中文标题#
通過多窗口對比學習學習心電圖表示
英文标题#
Learning ECG Representations via Poly-Window Contrastive Learning
中文摘要#
心電圖（ECG）分析是心血管疾病診斷的基礎，但深度學習模型的性能通常受到標註數據有限訪問的限制。自監督對比學習已成為從無標籤信號中學習強大 ECG 表示的強大方法。然而，大多數現有方法僅生成成對增強視圖，並未能利用 ECG 記錄豐富的時序結構。在本工作中，我們提出了一種多窗口對比學習框架。我們從每個 ECG 實例中提取多個時序窗口以構建正對，並通過統計學最大化它們的一致性。受慢特徵分析原理的啟發，我們的方法明確鼓勵模型學習隨時間不變且具有生理意義的特徵。我們在 PTB-XL 數據集上通過廣泛的實驗和消融研究驗證了我們的方法。我們的結果表明，多窗口對比學習在多標籤超類分類中始終優於傳統雙視圖方法，在達到更高的 AUROC（0.891 vs. 0.888）和 F1 分數（0.680 vs. 0.679）的同時，所需的預訓練周期最多減少了四倍（32 vs. 128），總牆鐘預訓練時間減少了 14.8%。儘管每個樣本處理多個窗口，我們仍實現了訓練周期和總計算時間的顯著減少，使我們的方法適用於訓練基礎模型。通過廣泛的消融實驗，我們確定了最佳設計選擇，並展示了在各種超參數下的魯棒性。這些發現確立了多窗口對比學習作為自動化 ECG 分析的高度高效和可擴展範式，並為生物醫學時間序列數據中的自監督表示學習提供了一個有前景的通用框架。
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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