Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
This work addresses the problem of inconsistent benchmarking in ECG analysis for researchers and clinicians, though it is incremental as it builds on existing datasets and methods.
The paper tackled the lack of standardized datasets and evaluation procedures in automatic ECG interpretation by benchmarking the PTB-XL dataset across multiple tasks, finding that convolutional neural networks, especially resnet- and inception-based architectures, significantly outperformed feature-based algorithms.
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.