Li Yongqin, Bisera Joe, Tang Wanchun, Weil Max Harry
Weil Institute of Critical Care Medicine, Rancho Mirage, California 92270, USA.
Crit Pathw Cardiol. 2007 Sep;6(3):131-4. doi: 10.1097/HPC.0b013e31813429b0.
Sudden death due to ventricular fibrillation (VF) is a catastrophic event, especially in out-of-hospital settings. Prompt detection of VF and preparedness to intervene with cardiopulmonary resuscitation (CPR) and especially the delivery of an electrical shock is potentially lifesaving. The reliability and accuracy of automated VF detection by current versions of automated external defibrillators (AEDs) require interruption of CPR because the ECG signal, which is the source of rhythm detection, is corrupted by chest compressions. Significantly better outcomes have been reported if effective chest compression precedes electrical defibrillation and especially if interruptions are minimized. We therefore sought a method by which VF detection could proceed without interrupting chest compressions. A VF detection algorithm was therefore derived based on a method by which continuous wavelet transform is used, together with measurement of morphologic consistency. This method was intended to distinguish between disorganized and organized rhythms. The Fourier-transform-based amplitude spectrum analysis was then used to detect the likelihood that VF was the rhythm prompting the delivery of an electrical shock. The algorithm was validated on 33,095 electrocardiographic segments, including 8840 segments corrupted by compression artifacts from 232 patients after out-of-hospital cardiac arrest. Nine thousand one hundred eighty-seven of 10,042 VF segments and 20,884 of 23,053 non-VF segments were correctly classified, with a sensitivity of 91.5% and a specificity of 90.6%. Although the proposed algorithm has a lesser predictive value for VF detection than the uncorrupted ECGs in clinical settings, it has the major potential for automated rhythm identification to guide defibrillation without repetitive interruptions of CPR.
心室颤动(VF)导致的猝死是一场灾难性事件,尤其在院外环境中。及时检测VF并做好进行心肺复苏(CPR)干预的准备,特别是实施电击,有可能挽救生命。当前版本的自动体外除颤器(AED)进行自动VF检测的可靠性和准确性需要中断CPR,因为作为心律检测源的心电图信号会因胸部按压而受到干扰。如果有效的胸部按压先于电击除颤,尤其是将中断减至最少,据报道会有显著更好的结果。因此,我们寻求一种在不中断胸部按压的情况下进行VF检测的方法。基于一种使用连续小波变换并结合形态一致性测量的方法,得出了一种VF检测算法。该方法旨在区分紊乱和规整的心律。然后使用基于傅里叶变换的振幅谱分析来检测VF作为提示电击除颤心律的可能性。该算法在33095个心电图片段上得到验证,包括232例院外心脏骤停患者的8840个因按压伪影而受干扰的片段。在10042个VF片段中有9187个、在23053个非VF片段中有20884个被正确分类,灵敏度为91.5%,特异性为90.6%。尽管所提出的算法在临床环境中对VF检测的预测价值低于未受干扰的心电图,但它具有自动识别心律以指导除颤而无需反复中断CPR的主要潜力。