EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers

Manisha Chandani; Arun Kumar

doi:doi:10.11648/j.ajist.20180202.12

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EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers

Manisha Chandani, Arun Kumar

Published in American Journal of Information Science and Technology (Volume 2, Issue 2)

Received: 16 April 2018 Accepted: 3 May 2018 Published: 2 June 2018

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Abstract

Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.

Published in	American Journal of Information Science and Technology (Volume 2, Issue 2)
DOI	10.11648/j.ajist.20180202.12
Page(s)	36-41
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), 2018. Published by Science Publishing Group

Keywords

Discrete Wavelet Transforms (DWT), Accuracy, Electroencephalogram Signals (EEG), Multilayer Perceptron (MLP), Epileptic Seizure, Support Vector Machine (SVM)

References

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[3]	Stein A. G, Eder H. G, Blum D. E, Drachev A, Fisher R. S. An automated drug delivery system for focal epilepsy. Epilepsy research 2000; 39: 103-114.
[4]	Osorio I, Frei M. G. Real-time detection, quantification, warning, and control of epileptic seizures: The foundations for a scientific epileptology. Epilepsy &Behavior 2009; 16: 391-396.
[5]	Mormann F, Kreuz T, Andrzejak R. G, David P, Lehnertz K, Elger C. E. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy research 2003; 53: 173-185.
[6]	Iasemidis L. D. Epileptic seizure prediction and control. In: IEEE 2003 Biomedical Engineering; pp. 549-558.
[7]	Tong S, Thakor N. V. Quantitative EEG analysis methods and clinical applications. Artech House, 2009.
[8]	Deburchgraeve W, Cherian P. J, De Vos M, Swarte R. M, Blok J. H, Visser G. H, Van Huffel S. Automated neonatal seizure detection mimicking a human observer reading EEG. Clinical Neurophysiology 2008; 119: 2447-2454.
[9]	Andrzejak R. G, Lehnertz K, Mormann F, Rieke C, David P, Elger C. E. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 2001; 64: 061907.
[10]	Kumari, Pinki, and AbhishekVaish. “Brainwave based user identification system: A pilot study in robotics environment.” Robotics and Autonomous Systems 65 (2015): 15-23.
[11]	Durka P. J. Adaptive time-frequency parametrization of epileptic spikes. Physical Review E 2004; 69:051914.
[12]	Kumari, Pinki, and AbhishekVaish. “Feature-level fusion of mental task’s brain signal for an efficient identification system.” Neural Computing and Applications: 1-11.
[13]	C. S. Burrus, R. A. Gopinath, & H. Guo (1998). Introduction to wavelets and wavelet transforms: A primer. Prentice-Hall, Upper Saddle River, NJ.
[14]	Mandeep Singh & Sunpreet Kaur (2012). Epilepsy, Frequency Band Separation for Epilepsy Detection Using EEG, International Journal of Information Technology & Knowledge Management, Vol 6, No. 1.
[15]	Claude Roberta, Jean-Franc¸ois Gaudyb & Aime´ Limogea (2002) “Electroencephalogram processing using neural networks”, Clinical Neurophysiology 113, pp. 694–701.
[16]	S. Theodoridis, and K. Koutroumbas. Pattern Recognition. 4th Ed., Elsevier - Academic Press, 2009.
[17]	P. S. Sastry. “An introduction to Support Vector Machines”. Chapter in J. C. Misra (Ed), computing and information sciences: Recent Trends. Narosa Publishing House, New Delhi 2003.
[18]	Alireza Baratloo, Mostafa Hosseini, Ahmmed Negida & Gehad El Ashal (2015). Simple Definition and Calculation of Accuracy, Sensitivity and Specificity. Volume 4 No. 2, pp. 48–49.

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APA Style

Manisha Chandani, Arun Kumar. (2018). EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. American Journal of Information Science and Technology, 2(2), 36-41. https://doi.org/10.11648/j.ajist.20180202.12

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ACS Style

Manisha Chandani; Arun Kumar. EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. Am. J. Inf. Sci. Technol. 2018, 2(2), 36-41. doi: 10.11648/j.ajist.20180202.12

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AMA Style

Manisha Chandani, Arun Kumar. EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. Am J Inf Sci Technol. 2018;2(2):36-41. doi: 10.11648/j.ajist.20180202.12

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@article{10.11648/j.ajist.20180202.12,
  author = {Manisha Chandani and Arun Kumar},
  title = {EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers},
  journal = {American Journal of Information Science and Technology},
  volume = {2},
  number = {2},
  pages = {36-41},
  doi = {10.11648/j.ajist.20180202.12},
  url = {https://doi.org/10.11648/j.ajist.20180202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20180202.12},
  abstract = {Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.},
 year = {2018}
}

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TY  - JOUR
T1  - EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers
AU  - Manisha Chandani
AU  - Arun Kumar
Y1  - 2018/06/02
PY  - 2018
N1  - https://doi.org/10.11648/j.ajist.20180202.12
DO  - 10.11648/j.ajist.20180202.12
T2  - American Journal of Information Science and Technology
JF  - American Journal of Information Science and Technology
JO  - American Journal of Information Science and Technology
SP  - 36
EP  - 41
PB  - Science Publishing Group
SN  - 2640-0588
UR  - https://doi.org/10.11648/j.ajist.20180202.12
AB  - Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.
VL  - 2
IS  - 2
ER  -

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Author Information

Manisha Chandani

Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Arun Kumar

Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India

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APA Style

Manisha Chandani, Arun Kumar. (2018). EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. American Journal of Information Science and Technology, 2(2), 36-41. https://doi.org/10.11648/j.ajist.20180202.12

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ACS Style

Manisha Chandani; Arun Kumar. EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. Am. J. Inf. Sci. Technol. 2018, 2(2), 36-41. doi: 10.11648/j.ajist.20180202.12

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AMA Style

Manisha Chandani, Arun Kumar. EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers. Am J Inf Sci Technol. 2018;2(2):36-41. doi: 10.11648/j.ajist.20180202.12

Copy | Download

@article{10.11648/j.ajist.20180202.12,
  author = {Manisha Chandani and Arun Kumar},
  title = {EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers},
  journal = {American Journal of Information Science and Technology},
  volume = {2},
  number = {2},
  pages = {36-41},
  doi = {10.11648/j.ajist.20180202.12},
  url = {https://doi.org/10.11648/j.ajist.20180202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajist.20180202.12},
  abstract = {Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers
AU  - Manisha Chandani
AU  - Arun Kumar
Y1  - 2018/06/02
PY  - 2018
N1  - https://doi.org/10.11648/j.ajist.20180202.12
DO  - 10.11648/j.ajist.20180202.12
T2  - American Journal of Information Science and Technology
JF  - American Journal of Information Science and Technology
JO  - American Journal of Information Science and Technology
SP  - 36
EP  - 41
PB  - Science Publishing Group
SN  - 2640-0588
UR  - https://doi.org/10.11648/j.ajist.20180202.12
AB  - Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the different combinations of two-class epilepsy detection is studied using Support Vector Machine (SVM) and neural network analysis (NNA) classifiers for the derived statistical features from DWT. In this new approach first parse EEG signals to sub-bands in different categories with the help of discrete wavelet transform (DWT) and then we derive statistical features such as Mean, Median, Standard Deviation, Kurtosis, Entropy, Skewness for each sub-band. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM) and neural network analysis (NNA). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and 99% by SVM it has been found that the computation time of NNA classifier is lesser than SVM to provide 100% accuracy.
VL  - 2
IS  - 2
ER  -

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