BackgroundBecause chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival.Methods and ResultsThe objective of this study was to apply a deep‐learning algorithm using convolutional layers, residual networks, and bidirectional long short‐term memory method to classify shockable versus nonshockable rhythms in the presence and absence of CPR artifact. Forty subjects' data from Physionet with 1131 shockable and 2741 nonshockable samples contaminated with 43 different CPR artifacts that were acquired from a commercial automated external defibrillator during asystole were used. We had separate data as train and test sets. Using our deep neural network model, the sensitivity and specificity of the shock versus no‐shock decision for the entire data set over the 4‐fold cross‐validation sets were 95.21% and 86.03%, respectively. This result was based on the training and testing of the model using ECG data in both the presence and the absence of CPR artifact. For ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. In addition to 4‐fold cross‐validation sets, we also examined leave‐one‐subject‐out validation. The sensitivity and specificity for the case of leave‐one‐subject‐out validation were 92.71% and 97.6%, respectively.ConclusionsThe proposed trained model can make shock versus nonshock decision in automated external defibrillators, regardless of CPR status. The results meet the American Heart Association's sensitivity requirement (>90%).

中文翻译：

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心肺复苏期间基于连续心电图的自动体外除颤器电击咨询系统的深度神经网络方法

背景因为胸外按压会在 ECG 中引起伪影，当前的自动体外除颤器会在执行自动节律分析时指示用户停止心肺复苏 (CPR)。已经表明，最大限度地减少 CPR 中断会增加生存机会。存在和不存在 CPR 伪影时的节律。使用了来自 Physionet 的 40 名受试者的数据，其中 1131 个可电击样本和 2741 个非电击样本被 43 种不同的 CPR 伪影污染，这些伪影是在心搏停止期间从商业自动体外除颤器获得的。我们有单独的数据作为训练和测试集。使用我们的深度神经网络模型，整个数据集在 4 倍交叉验证集上的休克与非休克决策的敏感性和特异性分别为 95.21% 和 86.03%。该结果基于在存在和不存在 CPR 伪影的情况下使用 ECG 数据对模型进行的训练和测试。对于无 CPR 伪影的心电图，敏感性为 99.04%，特异性为 95.2%。对于带有 CPR 伪影的心电图，获得了 94.21% 的灵敏度和 86.14% 的特异性。除了 4 折交叉验证集，我们还检查了留一主题验证。留一受试者退出验证案例的敏感性和特异性分别为 92.71% 和 97.6%。无论 CPR 状态如何。结果符合美国心脏协会的灵敏度要求 (>90%)。