Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms. We use a subset of a publicly available dataset (PTBXL) and use the metadata provided by human experts regarding noise for assigning a signal quality to each electrocardiogram. Furthermore, we compute a quantitative signal-to-noise ratio for each electrocardiogram. We analyze the accuracy of the Deep Learning model with respect to both metrics and observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads. False positive and false negative rates are slightly worse for data being labelled as noisy. Interestingly, data annotated as showing baseline drift noise results in an accuracy very similar to data without. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods that might not need preprocessing as many conventional methods do.

翻译：心电图分析广泛应用于各种临床应用中，深度学习分类任务模型目前是研究的重点。鉴于其数据驱动的特性，它们有可能有效地处理信号噪音，但其对这些方法的准确性的影响仍不清楚。因此，我们使用公开数据集（PTBXL）的子集，并使用人工专家提供的有关噪声的元数据为每个心电图分配信号质量。此外，我们为每个心电图计算定量的信号噪声比。我们针对这两个指标分析了深度学习模型的准确性，并观察到即使在信号质量被人工专家标记为多个引线有噪声的情况下，该方法仍然可以稳健地识别房颤。标记为有噪声的数据导致误报率和漏报率略微变差。有趣的是，基线漂移噪声的数据的准确性非常接近没有噪声的数据。我们得出结论，处理有噪声的心电图数据的问题可以通过深度学习方法成功解决，这些方法可能不需要像许多传统方法那样进行预处理。