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Estimation of the variations in mechanical impedance between the actuator and the chest, and the power delivered to the chest during cardiopulmonary resuscitation using machine-embedded sensors

机译:使用机器内置传感器估算执行器和胸部之间机械阻抗的变化以及心肺复苏过程中传递给胸部的功率

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摘要

To reduce the risk of patient damage and complications during the cardiopulmonary resuscitation (CPR) process in emergency situations, it is necessary to monitor the status of the patient and the quality of CPR while CPR processing without additional bio-signal measurement devices. In this study, an algorithm is proposed to estimate the mechanical impedance (MI) between an actuator of the CPR machine and the chest of the patient, and to estimate the power delivered to the chest of the patient during the CPR process. Two sensors for force and depth measurement were embedded into a custom-made CPR machine and the algorithm for MI and power estimation was implemented. The performance of the algorithm was evaluated by comparing the results from the kinetic model, the conventional discrete Fourier transform (DFT), and the proposed method. The estimations of the proposed method showed similar increasing/decreasing trends with the calculations from the kinetic model. In addition, the proposed method showed statistically equivalent performance in the MI estimation, and at the same time, showed statistically superior performance in the power estimation compared with the calculations from the conventional DFT. Furthermore, the MI and power estimation could be performed almost in real-time during the CPR process without excessive hands-off periods, and the intensity of random noise contained in the input signals did not seriously affect the MI and power estimations of the proposed method. We expect that the proposed algorithm can reduce various CPR-related complications and improve patient safety.
机译:为了降低紧急情况下心肺复苏(CPR)过程中患者受损和并发症的风险,有必要在不使用其他生物信号测量设备的情况下,在进行CPR时监视患者的状况和CPR的质量。在这项研究中,提出了一种算法来估计CPR机器的执行器和患者胸部之间的机械阻抗(MI),并估计在CPR过程中传递到患者胸部的功率。将用于力和深度测量的两个传感器嵌入到定制的CPR机器中,并实现了MI和功率估计算法。通过比较动力学模型,常规离散傅里叶变换(DFT)和提出的方法的结果,评估了算法的性能。与动力学模型的计算结果相比,所提方法的估计值显示出相似的增加/减少趋势。此外,与传统DFT的计算相比,该方法在MI估计中显示出统计上等效的性能,同时在功率估计中显示出统计上优越的性能。此外,MI和功率估计几乎可以在CPR过程中实时执行,而无需过多的切换时间,并且输入信号中包含的随机噪声强度不会严重影响建议方法的MI和功率估计。我们希望所提出的算法可以减少各种与CPR相关的并发症并提高患者安全性。

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