title: CPR artifact removal in ventricular fibrillation ECG signals using Gabor multipliers.
abstract: OBJECTIVE
  We present an algorithm for discarding cardiopulmonary resuscitation (CPR) components from ventricular fibrillation ECG (VF ECG) signals and establish a method for comparing CPR attenuation on a common dataset. Removing motion artifacts in ECG allows for uninterrupted rhythm analysis and reduces "hands-off" time during resuscitation.
  
  
  RESULTS
  The current approach assumes a multichannel setting where the information of the corrupted ECG is combined with an additional pressure signal in order to estimate the motion artifacts. The underlying algorithm relies on a localized time-frequency transformation, the Gabor transform, that reveals the perturbation components, which, in turn, can be attenuated. The performance of the method is evaluated on a small set of test signals in the form of error analysis and compared to two well-established CPR removal algorithms that use an adaptive filtering system and a state-space model, respectively.
  
  
  CONCLUSIONS
  We primarily point out the potential of the algorithm for successful artifact removal; however, on account of the limited set of human VF and animal asystole CPR signals, we refrain from a statistical analysis of the efficiency of CPR attenuation. The results encourage further investigations in both the theoretical and the clinical setup.
authors.affiliation: Faculty of Mathematics, University of Vienna, A-1090 Vienna, Austria. tobias.werther@univie.ac.at
authors.forename: Tobias
authors.initials: T
authors.lastname: Werther
authors.forename: Andreas
authors.initials: A
authors.lastname: Klotz
authors.forename: Günther
authors.initials: G
authors.lastname: Kracher
authors.forename: Michael
authors.initials: M
authors.lastname: Baubin
authors.forename: Hans G
authors.initials: HG
authors.lastname: Feichtinger
authors.forename: Hermann
authors.initials: H
authors.lastname: Gilly
authors.forename: Anton
authors.initials: A
authors.lastname: Amann