Search

Objectives To evaluate the performance of a real-time feedback algorithm for chest compression (CC) during cardiopulmonary resuscitation (CPR), which provides accurate estimation of the CC depth based on dual accelerometer signal processing, without assuming full CDC. Also, to explore the influence of incomplete chest decompression (CDC) on the CC depth estimation performance. Methods The performance of a real-time feedback algorithm for CC during CPR was evaluated by comparison with an offline algorithm using adult CPR manikin CC data obtained under various conditions. Results The real-time algorithm, using non-causal baselining, delivered comparable CC depth estimation accuracy to the offline algorithm on both soft and hard back support surfaces. In addition, for both algorithms incomplete CDC led to underestimation of the CC depth. Conclusions CPR feedback systems which utilize an assumption of full CDC may be unreliable especially in long duration CPR events where rescuer fatigue can strongly influence CC quality. In addition, these systems may increase the risk of thoracic and abdominal injury during CPR since rescuers may apply excessive compression forces due to underestimation of the CC depth when incomplete CDC occurs. Hence, there is a strong need for CPR feedback systems to accurately measure CDC in order to improve their clinical effectiveness.

The quality of cardiopulmonary resuscitation (CPR) is often inconsistent and frequently fails to meet recommended guidelines. One promising approach to address this problem is for clinicians to use an active feedback device during CPR. However, one major deficiency of existing feedback systems is that they fail to account for the displacement of the back support surface during chest compression (CC), which can be important when CPR is performed on a soft surface. In this study we present the development of a real-time CPR feedback system based on an algorithm which uses force and dual-accelerometer measurements to provide accurate estimation of the CC depth on a soft surface, without assuming full chest decompression. Based on adult CPR manikin tests it was found that the accuracy of the estimated CC depth for a dual accelerometer feedback system is significantly better (7.3% vs. 24.4%) than for a single accelerometer system on soft back support surfaces, in the absence or presence of a backboard. In conclusion, the algorithm used was found to be suitable for a real-time, dual accelerometer CPR feedback application since it yielded reasonable accuracy in terms of CC depth estimation, even when used on a soft back support surface.

Optimizing chest compression (CC) performance during neonatal cardiopulmonary resuscitation (CPR) is critical to improving survival outcomes since current clinical protocols often achieve only a fraction of the native cardiovascular perfusion. This study presents the development of a diagnostic tool to unobtrusively measure the CC depth and force during neonatal CPR using sensors mounted on a glove platform. The performance of the glove was evaluated by infant manikin tests using the two-thumb (TT) and two-finger (TF) methods of CC during simulated, unventilated neonatal CPR. The TT method yielded maximum CC depths and forces of as much as 25.7 ± 3.2 mm and 35.9 ± 2.2 N while the TF method produced CC depths and forces of as much as 21.6 ± 2.2 mm and 23.7 ± 2.9 N. These results are consistent with clinical findings which suggest that TT compression is more effective than TF compression since it produces greater CC depths and forces.