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.

该研究提出了使用机器生成的临床报告指导自我监督的心电图预训练的 ECG-Text 多模态自监督预训练（METS）。METS 在零 - shot 分类中能够实现约 10％的性能改善，而不使用任何带标签的数据，此外，在 MIT-BIH 数据集上，MET 对预训练数据集的 ECG 和其他类别的 ECG 之间的相似性进行了最小化，体现了在泛化性，效果和效率方面使用 ECG-Text 多模态自监督学习的优势。