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）是评估患者心脏状况的关键诊断工具。基于自动ECG解读算法的诊断辅助系统为医务人员提供了极大的便利——这一便利仅基于常规进行的ECG数量。然而，此类算法的开发需要大量的训练数据集和明确的基准程序。在我们看来，现有可自由获取的ECG数据集在上述两方面均未能得到充分满足。《PTB-XL》ECG数据集是一个包含21837例临床12导联心电图的大型数据集，数据来源于18885名患者的10秒长度心电图。原始波形数据由最多两名心脏病学家进行标注，每位记录可能被分配多个ECG陈述。总计71种不同的ECG陈述符合SCP-ECG标准，涵盖了诊断、形态和节律等方面的陈述。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，这使得该数据集成为自动ECG解读算法训练与评估的宝贵资源。该数据集还辅以广泛的元数据，包括人口统计学信息、梗死特征、诊断ECG陈述的可能性以及标注的信号特性。

搜集汇总

背景与挑战

背景概述

PTB-XL是一个大型公开的临床心电图数据集，包含21,837条12导联心电图记录，来自18,885名患者，每条记录都有心脏病专家的详细标注，并提供了标准化的训练测试划分建议，适用于心电图自动分析算法的开发和评估。