Rhythm discrimination during uninterrupted CPR using motion artifact reduction system

Due to motion artifact in the ECG caused by chest compressions automatic external defibrillators (AEDs) have difficulty recognizing ventricular fibrillation (VF) during cardiopulmonary resuscitation (CPR). Frequent interruption of CPR is required for artifact-free ECG interpretation, but these interruptions reduce the efficacy of CPR. We developed a motion artifact reduction system (MARS), based on adaptive noise cancellation techniques, for use during CPR. We hypothesized that this system would allow for automated rhythm discrimination during uninterrupted CPR.

Thirteen swine underwent CPR during normal sinus rhythm (NSR) and repeated inductions of VF and asystole, using an automated device that uses a load-distributing band to compress the anterior chest. A single ECG lead and the instantaneous compression force signal were sampled during continuous CPR and fed to MARS, which in turn provided a filtered ECG signal in which artifacts that correlated with compression force were suppressed. The filtered and unfiltered ECGs were then fed simultaneously, and in real time, to three pairs of defibrillators with rhythm discrimination functions. During CPR, non-shockable rhythms were correctly classified by the defibrillators in 59 of 63 instances using the raw ECG, and 60 of 63 instances using the MARS-filtered ECG (p=N.S.). During CPR, VF was correctly classified in 35 of 222 attempts using the raw ECG, and in 310 of 318 cases using the MARS-filtered ECG (p<0.001). As control, when CPR was not applied, all rhythms were correctly identified by each defibrillator using either the raw ECG or the filtered ECG.

Motion artifact reduction by adaptive noise cancellation allows for recognition of VF during uninterrupted automated CPR, while this is rarely possible based on the raw ECG. Incorporation of this signal processing strategy may obviate the need for interruptions in chest compression and thus enhance CPR efficacy.