Zuo Feng, Ding Youde, Dai Chenxi, Wei Liang, Gong Yushun, Wang Juan, Shen Yiming, Li Yongqin
Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China.
Department of Information Technology, Southwest Hospital, Army Medical University, Chongqing, China.
Ann Transl Med. 2021 Apr;9(8):619. doi: 10.21037/atm-20-7166.
Amplitude spectrum area (AMSA) calculated from ventricular fibrillation (VF) can be used to monitor the effectiveness of chest compression (CC) and optimize the timing of defibrillation. However, reliable AMSA can only be obtained during CC pause because of artifacts. In this study, we sought to develop a method for estimating AMSA during cardiopulmonary resuscitation (CPR) using only the electrocardiogram (ECG) waveform.
Intervals of 8 seconds ECG and CC-related references, including 4 seconds during CC and an adjacent 4 seconds without CC, were collected before 1,008 defibrillation shocks from 512 out-of-hospital cardiac arrest patients. Signal quality was analyzed based on the irregularity of autocorrelation of VF. If signal quality index (SQI) was high, AMSA would be calculated from the original signal. Otherwise, CC-related artifacts would be constructed and suppressed using the least mean square filter from VF before calculation of AMSA. The algorithm was optimized using 480 training shocks and evaluated using 528 independent testing shocks.
Overall, CC resulted in lower SQI [0.15 (0.04-0.61) with CC . 0.75 (0.61-0.83) without CC, P<0.01] and higher AMSA [11.2 (7.7-16.2) with CC . 7.2 (4.9-10.6) mVHz without CC, P<0.01] values. The predictive accuracy (49.2% . 66.5%, P<0.01) and area under the receiver operating characteristic curve (AUC) (0.647 . 0.734, P<0.01) were significantly decreased during CC. Using the proposed method, the estimated AMSA was 7.1 (5.0-15.2) mVHz, the predictive accuracy was 67.0% and the AUC was 0.713, which were all comparable with those calculated without CC.
Using the signal quality-based artifact suppression method, AMSA can be reliably estimated and continuously monitored during CPR.
从室颤(VF)计算得出的振幅频谱面积(AMSA)可用于监测胸外按压(CC)的效果并优化除颤时机。然而,由于伪迹,只有在CC暂停期间才能获得可靠的AMSA。在本研究中,我们试图开发一种仅使用心电图(ECG)波形来估计心肺复苏(CPR)期间AMSA的方法。
在对512例院外心脏骤停患者进行1008次除颤电击之前,收集8秒的ECG间隔和与CC相关的参考数据,包括CC期间的4秒和相邻的无CC的4秒。基于VF自相关的不规则性分析信号质量。如果信号质量指数(SQI)高,则从原始信号计算AMSA。否则,在计算AMSA之前,将使用来自VF的最小均方滤波器构建并抑制与CC相关的伪迹。该算法使用480次训练电击进行优化,并使用528次独立测试电击进行评估。
总体而言,CC导致较低的SQI[CC时为0.15(0.04 - 0.61),无CC时为0.75(0.61 - 0.83),P<0.01]和较高的AMSA值[CC时为11.2(7.7 - 16.2),无CC时为7.2(4.9 - 10.6)mVHz,P<0.01]。在CC期间,预测准确性(49.2%对66.5%,P<0.01)和受试者工作特征曲线下面积(AUC)(AUC)(0.647对0.734,P<0.01)显著降低。使用所提出的方法,估计的AMSA为7.1(5.0 - 15.2)mVHz,预测准确性为67.0%,AUC为0.713,这些均与无CC时计算的值相当。
使用基于信号质量的伪迹抑制方法,可以在CPR期间可靠地估计并持续监测AMSA。