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Deep learning for epileptic intracranial EEG data

Antoniades, Andreas, Spyrou, L, Cheong Took, Clive and Sanei, Saeid (2016) Deep learning for epileptic intracranial EEG data In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 13-16 Sep 2016, Vietri sul Mare, Salerno, Italy.

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Abstract

Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. In this paper, we consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically, we consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state of the art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences between the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in general

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Antoniades, Andreasaa00542@surrey.ac.uk
Spyrou, L
Cheong Took, Clivec.cheongtook@surrey.ac.uk
Sanei, SaeidS.Sanei@surrey.ac.uk
Editors :
NameEmailORCID
Palmieri, Francesco A. N.
Uncini, Aurelio
Diamantaras, Kostas
Larsen, Jan
Date : 10 November 2016
Identification Number : 10.1109/MLSP.2016.7738824
Copyright Disclaimer : Copyright ©2016 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved. Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For reprint or republication permission, email to IEEE Copyrights Manager at pubs-permissions@ieee.org. All rights reserved. Copyright ©2016 by IEEE.
Uncontrolled Keywords : EEG; Deep Learning; Epilepsy; Convolutional Neural Networks
Related URLs :
Additional Information : Print on Demand(PoD) ISBN: 978-1-5090-0747-9
Depositing User : Symplectic Elements
Date Deposited : 01 Mar 2017 17:51
Last Modified : 05 Feb 2018 10:50
URI: http://epubs.surrey.ac.uk/id/eprint/813465

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