Automated epilepsy diagnosis using interictal scalp EEG

Forrest Sheng Bao, Jue Ming Gao, Jing Hu, Donald Y.C. Lie, Yuanlin Zhang, K. J. Oommen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.

Original languageEnglish
Title of host publicationProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEngineering the Future of Biomedicine, EMBC 2009
PublisherIEEE Computer Society
Pages6603-6607
Number of pages5
ISBN (Print)9781424432967
DOIs
StatePublished - 2009
Event31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 - Minneapolis, MN, United States
Duration: Sep 2 2009Sep 6 2009

Publication series

NameProceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009

Conference

Conference31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
CountryUnited States
CityMinneapolis, MN
Period09/2/0909/6/09

Keywords

  • Electroencephalogram (EEG)
  • Epilepsy
  • Probabilistic Neural Network (PNN)
  • Seizure

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  • Cite this

    Bao, F. S., Gao, J. M., Hu, J., Lie, D. Y. C., Zhang, Y., & Oommen, K. J. (2009). Automated epilepsy diagnosis using interictal scalp EEG. In Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (pp. 6603-6607). [5332550] (Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009). IEEE Computer Society. https://doi.org/10.1109/IEMBS.2009.5332550