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 21799 clinical 12-lead ECGs from
18869 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（Electrocardiography, ECG）是评估患者心脏状况的关键诊断工具。作为诊断辅助系统的自动心电图解读算法，有望大幅减轻医疗人员的工作负担——仅以常规采集的心电图数量计算即可见其应用价值。然而，此类算法的开发离不开大规模训练数据集与规范的基准测试流程。但在我们看来，当前已有的可免费获取的心电图数据集均未能在这两方面做到令人满意。

PTB-XL心电图数据集是一项大型数据集，包含来自18869名患者的21799份时长为10秒的临床12导联心电图记录。原始波形数据由至多两名心脏病学家进行标注，每份记录可被赋予多条心电图诊断表述。总计71种不同的心电图诊断表述均符合SCP-ECG标准，涵盖诊断类、形态类与节律类表述。为确保基于本数据集训练的机器学习算法具备可比较性，我们提供了推荐的训练集与测试集划分方案。结合详尽的标注信息，本数据集可成为训练与评估自动心电图解读算法的优质资源。本数据集还附带了丰富的元数据，包括人口统计学信息、梗死特征、诊断类心电图表述的可能性评分以及标注后的信号属性。

搜集汇总

背景与挑战

背景概述

PTB-XL是一个大型公开临床心电图数据集，包含来自18869名患者的21799条10秒12导联心电图记录，每条记录由心脏病专家根据SCP-ECG标准进行多标签标注，涵盖71种诊断、形态和心律陈述。该数据集提供了建议的训练测试划分和丰富的患者元数据，专门设计用于支持自动心电图解释算法的开发、训练与基准评估。