A Machine Learning Shock Decision Algorithm for use during Piston-driven Chest Compressions
Cardiopulmonary resuscitation (CPR) therapy provides oxygen to the vital organs during cardiac arrest. An accurate heart rhythm analysis during piston-driven mechanical chest compressions would avoid interruptions in CPR therapy. We developed a rhythm analysis algorithm that combines adaptive filtering to remove compression artifacts from the electrocardiogram, multiresolution stationary wavelet transform (SWT) analysis for feature extraction, and a gaussian support vector machine (SVM) classifier for the shock/no-shock decision. Our results show that the heart rhythm can be accurately diagnosed during mechanical compressions, avoiding interruptions in CPR that compromise perfusion of the vital organs.