Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG

Antoniades, A, Spyrou, L, Martin-Lopez, D, Valentin, A, Alarcon, G, Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801 and Cheong Took, C, 2017. Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (12), pp. 2285-2294. ISSN 1534-4320

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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:	Journal article
Publication Title:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Creators:	Antoniades, A., Spyrou, L., Martin-Lopez, D., Valentin, A., Alarcon, G., Sanei, S. and Cheong Took, C.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	December 2017
Volume:	25
Number:	12
ISSN:	1534-4320
Identifiers:	Number Type 10.1109/TNSRE.2017.2755770 DOI 28952945 PubMed ID
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	31 Jan 2018 10:07
Last Modified:	01 May 2018 15:36
URI:	https://irep.ntu.ac.uk/id/eprint/32588

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