In this study, a closed-loop controller for chest compression which adjusts chest compression depth according to the coronary perfusion pressure (CPP) was proposed. An effective and personalized chest compression method for automatic mechanical compression devices was provided, and the traditional and uniform chest compression standard neglecting individual difference was improved. This study rebuilds Charles F. Babbs human circulation model with CPP simulation module and proposes a closed-loop controller based on a fuzzy control algorithm. The performance of the fuzzy controller was evaluated and compared to that of a traditional PID controller in computer simulation studies. The simulation results demonstrated that the fuzzy closed-loop controller produced shorter regulation time, fewer oscillations and smaller overshoot than those of the traditional PID controller and outperforms the traditional PID controller in CPP regulation and maintenance.
Artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) seriously affect the reliability of shockable rhythm detection algorithms. In this paper, we proposed an adaptive CPR artifacts elimination algorithm without needing any reference channels. The clean electrocardiogram (ECG) signals can be extracted from the corrupted ECG signals by incorporating empirical mode decomposition (EMD) and independent component analysis (ICA). For evaluating the performance of the proposed algorithm, a back propagation neural network was constructed to implement the shockable rhythm detection. A total of 1 484 corrupted ECG samples collected from pigs were included in the analysis. The results of the experiments indicated that this method would greatly reduce the effects of the CPR artifacts and thereby increase the accuracy of the shockable rhythm detection algorithm.