An Improved Method for Classification of Epileptic EEG Signals based on Spectral Features using k-NN
|International Journal of Electronics and Communication Engineering|
|© 2015 by SSRG - IJECE Journal|
|Volume 2 Issue 7|
|Year of Publication : 2015|
|Authors : Anu V. S.and Paul Thomas|
How to Cite?
Anu V. S.and Paul Thomas, "An Improved Method for Classification of Epileptic EEG Signals based on Spectral Features using k-NN," SSRG International Journal of Electronics and Communication Engineering, vol. 2, no. 7, pp. 22-25, 2015. Crossref, https://doi.org/10.14445/23488549/IJECE-V2I7P108
In this paper, a method for classification of epileptic EEG signals based on k-NN classifier is discussed. This scheme provides an improved performance in terms of sensitivity, specificity and accuracy. EEG signal is decomposed into sub bands using multi-wavelet transform and spectral features such as mean spectral magnitude, spectral entropy, and spectral squared entropies are extracted. The distributions of these features for normal and epileptic EEG signal are clustered in different regions which can be utilized to yield good classification result. The results show that classification of these features using k-NN achieved an accuracy of 98.585%. In order to evaluate the efficiency of the proposed method, classification is also done using ANN and SVM which yielded an accuracy of 98.415% and 87.5% respectively
Epilepsy, Spectral Features, k-NN classifier, Artificial Neural Network, Support Vector Machine
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