Strong corruption of electrocardiograms caused by cardiopulmonary resuscitation reduces efficiency of two-channel methods for removing motion artefacts in non-shockable rhythms.

AIM: Cardiopulmonary resuscitation (CPR) artefact removal methods provide satisfactory results when the rhythm is shockable but fail on non-shockable rhythms. We investigated the influence of the corruption level on the performance of four different two-channel methods for CPR artefact removal. MATERIALS AND METHODS: 395 artefact-free ECGs and 13 pure CPR artefacts with corresponding blood pressure readings as a reference channel were selected. Using a simplified additive data model we generated CPR-corrupted signals at different signal-to-noise ratio (SNR) levels from -10 to +10 dB. The algorithms were optimized on learning data with respect to SNR improvement and then applied to testing data. Sensitivity and specificity were derived from the shock/no-shock advice of an automated external defibrillator before CPR corruption and after artefact removal. RESULTS: Sensitivity for the filtered data (>95%) was significantly superior to that for the unfiltered data (76%), p<0.001. However, specificity was similar for the filtered and unfiltered data (<90% vs 89.3%). For large artefacts (-10 dB) specificity decreased below 70%. No important difference in the performance of the four algorithms was found. CONCLUSION: Using a simplified data model we showed that, when the ECG rhythm is non-shockable, two-channel methods could not reduce CPR artefacts without affecting the rhythm analysis for shock recommendation. The reason could be poor reconstruction when the artefacts are large. However, poor reconstruction was not a hindrance to re-identifying shockable rhythms. Future investigations should both include the refinement of filter methods and also focus on reducing motion artefacts already at the recording stage.

CPR artifact removal in ventricular fibrillation ECG signals using Gabor multipliers.

BACKGROUND AND 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. METHODS AND 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. CONCLUSION: 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.