Removal of CPR Artifacts From the Ventricular Fibrillation ECG by Adaptive Regression on Lagged Reference Signals

Application of Filtering Methods for Removal of Resuscitation Artifacts from Human ECG Signals

“…The Kalman filtering method is also noncausal and therefore has an advantage over the adaptive filtering method, however, it is based on a timeinvariant model, which is a limitation when the data is generated by a time-varying "true" system. For high SNR the best performance is achieved by the adaptive filtering method of [6], which suggests that the true model for v is indeed time-varying. …”

“…In general, different methods for artifacts removal produce different approximations v of the unknown signal v. The quality of approximation of v by v is evaluated in [6] by the signal-to-noise ratio (SNR) …”

“…The empirical results published in [6] do not compare the proposed method with other methods from the literature. We fulfill this gap in the literature by presenting a comparative study of the methods of [6] with classical filtering methods.…”

“…We fulfill this gap in the literature by presenting a comparative study of the methods of [6] with classical filtering methods. Section II described the filtering methods, used in the comparison.…”

“…Section III presents the performance of the methods on a database of separately recorded resuscitation artifacts and reference signals from pigs and ventricular fibrillation ECG signals from human. The same data is used in [6] for tuning and evaluation of an adaptive filtering method. …”