Classification of Epileptic & Non Epileptic EEG Signal Using Matlab

Authors(2) :-Sanat Kumar, Dinesh Kumar Atal

Epilepsy is a typical incessant neurological issue. Epilepsy seizures are the consequence of the transient and surprising electrical aggravation of the cerebrum. Around 50 million individuals worldwide have epilepsy, and about two out of each three new cases are found in creating nations. Epilepsy will probably happen in youthful youngsters or individuals beyond 65 years old years; nonetheless, it can happen at any age. The identification of epilepsy is conceivable by investigating EEG signals. In this paper we are using a technique to classify normal & epileptic EEG signal using k-means clustering algorithm in MATLAB. Further the SVM & Discriminant classifier in MATLAB Machine learning toolbox is used to classify the epileptic and normal EEG signal & wavelet transform is used to process the EEG signal. After the implementation of the signals there is ~70% accuracy with SVM classifier and ~93% accuracy with discriminant classifier.

Authors and Affiliations

Sanat Kumar
Department of Biomedical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonipat, Haryana, India
Dinesh Kumar Atal
Department of Biomedical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonipat, Haryana, India

Epilepsy seizures, SVM Classifier, MATLAB, k-means clustering, wavelet transform, Discriminant Classifier.

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 203-207
Manuscript Number : IJSRST162449
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Sanat Kumar, Dinesh Kumar Atal, " Classification of Epileptic & Non Epileptic EEG Signal Using Matlab ", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 4, pp.203-207 , July-August-2016.
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