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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: IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016-09-13 - 2016-09-16, Vietri sul Mare, Salerno, Italy.

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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 :
Cheong Took,
Date : 10 November 2016
Identification Number : 10.1109/MLSP.2016.7738824
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User : Symplectic Elements
Date Deposited : 01 Mar 2017 17:51
Last Modified : 05 Jul 2017 11:47

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