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Annals of Biomedical Engineering

, Volume 37, Issue 10, pp 2129–2145 | Cite as

Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures

  • Levin Kuhlmann
  • Anthony N. Burkitt
  • Mark J. Cook
  • Karen Fuller
  • David B. Grayden
  • Linda Seiderer
  • Iven M. Y. Mareels
Article

Abstract

This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector. On a testing set with the combination of their three features, Saab and Gotman reported a sensitivity of 0.78, a false positive rate of 0.86/h, and a median detection delay of 9.8 s. Based on 10-fold cross-validation the testing performance of our implementation of their method achieved a sensitivity of 0.79, a false positive rate of 0.62/h, and a median detection delay of 21.3 s. A detector based on an alternative combination of features achieved sensitivity of 0.81, a false positive rate of 0.60/h, and a median detection delay of 16.9 s. By including filtering techniques, it was possible to achieve performance levels similar to Saab and Gotman using our implementation of their method, although this involved increases in detection delays. Of the seizure detection measures investigated, relative average amplitude, relative power, relative derivative, and coefficent of variation of amplitude provided the best performing combinations. These better-performing features can be employed together to make robust and reliable seizure detectors.

Keywords

EEG Epilepsy Seizure detection Seizure onset 

Notes

Acknowledgments

This work was supported by an Australian Research Council Linkage Project Grant (LP0560684), The Bionic Ear Institute and St. Vincent’s Hospital Melbourne. We are grateful for the EEG data provided by the patients, and to the St. Vincent’s Hospital Melbourne Neurophysiology Clinic for collecting the data. EEG data collection was approved by the St. Vincent’s Hospital Melbourne Ethics Committee. We also thank Michael Eager for helping to format the manuscript.

Supplementary material

10439_2009_9755_MOESM1_ESM.pdf (694 kb)
PDF (693 KB)

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Copyright information

© Biomedical Engineering Society 2009

Authors and Affiliations

  • Levin Kuhlmann
    • 1
  • Anthony N. Burkitt
    • 1
    • 2
  • Mark J. Cook
    • 2
    • 3
  • Karen Fuller
    • 3
  • David B. Grayden
    • 1
    • 2
  • Linda Seiderer
    • 3
  • Iven M. Y. Mareels
    • 1
  1. 1.Department of Electrical and Electronic EngineeringThe University of MelbourneParkvilleAustralia
  2. 2.The Bionic Ear InstituteEast MelbourneAustralia
  3. 3.Department of NeurologySt. Vincent’s Hospital MelbourneFitzroyAustralia

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