Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG

James R. Williamson, Daniel W. Bliss, David W. Browne

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

33 Scopus citations

Abstract

A patient-specific seizure prediction algorithm is proposed that extracts novel multivariate signal coherence features from ECoG recordings and classifies a patient's pre-seizure state. The algorithm uses space-delay correlation and covariance matrices at several delay scales to extract the spatiotemporal correlation structure from multichannel ECoG signals. Eigenspectra and amplitude features are extracted from the correlation and covariance matrices, followed by dimensionality reduction using principal components analysis, classification using a support vector machine, and temporal integration to produce a seizure prediction score. Evaluation on the Freiburg EEG database produced a sensitivity of 90.8% and false positive rate of.094.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages665-668
Number of pages4
DOIs
StatePublished - Aug 18 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • EEG Signal Processing
  • Epilepsy
  • Multivariate Features
  • Seizure Prediction
  • Support Vector Machines

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Williamson, J. R., Bliss, D. W., & Browne, D. W. (2011). Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 665-668). [5946491] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5946491