ECG Rhythm Analysis During Manual Chest Compressions Using an Artefact Removal Filter and Random Forest Classifiers

摘要

Interruptions in cardiopulmonary resuscitation (CPR) decrease the chances of survival. However, CPR must be interrupted for a reliable rhythm analysis because chest compressions (CCs) induce artifacts in the ECG. This paper introduces a double-stage shock advice algorithm (SAA) for a reliable rhythm analysis during manual CCs. The method used two configurations of the recursive least-squares (RLS) filter to remove CC artifacts from the ECG. For each filtered ECG segment over 200 shocko-shock decision features were computed and fed into a random forest (RF) classifier to select the most discriminative 25 features. The proposed SAA is an ensemble of two RF classifiers which were trained using the 25 features derived from different filter configurations. Then, the average value of class posterior probabilities was used to make a final shocko-shock decision. The dataset was comprised of 506 shockable and 1697 non-shockable rhythms which were labelled by expert rhythm resuscitation reviewers in artifact-free intervals. Shocko-shock diagnoses obtained through the proposed double-stage SAA were compared with the rhythm annotations to obtain the Sensitivity (Se), Specificity (Sp) and balanced accuracy (BAC) of the method. The results were 93.5%, 96.5% and 95.0%, respectively.