Volume 2, Issue 2, June 2018, Page: 36-41
EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers
Manisha Chandani, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Arun Kumar, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Received: Apr. 16, 2018;       Accepted: May 3, 2018;       Published: Jun. 2, 2018
DOI: 10.11648/j.ajist.20180202.12      View  744      Downloads  31
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.
Discrete Wavelet Transforms (DWT), Accuracy, Electroencephalogram Signals (EEG), Multilayer Perceptron (MLP), Epileptic Seizure, Support Vector Machine (SVM)
To cite this article
Manisha Chandani, Arun Kumar, EEG Signal Processing for Epileptic Seizure Prediction by Using MLPNN and SVM Classifiers, American Journal of Information Science and Technology. Vol. 2, No. 2, 2018, pp. 36-41. doi: 10.11648/j.ajist.20180202.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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