Automated detection of postictal generalized EEG suppression

Wanchat Theeranaew, James McDonald, Bilal Zonjy, Farhad Kaffashi, Brian D. Moseley, Daniel Friedman, Elson So, James Tao, Maromi Nei, Philippe Ryvlin, Rainer Surges, Roland Thijs, Stephan Schuele, Samden Lhatoo, Kenneth A. Loparo

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.

Original languageEnglish (US)
Article number8100890
Pages (from-to)371-377
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume65
Issue number2
DOIs
StatePublished - Feb 2018

Keywords

  • Boosting algorithm
  • SUDEP
  • epilepsy
  • post-ictal generalized EEG suppression (PGES)

ASJC Scopus subject areas

  • Biomedical Engineering

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  • Cite this

    Theeranaew, W., McDonald, J., Zonjy, B., Kaffashi, F., Moseley, B. D., Friedman, D., So, E., Tao, J., Nei, M., Ryvlin, P., Surges, R., Thijs, R., Schuele, S., Lhatoo, S., & Loparo, K. A. (2018). Automated detection of postictal generalized EEG suppression. IEEE Transactions on Biomedical Engineering, 65(2), 371-377. [8100890]. https://doi.org/10.1109/TBME.2017.2771468