Development of a real-time feedback algorithm for chest compression during CPR without assuming full chest decompression

摘要

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.