Chest compressions during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may provoque inaccurate rhythm classification by the algorithm of the defibrillator. The objective of this study was to design an algorithm to produce reliable shock/no-shock decisions during CPR using convolutional neural networks (CNN). A total of 3319 ECG segments of 9 s extracted during chest compressions were used, whereof 586 were shockable and 2733 nonshockable. Chest compression artifacts were removed using a Recursive Least Squares (RLS) filter, and the filtered ECG was fed to a CNN classifier with three convolutional blocks and two fully connected layers for the shock/no-shock classification. A 5-fold cross validation architecture was adopted to train/test the algorithm, and the proccess was repeated 100 times to statistically characterize the performance. The proposed architecture was compared to the most accurate algorithms that include handcrafted ECG features and a random forest classifier (baseline model). The median (90% confidence interval) sensitivity, specificity, accuracy and balanced accuracy of the method were 95.8% (94.6-96.8), 96.1% (95.8-96.5), 96.1% (95.7-96.4) and 96.0% (95.5-96.5), respectively. The proposed algorithm outperformed the baseline model by 0.6-points in accuracy. This new approach shows the potential of deep learning methods to provide reliable diagnosis of the cardiac rhythm without interrupting chest compression therapy.

中文翻译：

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使用卷积神经网络进行心肺复苏期间的节律分析

心肺复苏 (CPR) 期间的胸外按压会导致心电图出现伪影，这可能会导致除颤器算法对心律分类不准确。本研究的目的是设计一种算法，使用卷积神经网络 (CNN) 在心肺复苏过程中生成可靠的电击/不电击决策。总共使用了胸外按压期间提取的 3319 个 9 秒心电图片段，其中 586 个可电击，2733 个不可电击。使用递归最小二乘 (RLS) 滤波器去除胸部按压伪影，并将过滤后的心电图输入具有三个卷积块和两个全连接层的 CNN 分类器，用于电击/无电击分类。采用 5 折交叉验证架构来训练/测试算法，并将该过程重复 100 次以统计表征性能。将所提出的架构与最准确的算法进行了比较，其中包括手工制作的心电图特征和随机森林分类器（基线模型）。该方法的中位（90%置信区间）灵敏度、特异度、准确度和平衡准确度分别为95.8%（94.6-96.8）、96.1%（95.8-96.5）、96.1%（95.7-96.4）和96.0%（95.5-96.5） ）， 分别。所提出的算法在准确度上比基线模型高出 0.6 个百分点。这种新方法显示了深度学习方法在不中断胸外按压治疗的情况下提供可靠的心律诊断的潜力。