Deep learning for robust detection of interictal epileptiform discharges

David Geng, Ayham Alkhachroum, Manuel A. Melo Bicchi, Jonathan R. Jagid, Iahn Cajigas, Zhe Sage Chen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Objective. Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracranial electroencephalogram (iEEG) may facilitate online seizure monitoring and closed-loop neurostimulation. Approach. We developed a new deep learning approach, which employs a long short-term memory network architecture ('IEDnet') and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from iEEG recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances. Main results. IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets. Significance. IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.

Original languageEnglish (US)
Article number056015
JournalJournal of neural engineering
Issue number5
StatePublished - Oct 2021


  • auxiliary classifier generalized adversarial network (AC-GAN)
  • deep learning
  • gated recurrent unit (GRU)
  • interictal epileptiform discharge (IED)
  • long short-term memory (LSTM)
  • recurrent neural network (RNN)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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