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

Research output: Contribution to journalArticle

Andreas Antoniades, Loukianos Spyrou, David Martin Lopez, Antonio Valentin, Gonzalo Alarcon, Saeid Sanei, Clive Cheong Took

Original languageEnglish
JournalIEEE transactions on neural systems and rehabilitation engineering
Early online date22 Sep 2017
DOIs
Accepted/In press21 Sep 2017
E-pub ahead of print22 Sep 2017
PublishedDec 2017

King's Authors

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.

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