Additive Model to Evaluate the Accuracy of Chest Compression Feedback Systems in Moving Vehicles
2016 Computing in Cardiology Conference (CinC)
Quality of cardiopulmonary resuscitation (CPR) affects survival from cardiac arrest. Feedback devices that monitor chest compression rate and depth can be used to guide the rescuer. Many analyze chest acceleration, and could be inaccurate when used in moving vehicles. Our aim was to propose an additive model to evaluate the accuracy of accelerometer-based feedback devices in moving vehicles and to apply it to the case of a plane. Volunteers provided chest compressions to a resuscitation manikin
... suscitation manikin in static conditions with an accelerometer placed beneath their hands. Dynamic noise was measured during the plane trips Bilbao-Munich and Frankfurt-Bilbao. The acceleration that would have been measured by a feedback device used in a plane was modeled as the sum of the acceleration measured in static conditions and the dynamic noise. Compression depth and rate were estimated from the acceleration both in static conditions and when adding dynamic noise. In static conditions, median (IQR) unsigned error in depth and rate estimation were 1.4 (0.6, 2.3) mm and 0.9 (0.4, 1.5) cpm, respectively. When adding dynamic noise of the plane, errors were 1.6 (0.7 ,2.9) mm and 0.9 (0.4, 1.5) cpm. The additive model simplifies the evaluation of the accuracy of CPR feedback devices in moving vehicles. In the evaluated conditions, the algorithm was accurate.