A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE

Yuedong Song, Sarita Azad, Pietro Lio

Abstract

In this paper, we investigate the potential of discrete wavelet transform (DWT), together with a recentlydeveloped machine learning algorithm referred to as Extreme Learning Machine (ELM), to the task of classifying EEG signals and detecting epileptic seizures. EEG signals are decomposed into frequency sub-bands using DWT, and then these sub-bands are passed to an ELM classifier. A comparative study on system performance is conducted between ELM and back-propagation neural networks (BPNN). Results show that the ELM classifier not only achieves better classification accuracy, but also needs much less learning time compared to the BPNN classifier. It is also found that the length of the EEG segment used affects the prediction performance of classifiers.

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Paper Citation


in Harvard Style

Song Y., Azad S. and Lio P. (2010). A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 436-441. DOI: 10.5220/0002745904360441


in Bibtex Style

@conference{biosignals10,
author={Yuedong Song and Sarita Azad and Pietro Lio},
title={A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={436-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002745904360441},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - A NEW APPROACH FOR EPILEPTIC SEIZURE DETECTION USING EXTREME LEARNING MACHINE
SN - 978-989-674-018-4
AU - Song Y.
AU - Azad S.
AU - Lio P.
PY - 2010
SP - 436
EP - 441
DO - 10.5220/0002745904360441