The electrocardiogram (ECG) is one of the most commonly used non-invasive, convenient medical monitoring tools that assist in the clinical diagnosis of heart diseases. Recently, deep learning (DL) techniques, particularly self-supervised learning (SSL), have demonstrated great potential in the classification of ECG. SSL pre-training has achieved competitive performance with only a small amount of annotated data after fine-tuning. However, current SSL methods rely on the availability of annotated data and are unable to predict labels not existing in fine-tuning datasets. To address this challenge, we propose Multimodal ECG-Text Self-supervised pre-training (METS), the first work to utilize the auto-generated clinical reports to guide ECG SSL pre-training. We use a trainable ECG encoder and a frozen language model to embed paired ECG and automatically machine-generated clinical reports separately. The SSL aims to maximize the similarity between paired ECG and auto-generated report while minimize the similarity between ECG and other reports. In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data via zero-shot classification, compared to other supervised and SSL baselines that rely on annotated data. Furthermore, METS achieves the highest recall and F1 scores on the MIT-BIH dataset, despite MIT-BIH containing different classes of ECG compared to the pre-trained dataset. The extensive experiments have demonstrated the advantages of using ECG-Text multimodal self-supervised learning in terms of generalizability, effectiveness, and efficiency.

翻译：心电图（ECG）是最常用的无创、便捷的医学监测工具之一，有助于心脏疾病的临床诊断。近年来，深度学习（DL）技术，特别是自监督学习（SSL），在心电图分类中展现出巨大潜力。基于SSL的预训练在仅需少量标注数据微调后即可达到具有竞争力的性能。然而，当前SSL方法依赖于标注数据的可用性，无法预测微调数据集中不存在的标签。为解决这一挑战，我们提出了多模态心电图-文本自监督预训练（METS），这是首个利用自动生成的临床报告指导心电图SSL预训练的工作。我们使用可训练的心电图编码器和冻结语言模型，分别嵌入配对的心电图与自动机器生成的临床报告。该SSL的目标是最大化配对心电图与自动生成报告之间的相似性，同时最小化心电图与其他报告之间的相似性。在下游分类任务中，METS通过零样本分类，在完全不使用任何标注数据的情况下，相比依赖标注数据的其他监督学习和SSL基线方法，性能提升了约10%。此外，尽管MIT-BIH数据集包含与预训练数据集不同类别的心电图，METS在该数据集上仍取得了最高的召回率和F1分数。大量实验证明了心电图-文本多模态自监督学习在泛化性、有效性和效率方面的优势。