Researchers in Spain are developing a novel method for computing depth and rate of chest compressions during cardiopulmonary resuscitation (CPR) that relies solely on the spectral analysis of chest acceleration, according to a study1 published in Plos One. The investigational method could improve the effectiveness of CPR for out-of-hospital cardiac arrest (OHCA), the researchers reported.
“It is essential to perform CPR properly for the maneuver to be effective, and that is not easy even for highly trained personnel, since the chest has to be compressed at the appropriate frequency and depth (between 100-120 compressions per minute and between 5 and 6 cm),” explained author Digna María González-Otero.
The quality of the compressions is related to the patient’s survival. That is why the resuscitation guidelines recommend the use of feedback systems to monitor the quality of CPR in real time. “These devices are usually placed between the patient’s chest and the rescuer’s hands and guide the rescuer to help him/her achieve the target depth and frequency of the compression,” González-Otero explained. So, researchers in the UPV/EHU’s Signal and Communications Group have developed an algorithm to calculate the depth and frequency of the compressions on the basis of chest acceleration. “In other words, just by placing an accelerometer on the patient’s chest we can measure, in real time, the depth and frequency at which the compressions are being performed, and then correct the rescuer if necessary so that he/she performs quality CPR,” said González-Otero.
The algorithm was evaluated retrospectively with 75 OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every two seconds from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error.
The algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed two-second windows the error was below 3.5 mm and 3.1 cpm, respectively.
Based on results, Bexen Cardio (Ermua, Spain) is developing a device using this algorithm. “The device…is the screen of the defibrillator which tells the rescuer whether he/she has to press harder, work faster,” said González-Otero. The company is planning to mass market the device within a few months.