ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
Che Liu Zhongwei Wan Sibo Cheng Mi Zhang Rossella Arcucci
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG)
serves as a critical, non-invasive diagnostic tool. Although recent strides in
self-supervised learning (SSL) have been promising for ECG representation
learning, these techniques often require annotated samples and struggle with
classes not present in the fine-tuning stages. To address these limitations, we
introduce ECG-Text Pre-training (ETP), an innovative framework designed to
learn cross-modal representations that link ECG signals with textual reports.
For the first time, this framework leverages the zero-shot classification task
in the ECG domain. ETP employs an ECG encoder along with a pre-trained language
model to align ECG signals with their corresponding textual reports. The
proposed framework excels in both linear evaluation and zero-shot
classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets,
showcasing its ability for robust and generalizable cross-modal ECG feature
learning.