title: Removal of CPR artifacts from the ventricular fibrillation ECG by adaptive regression on lagged reference signals.
abstract: OBJECTIVE
  Removing cardiopulmonary resuscitation (CPR)-related artifacts from human ventricular fibrillation (VF) electrocardiogram (ECG) signals provides the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This could reduce "hands-off" analysis times which diminish the cardiac perfusion and deteriorate the chance for successful defibrillations.
  
  
  RESULTS
  Our approach consists in estimating the CPR part of a corrupted signal by adaptive regression on lagged copies of a reference signal which correlate with the CPR artifact signal. The algorithm is based on a state-space model and the corresponding Kalman recursions. It allows for stochastically changing regression coefficients. The residuals of the Kalman estimation can be identified with the CPR-filtered ECG signal. In comparison with ordinary least-squares regression, the proposed algorithm shows, for low signal-to-noise ratio (SNR) corrupted signals, better SNR improvements and yields better estimates of the mean frequency and mean amplitude of the true VF ECG signal.
  
  
  CONCLUSIONS
  The preliminary results from a small pool of human VF and animal asystole CPR data are slightly better than the results of comparable previous studies which, however, not only used different algorithms but also different data pools. The algorithm carries the possibility of further optimization.
authors.affiliation: Research Center Process and Product Engineering, University of Applied Sciences Vorarlberg, Dornbirn 6850, Austria. klaus.rheinberger@fhv.at
authors.forename: Klaus
authors.initials: K
authors.lastname: Rheinberger
authors.forename: Thomas
authors.initials: T
authors.lastname: Steinberger
authors.forename: Karl
authors.initials: K
authors.lastname: Unterkofler
authors.forename: Michael
authors.initials: M
authors.lastname: Baubin
authors.forename: Andreas
authors.initials: A
authors.lastname: Klotz
authors.forename: Anton
authors.initials: A
authors.lastname: Amann
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016887:Cardiopulmonary Resuscitation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D012016:Reference Values; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000503:physiopathology / Q000517:prevention & control*