A Kalman filter based methodology for EEG spike enhancement

  • Authors:
  • V. P. Oikonomou;A. T. Tzallas;D. I. Fotiadis

  • Affiliations:
  • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Medical Physics, Medical School, ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Biomedical Research Institute-FORTH, GR 45110 ...

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2007

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Abstract

In this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives.