Epileptiform spike detection via convolutional neural networks

Alexander Rosenberg Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Publication date2016
Pages754-758
ISBN (Print)978-1-4799-9988-0
DOIs
Publication statusPublished - 2016
Event41st IEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
Conference number: 41

Conference

Conference41st IEEE International Conference on Acoustics, Speech, and Signal Processing
Number41
CountryChina
CityShanghai
Period20/03/201625/03/2016
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Epilepsy
  • Spike detection
  • EEG
  • Deep learning
  • Convolutional neural network

Cite this

Johansen, A. R., Jin, J., Maszczyk, T., Dauwels, J., Cash, S. S., & Westover, M. B. (2016). Epileptiform spike detection via convolutional neural networks. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 754-758). IEEE. I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings https://doi.org/10.1109/ICASSP.2016.7471776