Automated inter-ctal epileptiform discharge detection from routine EEG

Duong Nhu, Mubeen Janmohamed, Lubna Shakhatreh, Ofer Gonen, Patrick Kwan, Amanda Gilligan, Chang Wei Tan, Levin Kuhlmann

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881-0.907) when trained on Alfred's dataset and tested on RMH's dataset, and 0.857 (95% CI, 0.847-0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

Original languageEnglish
Pages (from-to)65-71
Number of pages7
JournalStudies in Health Technology and Informatics
Publication statusPublished - 19 Apr 2021
EventDigital Health Institute Summit 2020 - Virtual, Online, Australia
Duration: 20 Nov 2020 → …


  • automation
  • deep learning
  • epilepsy
  • epileptiform discharges
  • Resnet

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