PTB-XL, a large publicly available electrocardiography dataset

Electrocardiography (ECG) is a key diagnostic tool to assess the cardiac condition of a patient. Automatic ECG interpretation algorithms as diagnosis support systems promise large reliefs for the medical personnel - only on the basis of the number of ECGs that are routinely taken. However, the development of such algorithms requires large training datasets and clear benchmark procedures. In our opinion, both aspects are not covered satisfactorily by existing freely accessible ECG datasets.

The PTB-XL ECG dataset is a large dataset of 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The raw waveform data was annotated by up to two cardiologists, who assigned potentially multiple ECG statements to each record. The in total 71 different ECG statements conform to the SCP-ECG standard and cover diagnostic, form, and rhythm statements. To ensure comparability of machine learning algorithms trained on the dataset, we provide recommended splits into training and test sets. In combination with the extensive annotation, this turns the dataset into a rich resource for the training and the evaluation of automatic ECG interpretation algorithms. The dataset is complemented by extensive metadata on demographics, infarction characteristics, likelihoods for diagnostic ECG statements as well as annotated signal properties.

心电图（ECG）作为评估患者心脏状况的关键诊断工具，具有举足轻重的作用。自动心电图解读算法作为诊断辅助系统，有望为医务人员带来极大的缓解——这仅基于常规采集的心电图数量。然而，此类算法的开发需要庞大的训练数据集和明确的基准程序。在我们看来，这两个方面均未能被现有的免费心电图数据集所充分覆盖。

PTB-XL心电图数据集是一个包含21837例临床12导联心电图的大规模数据集，涉及18885名患者的10秒长度记录。原始波形数据由最多两位心脏病学家进行标注，每位记录可能被分配多个心电图陈述。总计71种不同的心电图陈述符合SCP-ECG标准，涵盖了诊断、形态和节律陈述。为确保在数据集上训练的机器学习算法的可比性，我们提供了推荐的训练集和测试集划分。结合广泛的标注，该数据集成为自动心电图解读算法训练和评估的宝贵资源。此外，数据集还补充了关于人口统计学、梗死特征、诊断心电图陈述的可能性以及标注信号特性的详尽元数据。