CPR artifact removal from human ECG using optimal multichannel filtering

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be remov ed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial c ompressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-fr ee human ECGs with ventricular fibrillation (VF) and ventricular tachycardi a (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human EGG, where the weight factor was cho sen to provide the desired signal-to-noise ratio (SNR) Level. As artifact s ignals we used ECGs recorded from animals in asystole during precordial com pressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such r eference signals are available and, using an adaptive multichannel Wiener f ilter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the propo sed method is demonstrated through graphic examples, SNR, and rhythm classi fication evaluations.