发表机构
University of Minnesota Twin Cities（明尼苏达大学双子城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在Icentia11k数据集上对比不同时间上下文长度与编码策略，发现扩展上下文、采用连续补丁嵌入的自监督ECG模型在心律检测与跨会话检索任务中性能更优，为临床ECG模型构建提供了指导。
AI 中文摘要
自监督心电图（ECG）模型通常基于数秒的ECG信号进行训练，且越来越多地采用离散化的令牌序列。目前仍不清楚这些选择是否会牺牲真实动态记录中心律推断和纵向一致性所需的信息。我们针对Icentia11k单导联数据集开展了一项受控研究，在保持Transformer骨干网络和训练协议不变的情况下，调整了两项参数：（i）输入时间范围（16秒、1分钟、5分钟、10分钟）；（ii）前端表征（连续卷积补丁嵌入与固定向量量化令牌）。我们通过下游异常心律检测和探究跨会话稳定性的患者级检索来评估表征。结果显示，将时间上下文扩展至16秒快照以上可提升迁移性能与检索准确率，5分钟和10分钟模型的性能最优，表明其能更好捕捉慢变心律动态与个体特异性结构。在所有评估的时间范围内，连续补丁嵌入均优于离散化令牌，说明量化会丢弃临床相关的波形细节。这些发现为构建ECG基础模型提供了方向，该模型需强调扩展上下文与连续编码器，以应用于临床预测和基于相似性的任务。我们的代码与预训练模型已公开于该https URL。
英文摘要
Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear whether these choices sacrifice information needed for rhythm inference and longitudinal consistency in real-world ambulatory recordings. We present a controlled study on the Icentia11k single-lead dataset that varies (i) the input horizon (16 seconds, 1 minute, 5 minutes, and 10 minutes) and (ii) the front-end representation (continuous convolutional patch embeddings vs. fixed vector-quantized tokens), while holding the Transformer backbone and training protocol constant. Representations are assessed by downstream abnormal rhythm detection and by patient-level retrieval that probes cross-session stability. Our results show that increasing temporal context beyond 16-second snapshots yields stronger transfer and higher retrieval accuracy, with the strongest performance achieved by the 5- and 10-minute models, indicating improved capture of slow-varying rhythm dynamics and individual-specific structure. Across all evaluated horizons, continuous patch embeddings outperform discretized tokens, suggesting that quantization can discard clinically relevant waveform detail. These findings motivate ECG foundation models that emphasize extended context and continuous encoders for clinical prediction and similarity-based applications. Our code and pretrained models are publicly available at https://github.com/muha-0/ecg-ssl-representation-learning.
CommentsAccepted at the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026). 6 pages, 2 figures, 1 table
