Feature extraction of linear predictors at spectral bands of interest

Stathis S. Leondopulos, Wanpracha A. Chaovalitwongse, Evangelia Micheli-Tzanakou, Stephen Wong, Brenda Y. Wu

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

Abstract

Intra-cranial electroencephalograms (EEG) from two patients diagnosed with epilepsy are sampled at 1kHz, enabling analysis and feature extraction at frequency bands above the gamma range. This study focuses on the extraction of linear features (including autoregressive, autoregressive-moving average and Fourier coefficients) obtained at both low (below 100Hz) and high (100-500Hz) bands of the signal spectrum. Comparisons of the performance of each feature are made based on a binary hypothesis test of statistical distributions from inter-ictal and pre-ictal epochs. Results are obtained from pre-ictal time periods as assessed by an expert epileptologist.

Original languageEnglish (US)
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
Pages2612-2616
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

Other

Other31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Country/TerritoryUnited States
CityMinneapolis, MN
Period9/2/099/6/09

All Science Journal Classification (ASJC) codes

  • Cell Biology
  • Developmental Biology
  • Biomedical Engineering
  • General Medicine

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