ETP: Learning Transferable ECG Representations via ECG-Text Pre-training

• URL: http://arxiv.org/abs/2309.07145v1
• Date: Wed, 6 Sep 2023 19:19:26 GMT
• Title: ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
• Authors: Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, Rossella Arcucci
• Abstract summary: ECG-Text Pre-training (ETP) is an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports.
• Score: 10.856365645831728
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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