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场景，而用于分类任务的深度学习模型目前是研究热点。由于其数据驱动的特性，此类模型具有有效处理信号噪声的潜力，但噪声对这些方法准确性的影响尚不明确。为此，我们基准测试了四种噪声类型对基于深度学习的12导联心电图房颤检测方法准确性的影响。我们使用了公开数据集PTBXL的一个子集，并借助人类专家提供的关于噪声的元数据为每份心电图分配信号质量标签。此外，我们为每份心电图计算了定量信噪比。我们针对这两个指标分析了深度学习模型的准确性，并观察到该方法能够稳健地识别房颤，即使当信号被人类专家标记为多个导联存在噪声时也是如此。被标记为噪声数据的假阳性率和假阴性率略有恶化。有趣的是，被标注为存在基线漂移噪声的数据所获得的准确性与无噪声数据非常相似。我们得出结论：深度学习方法可以成功处理含噪声的心电图数据问题，且可能无需像许多传统方法那样进行预处理。