  Electrocardiography is a very common, non-invasive diagnostic procedure and
its interpretation is increasingly supported by automatic interpretation
algorithms. The progress in the field of automatic ECG interpretation 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 PTB-XL dataset, covering
a variety of tasks from different ECG statement prediction tasks over age and
gender prediction to signal quality assessment. We find that convolutional
neural networks, in particular resnet- and inception-based architectures, show
the strongest performance across all tasks outperforming feature-based
algorithms by a large margin. These results are complemented by deeper insights
into the classification algorithm in terms of hidden stratification, model
uncertainty and an exploratory interpretability analysis. We also put forward
benchmarking results for the ICBEB2018 challenge ECG dataset and discuss
prospects of transfer learning using classifiers pretrained on PTB-XL. 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.
