Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL, covering a variety of tasks from different ECG statement prediction tasks to age and sex prediction. Among the investigated deep-learning-based timeseries classification algorithms, we find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. We find consistent results on the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL. These benchmarking results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis, which provide connecting points for future research on the dataset. Our results emphasize the prospects of deep-learning-based algorithms in the field of ECG analysis, not only in terms of quantitative accuracy but also in terms of clinically equally important further quality metrics such as uncertainty quantification and interpretability. With this resource, we aim to establish the PTB-XL dataset as a resource for structured benchmarking of ECG analysis algorithms and encourage other researchers in the field to join these efforts.

中文翻译：

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心电图分析的深度学习：来自 PTB-XL 的基准和见解

心电图 (ECG) 是一种非常常见的非侵入性诊断程序，其解释越来越受到算法的支持。迄今为止，由于缺乏合适的训练数据集以及缺乏明确定义的评估程序以确保不同算法的可比性，自动心电图分析领域的进展受到阻碍。为了缓解这些问题，我们为最近发布的、可免费访问的临床 12 导联心电图数据集 PTB-XL 提出了第一个基准测试结果，涵盖了从不同心电图语句预测任务到年龄和性别预测的各种任务。在研究的基于深度学习的时间序列分类算法中，我们发现卷积神经网络，特别是基于 resnet 和 inception 的架构，在所有任务中表现出最强的性能。我们在 ICBEB2018 挑战心电图数据集上找到了一致的结果，并讨论了使用在 PTB-XL 上预训练的分类器进行迁移学习的前景。这些基准测试结果与在隐藏分层、模型不确定性和探索性可解释性分析方面对分类算法的更深入了解相辅相成，为未来对数据集的研究提供了连接点。我们的结果强调了基于深度学习的算法在心电图分析领域的前景，不仅在定量准确性方面，而且在临床上同样重要的进一步质量指标方面，如不确定性量化和可解释性。有了这个资源，