Obtaining labelled ECG data for developing supervised models is challenging. Contrastive learning (CL) has emerged as a promising pretraining approach that enables effective transfer learning with limited labelled data. However, existing CL frameworks either focus solely on global context or fail to exploit ECG-specific characteristics. Furthermore, these methods rely on hard contrastive targets, which may not adequately capture the continuous nature of feature similarity in ECG signals. In this paper, we propose Beat-SSL, a contrastive learning framework that performs dual-context learning through both rhythm-level and heartbeat-level contrasting with soft targets. We evaluated our pretrained model on two downstream tasks: 1) multilabel classification for global rhythm assessment, and 2) ECG segmentation to assess its capacity to learn representations across both contexts. We conducted an ablation study and compared the best configuration with three other methods, including one ECG foundation model. Despite the foundation model's broader pretraining, Beat-SSL reached 93% of its performance in multilabel classification task and surpassed all other methods in the segmentation task by 4%.

翻译：获取用于开发监督模型的标记心电图数据具有挑战性。对比学习作为一种有前景的预训练方法，能够在标记数据有限的情况下实现有效的迁移学习。然而，现有的对比学习框架要么仅关注全局上下文，要么未能充分利用心电图特有的属性。此外，这些方法依赖于硬对比目标，可能无法充分捕捉心电图信号中特征相似性的连续特性。本文提出Beat-SSL，一种通过节律级和心跳级双重上下文对比并结合软目标进行学习的对比学习框架。我们在两个下游任务上评估了预训练模型：1）用于全局节律评估的多标签分类，以及2）心电图分割，以评估其跨两种上下文学习表征的能力。我们进行了消融研究，并将最佳配置与其他三种方法（包括一个心电图基础模型）进行了比较。尽管该基础模型进行了更广泛的预训练，Beat-SSL在多标签分类任务中达到了其性能的93%，并在分割任务中以4%的优势超越了所有其他方法。