This paper introduces the development and animal tests of a miniaturized electrical chest compression device. Based on pulse width modulation technology produced by micro control unit, the device can control the frequency and depth of the compression accurately, as well as perform real-time adjustment. Therefore, it can perform continuous and stable chest compression for long time, which may increase the successful rate of cardiopulmonary resuscitation (CPR). Besides, the device can also produce different types of compression waveforms, including trapezoidal and triangular waveforms. Then, the performance and efficacy of the device was assessed with a rat model of asphyxial cardiac arrest (CA).
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.