Program of Mechanical & Biomedical Engineering, Kangwon National University, Chuncheon, South Korea.
Division of Biomedical Engineering, Pusan National University Yangsan Hospital, Yangsan, South Korea.
Biomed Eng Online. 2018 Jun 19;17(1):84. doi: 10.1186/s12938-018-0521-5.
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.
为了降低紧急情况下心肺复苏（CPR）过程中患者损伤和并发症的风险，有必要在不使用额外生物信号测量设备的情况下，监测患者的状态和 CPR 的质量。在本研究中，提出了一种算法，用于估计 CPR 机致动器与患者胸部之间的机械阻抗（MI），并估计 CPR 过程中向患者胸部传递的功率。
将两个用于力和深度测量的传感器嵌入到定制的 CPR 机中，并实现了用于 MI 和功率估计的算法。通过将结果与动力学模型、传统离散傅里叶变换（DFT）和所提出的方法进行比较，评估了算法的性能。
所提出方法的估计结果与动力学模型的计算结果具有相似的增减趋势。此外，所提出的方法在 MI 估计方面表现出统计学上等效的性能，同时在功率估计方面表现出统计学上优于传统 DFT 的性能。此外，在 CPR 过程中几乎可以实时进行 MI 和功率估计，而输入信号中包含的随机噪声的强度不会严重影响所提出方法的 MI 和功率估计。
我们期望所提出的算法可以减少各种与 CPR 相关的并发症，提高患者安全性。
