Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

This study is focused on the removal of artifacts due to Cardio Pulmonary Resuscitation (CPR) on Ventricular Fibrillation ECG signals. The aim is to allow a reliable analysis of the cardiac rhythm by an AED or the defibrillation success analysis during CPR episodes. The research is based on a human model for the CPR artifact and the VF ECG signals. The test signals were generated adding the CPR artifact (noise) to the VF (signal), with a known Signal-to-Noise Ratio (SNR). The results of the adaptive Kalman filtering have been obtained according to three different levels: SNR improvement; Sensitivity improvement in the AED algorithm for the detection of shockable rhythm; and Variations of the significant frequencies, compared to the values obtained with the original VF signals. In all cases, remarkable results have been achieved regarding to the efficiency in the artifact removal. 1

CPR artefact removal from VF signals by means of an adaptive Kalman filter using the chest compression frequency as reference signal

Chest compressions during Cardiopulmonary Resuscitation (CPR) generate important artefacts which impede the correct analysis of the ECG signal by an automated external defibrillator (AED). The suppression of the CPR artefact is thus necessary during resuscitation. The filtering technique proposed in the present paper is based on a Kalman adaptive scheme where the CPR artefact model follows the instantaneous frequency of the chest compressions, while the VF signal is modeled as a sum of two sinusoidal signals. The good results obtained for the increase of the signal to noise ratio and the excellent sensitivity scores provided by a commercial AED on the cleaned VF signals certifies the value of the method. 1