Learning to Automatically Generate Accurate ECG Captions

Mathieu G. G. Bartels, Ivona Najdenkoska, Rutger R Leur, Arjan Sammani, Karim Taha, David M Knigge, Pieter A Doevendans, Marcel Worring, René Es

MIDL 2023 pp. 86-102

/midl/2023/bartels2023midl-learning/

Abstract

The electrocardiogram (ECG) is an affordable, non-invasive and quick method to gain essential information about the electrical activity of the heart. Interpreting ECGs is a time-consuming process even for experienced cardiologists, which motivates the current usage of rule-based methods in clinical practice to automatically describe ECGs. However, in comparison with descriptions created by experts, ECG-descriptions generated by such rule-based methods show considerable limitations. Inspired by image captioning methods, we instead propose a data-driven approach for ECG description generation. We introduce a label-guided Transformer model, and show that it is possible to automatically generate relevant and readable ECG descriptions with a data-driven captioning model. We incorporate prior ECG labels into our model design, and show this improves the overall quality of generated descriptions. We find that training these models on free-text annotations of ECGs - instead of the clinically-used computer generated ECG descriptions - greatly improves performance. Moreover, we perform a human expert evaluation study of our best system, which shows that our data-driven approach improves upon existing rule-based methods.

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Cite

Text

Bartels et al. "Learning to Automatically Generate Accurate ECG Captions." Medical Imaging with Deep Learning, 2023.

Markdown

[Bartels et al. "Learning to Automatically Generate Accurate ECG Captions." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/bartels2023midl-learning/)

BibTeX

@inproceedings{bartels2023midl-learning,
  title     = {{Learning to Automatically Generate Accurate ECG Captions}},
  author    = {Bartels, Mathieu G. G. and Najdenkoska, Ivona and Leur, Rutger R and Sammani, Arjan and Taha, Karim and Knigge, David M and Doevendans, Pieter A and Worring, Marcel and Es, René},
  booktitle = {Medical Imaging with Deep Learning},
  year      = {2023},
  pages     = {86-102},
  volume    = {172},
  url       = {https://mlanthology.org/midl/2023/bartels2023midl-learning/}
}