中文标题#
通过多窗口对比学习学习心电图表示
英文标题#
Learning ECG Representations via Poly-Window Contrastive Learning
中文摘要#
心电图（ECG）分析是心血管疾病诊断的基础，但深度学习模型的性能通常受到标注数据有限访问的限制。自监督对比学习已成为从无标签信号中学习强大 ECG 表示的强大方法。然而，大多数现有方法仅生成成对增强视图，并未能利用 ECG 记录丰富的时序结构。在本工作中，我们提出了一种多窗口对比学习框架。我们从每个 ECG 实例中提取多个时序窗口以构建正对，并通过统计学最大化它们的一致性。受慢特征分析原理的启发，我们的方法明确鼓励模型学习随时间不变且具有生理意义的特征。我们在 PTB-XL 数据集上通过广泛的实验和消融研究验证了我们的方法。我们的结果表明，多窗口对比学习在多标签超类分类中始终优于传统双视图方法，在达到更高的 AUROC（0.891 vs. 0.888）和 F1 分数（0.680 vs. 0.679）的同时，所需的预训练周期最多减少了四倍（32 vs. 128），总墙钟预训练时间减少了 14.8%。尽管每个样本处理多个窗口，我们仍实现了训练周期和总计算时间的显著减少，使我们的方法适用于训练基础模型。通过广泛的消融实验，我们确定了最佳设计选择，并展示了在各种超参数下的鲁棒性。这些发现确立了多窗口对比学习作为自动化 ECG 分析的高度高效和可扩展范式，并为生物医学时间序列数据中的自监督表示学习提供了一个有前景的通用框架。
英文摘要#
Electrocardiogram (ECG) analysis is foundational for cardiovascular disease diagnosis, yet the performance of deep learning models is often constrained by limited access to annotated data. Self-supervised contrastive learning has emerged as a powerful approach for learning robust ECG representations from unlabeled signals. However, most existing methods generate only pairwise augmented views and fail to leverage the rich temporal structure of ECG recordings. In this work, we present a poly-window contrastive learning framework. We extract multiple temporal windows from each ECG instance to construct positive pairs and maximize their agreement via statistics. Inspired by the principle of slow feature analysis, our approach explicitly encourages the model to learn temporally invariant and physiologically meaningful features that persist across time. We validate our approach through extensive experiments and ablation studies on the PTB-XL dataset. Our results demonstrate that poly-window contrastive learning consistently outperforms conventional two-view methods in multi-label superclass classification, achieving higher AUROC (0.891 vs. 0.888) and F1 scores (0.680 vs. 0.679) while requiring up to four times fewer pre-training epochs (32 vs. 128) and 14.8% in total wall clock pre-training time reduction. Despite processing multiple windows per sample, we achieve a significant reduction in the number of training epochs and total computation time, making our method practical for training foundational models. Through extensive ablations, we identify optimal design choices and demonstrate robustness across various hyperparameters. These findings establish poly-window contrastive learning as a highly efficient and scalable paradigm for automated ECG analysis and provide a promising general framework for self-supervised representation learning in biomedical time-series data.
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