI: https://doi.org/10.1016/j.bspc.2020.101856.
[42]	Prasanna J., M. S. P. Subathra, Mazin Abed Mohammed, Mashael S. Maashi, Begonya Garcia-Zapirain, N. J. Sairamya, S. Thomas George, ‘‘Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network’’, Sensors, Vol. 20, No. 4952, 2020. DOI: 10.3390/s20174952.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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}
}

Copy | Download

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  -

Copy | Download

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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}
}

Copy | Download

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  -

Copy | Download