title: A Kalman filter based methodology for EEG spike enhancement.
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.
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: V P
authors.initials: VP
authors.lastname: Oikonomou
authors.forename: A T
authors.initials: AT
authors.lastname: Tzallas
authors.forename: D I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D004569:Electroencephalography; D004827:Epilepsy; D006115:Greece; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D015233:Models, Statistical