Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios.

中文翻译：

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使用级联 CNNED 提高持续心肺复苏期间自动体外除颤器电击建议算法的准确性

持续心肺复苏 (CPR) 的实施对于院外心脏骤停 (OHCA) 的存活率起着重要作用。然而，为了防止心肺复苏伪影叠加在心电图形态数据上，目前可用的自动体外除颤器 (AED) 需要暂停心肺复苏才能准确分析心律。在本研究中，我们提出了一种新颖的基于卷积神经网络的编码器-解码器 (CNNED) 结构，具有电击咨询算法，可提高 OHCA 场景中电击与非电击决策的准确性和可靠性，而无需 CPR 暂停。