Yu Ming, Chen Feng, Zhang Guang, Li Liangzhe, Wang Chunchen, Wang Dan, Zhan Ningbo, Gu Biao, Wu Taihu
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Oct;33(5):834-41.
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.
心肺复苏（CPR）过程中胸部按压产生的伪迹严重影响可电击心律检测算法的可靠性。在本文中，我们提出了一种无需任何参考通道的自适应CPR伪迹消除算法。通过结合经验模态分解（EMD）和独立成分分析（ICA），可以从 corrupted ECG 信号中提取出干净的心电图（ECG）信号。为了评估所提算法的性能，构建了一个反向传播神经网络来实现可电击心律检测。分析中总共纳入了从猪身上采集的1484个 corrupted ECG 样本。实验结果表明，该方法将大大降低CPR伪迹的影响，从而提高可电击心律检测算法的准确性。
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