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), 2024. Published by Science Publishing Group |
Discrete Wavelet Transforms (DWT), Accuracy, Electroencephalogram Signals (EEG), Multilayer Perceptron (MLP), Epileptic Seizure, Support Vector Machine (SVM)
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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
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
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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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}
}
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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