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Detection of Interictal Discharges with Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG

Antoniades, Andreas, Spyrou, Loukianos, Martin-Lopez, David, Valentin, Antonio, Alarcon, Gonzalo, Sanei, Saeid and Cheong Took, Clive (2017) Detection of Interictal Discharges with Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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Abstract

Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the data.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Antoniades, Andreasaa00542@surrey.ac.ukUNSPECIFIED
Spyrou, LoukianosUNSPECIFIEDUNSPECIFIED
Martin-Lopez, DavidUNSPECIFIEDUNSPECIFIED
Valentin, AntonioUNSPECIFIEDUNSPECIFIED
Alarcon, GonzaloUNSPECIFIEDUNSPECIFIED
Sanei, SaeidS.Sanei@surrey.ac.ukUNSPECIFIED
Cheong Took, Clivec.cheongtook@surrey.ac.ukUNSPECIFIED
Date : 2017
Copyright Disclaimer : © 2017 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.
Uncontrolled Keywords : Convolutional neural networks; Epilepsy detection; Intracranial EEG; Multi score class learning
Depositing User : Clive Harris
Date Deposited : 23 Aug 2017 09:43
Last Modified : 23 Aug 2017 09:43
URI: http://epubs.surrey.ac.uk/id/eprint/842003

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