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Performance Analysis of Extreme Learning Machine for Robust Classification of Epilepsy from EEG Signals
IJCA Proceedings on International Conference on Innovations in Information, Embedded and Communication Systems
© 2014 by IJCA Journal
ICIIECS - Number 1
Year of Publication: 2014
R Harikumar, C Ganeshbabu, M Balasubramani and G A Nivhedhitha. Article: Performance Analysis of Extreme Learning Machine for Robust Classification of Epilepsy from EEG Signals. IJCA Proceedings on International Conference on Innovations in Information, Embedded and Communication Systems ICIIECS(1):1-5, November 2014. Full text available. BibTeX
@article{key:article,
author = {R. Harikumar and C. Ganeshbabu and M. Balasubramani and G. A. Nivhedhitha},
title = {Article: Performance Analysis of Extreme Learning Machine for Robust Classification of Epilepsy from EEG Signals},
journal = {IJCA Proceedings on International Conference on Innovations in Information, Embedded and Communication Systems},
year = {2014},
volume = {ICIIECS},
number = {1},
pages = {1-5},
month = {November},
note = {Full text available}
}
Abstract
Epilepsy is a common brain disorder that affects one out of hundred patients. EEG (electroencephalogram) is a signal that represents that effect of the superimposition of diverse processes in the brain. This paper investigates the possibility of Extreme Learning Machine (ELM) as a classifier for detecting and classifies the epilepsy of various risk levels from the EEG signals. The Singular Value Decomposition (SVD) is used for dimensionality reduction. Twenty patients are analysed in this study.
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