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 21801 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.