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PubMed2001

"Probability of successful defibrillation" as a monitor during CPR in out-of-hospital cardiac arrested patients.

The frequency spectrum of the ECG in ventricular fibrillation (VF) correlates with myocardial perfusion and might predict defibrillation success defined as return of spontaneous circulation (ROSC). The predictive power increases when more spectral variables are combined, but the complex information can be difficult to handle during the intensity of CPR. We therefore developed a method for expressing this multidimensional information in a single reproducible variable reflecting the probability of defibrillation success. This is based on the highest performing predictor for ROSC after 883 shocks given to 156 patients with VF. This was a combination of two decorrelated spectral features based on a principal component analysis of an original feature set with information on centroid frequency, peak power frequency, spectral flatness and energy. The function "Probability of defibrillation success" (P(ROSC)(v)) was developed by a 2-dimensional histogram technique. P(ROSC)(v) discriminated between shocks followed by ROSC and No-ROSC (P<0.0001). The present methodology indicates a possible way to develop a CPR monitor.

Cardiopulmonary Resuscitation↗

PubMed2000

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 removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation we started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, we added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals we used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, we construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.

Biomedical Engineering↗