Yang Zhijun, Yang Zhengrong, Lu Weiping, Harrison Robert G, Eftestøl Trygve, Steen Petter A
Department of Computer Science, Nanjing Normal University, Nanjing, China.
Resuscitation. 2005 Jan;64(1):31-6. doi: 10.1016/j.resuscitation.2004.07.002.
Although modern defibrillators are nearly always successful in terminating ventricular fibrillation (VF), multiple defibrillation attempts are usually required to achieve return of spontaneous circulation (ROSC). This is potentially deleterious as cardiopulmonary resuscitation (CPR) must be discontinued during each defibrillation attempt which causes deterioration in the heart muscle and reduces the chance of ROSC from later defibrillation attempts. In this work defibrillation outcomes are predicted prior to electrical shocks using a neural network model to analyse VF time series in an attempt to avoid defibrillation attempts that do not result in ROSC.
The 198 pre-shock VF ECG episodes from 83 cardiac arrest patients with defibrillation conversions to different outcomes were selected from the Oslo ambulance service database. A probabilistic neural network model was designed for training and testing with a cross validation method being used for the better generalisation performance.
We achieved an accuracy of 75% in overall prediction with a sensitivity of 84% and a specificity of 65% using VF ECG time series of an order of 1 s in length.
Pre-shock VF ECG time series can be classified according to the defibrillation conversion to a return of spontaneous circulation (ROSC) or No-ROSC.
尽管现代除颤器几乎总能成功终止心室颤动（VF），但通常需要多次除颤尝试才能实现自主循环恢复（ROSC）。这可能具有潜在危害，因为在每次除颤尝试期间必须中断心肺复苏（CPR），这会导致心肌恶化，并降低后续除颤尝试实现ROSC的机会。在这项研究中，在电击前使用神经网络模型分析VF时间序列来预测除颤结果，以避免进行无法导致ROSC的除颤尝试。
从奥斯陆救护车服务数据库中选取了83例心脏骤停患者的198次电击前VF心电图发作，这些发作具有不同的除颤转归。设计了一个概率神经网络模型进行训练和测试，并采用交叉验证方法以获得更好的泛化性能。
使用长度约为1秒的VF心电图时间序列，我们在总体预测中的准确率为75%，敏感性为84%，特异性为65%。
电击前的VF心电图时间序列可根据除颤转归为自主循环恢复（ROSC）或未恢复自主循环（No-ROSC）进行分类。
