Abstract
Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks. While previous studies have explored SSL for ECG pretraining and multi-modal ECG-language alignment, they often fail to capture the multi-scale nature of ECG signals. As a result, these methods struggle to learn generalized representations due to their inability to model the hierarchical structure of ECG data. To address this gap, we introduce MELP, a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs. MELP first pretrains a cardiology-specific language model to enhance its understanding of clinical text. It then applies three levels of cross-modal supervision-at the token, beat, and rhythm levels-to align ECG signals with textual reports, capturing structured information across different time scales. We evaluate MELP on three public ECG datasets across multiple tasks, including zero-shot ECG classification, linear probing, and transfer learning. Experimental results demonstrate that MELP outperforms existing SSL methods, underscoring its effectiveness and adaptability across diverse clinical applications. Our code is available at this https URL.
Abstract (translated)
心电图（ECG）在监测心脏健康和诊断心脏病方面发挥着重要作用。然而，传统的深度学习方法在分析心电图时依赖于大规模的手动注释，这既耗时又耗费资源。为了解决这一限制，自监督学习（SSL）作为一种有前景的替代方案已经出现，它能够提取出鲁棒的心电图表示，并高效地应用于各种下游任务中。尽管以往的研究探索了心电图预训练和多模态心电信号-语言对齐方面的SSL方法，但它们往往未能捕捉到心电图信号的多层次特性。因此，由于无法建模心电数据中的层次结构，这些方法难以学习到泛化的表示形式。 为了解决这一不足，我们引入了一种新的模型MELP（多尺度心电图-语言预训练），该模型充分利用了来自心电图文本对的分层监督信号。首先，MELP预先训练了一个心脏病学特定的语言模型以增强其理解和分析临床文本的能力。然后，在标记、节律和节奏三个层次上应用跨模态监督，将心电信号与文字报告相匹配，从而捕获不同时间尺度上的结构化信息。 我们在三个公开的心电图数据集的多个任务上对MELP进行了评估，包括零样本心电图分类、线性探测以及迁移学习。实验结果显示，MELP优于现有的SSL方法，在各种临床应用中表现出强大的有效性和适应能力。我们的代码可在该链接提供。 这段文本描述了一种新的自监督学习模型——多尺度心电图-语言预训练（MELP），用于改进对心电信号的分析和理解，并在多个任务上证明了其优越性。
URL
https://arxiv.org/abs/2506.21803
