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

作者：Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, Rossella Arcucci · 年份：2024 · DOI：10.1109/icassp48485.2024.10446742 · 被引用次数：20 · 研究领域：ECG Monitoring and Analysis、EEG and Brain-Computer Interfaces、Phonocardiography and Auscultation Techniques

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.