Monitoring chest compression quality during cardiopulmonary resuscitation: Proof-of-concept of a single accelerometer-based feedback algorithm

Abstract

BackgroundThe use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases.Materials and methodsThe algorithm was evaluated retrospectively with seventy five 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 2-s 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.ResultsThe algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively.ConclusionsThe CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.

The TrueCPR device in the process of teaching cardiopulmonary resuscitation

“…They are still being improved, and new methods for assessing chest compression depth include an algorithm for spectral analysis of chest acceleration. [9]…”

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

“…In contrast to the CCD prediction results here, Lu et al [32] used the accelerometer data to fit a polynomial model to predict the CCD with high accuracy (about 0.03 ± 0.5 cm within 95%-CI). González-Otero et al [33] report an CCD prediction error below 0.35 cm in 95% of 2-s-windows of accelerometer data using the peak-to-peak value of the reconstructed compression signal.…”

The Iterative Development and Testing of an Interactive Mobile Application for Skill Retention of Cardiopulmonary Resuscitation Among High School Students: A Pilot Study

“…With the app, users mimic CPR compressions on a pillow to simulate conducting CPR on a victim in one-minute intervals. Using the three-axis accelerometer feature (González et al, 2018) on the iPhone, an algorithm was created to calculate average CPR compressions rate. Background music that was 100-120 beats per minute (bpm) and a color-coded feedback mechanism allowed users to practice CPR compressions and obtain performance feedback.…”