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Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algorithm and convolutional neural network (CNN) for the detection of intracranial IEDs ("AiED"). 1000 intracranial electroencephalogram (EEG) epochs extracted randomly from 307 subjects with refractory epilepsy were annotated independently by two expert neurophysiologists. These annotated epochs were divided into 1062 two-second epochs with IEDs and 1428 two-second epochs without IEDs, which were transformed into spectrograms prior to training the neural network. The highest performing network was validated on an annotated external test set. The final network had an F1-score of 0.95 (95% CI: 0.91-0.98) and an average Area Under the Receiver Operating Characteristic of 0.98 (95% CI: 0.96-1.00). For the external test set, it showed an overall F1-score of 0.71, correctly identifying 100% of all high-amplitude IED complexes, 96.23% of all high-amplitude isolated IEDs, and 66.15% of all IEDs of atypical morphology. Template-matching combined with a CNN offers a fast, robust method for detecting intracranial IEDs. "AiED" is generalizable and achieves comparable performance to human reviewers; it may support clinical and research EEG analyses. Copyright © 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.


Robert J Quon, Stephen Meisenhelter, Edward J Camp, Markus E Testorf, Yinchen Song, Qingyuan Song, George W Culler, Payam Moein, Barbara C Jobst. AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2022 Jan;133:1-8

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PMID: 34773796

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