Keywords: Transformer, Encoder-Decoder, ECG, Signal processing, ResNet, Captioning
TL;DR: Data-driven methods can generate cardiologist-level ECG descriptions; incorporating prior knowledge improves generations.
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 to 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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Paper Type: methodological development
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Application: Other
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Code And Data: The dataset used in this research was chosen for its quality and for the fact that it was obtained in a real clinical setting, making it unpublishable publicly. However the code is publicly available here: https://github.com/MathieuBartels/ECGCaption