A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions.

Cardiopulmonary resuscitation (CPR) artifacts caused by chest compressions and ventilations interfere with the rhythm diagnosis of automated external defibrillators (AED). CPR must be interrupted for a reliable diagnosis. However, pauses in chest compressions compromise the defibrillation success rate and reduce perfusion of vital organs. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. We have estimated the CPR artifact using only the frequency of the compressions as additional information to model it. Our model of the artifact is adaptively estimated using a least mean-square (LMS) filter. It was tested on 89 shockable and 292 nonshockable ECG samples from real out-of-hospital sudden cardiac arrest episodes. We evaluated the results using the shock advice algorithm of a commercial AED. The sensitivity and specificity were above 95% and 85%, respectively, for a wide range of working conditions of the LMS filter. Our results show that the CPR artifact can be accurately modeled using only the frequency of the compressions. These can be easily registered after small changes in the hardware of the CPR compression pads.

Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artefacts.

Providing cardiopulmonary resuscitation (CPR) to a patient in cardiac arrest introduces artefacts into the electrocardiogram (ECG), corrupting the diagnosis of the underlying heart rhythm. CPR must therefore be discontinued for reliable shock advice analysis by an automated external defibrillator (AED). Detection of ventricular fibrillation (VF) during CPR would enable CPR to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. This study presents a new adaptive filtering method to clean the ECG. The approach consists of a filter that adapts its characteristics to the spectral content of the signal exclusively using the surface ECG that commercial AEDs capture through standard patches. A set of 200 VF and 25 CPR artefact samples collected from real out-of-hospital interventions were used to test the method. The performance of a shock advice algorithm was evaluated before and after artefact removal. CPR artefacts were added to the ECG signals and four degrees of corruption were tested. Mean sensitivities of 97.83%, 98.27%, 98.32% and 98.02% were achieved, producing sensitivity increases of 28.44%, 49.75%, 59.10% and 64.25%, respectively, sufficient for ECG analysis during CPR. Although satisfactory and encouraging sensitivity values have been obtained, further clinical and experimental investigation is required in order to integrate this type of artefact suppressing algorithm in current AEDs.