发表机构
University of Minnesota Twin Cities（明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对心电信号Transformer的固定时间分词易分割心跳结构的问题，提出心跳同步分词策略，在两个公开心电数据集上验证其性能，表明该方法是固定时间分词的紧凑且具竞争力替代方案。
AI 中文摘要
基于Transformer的心电信号（ECG）模型通常将波形划分为固定时间块进行分词，该方法虽便捷，但会将心跳结构分割在不同 token 边界。本文研究心跳同步分词作为符合生理特性的替代方案，对比固定时间块与三种心跳对齐策略：重采样心跳、自适应池化心跳、添加R-R间期信息的重采样心跳。实验涵盖两个场景：在MIMIC-IV-ECG掩码预训练后，于PTB-XL数据集上开展10秒12导联诊断分类；在患者级对比预训练后，于Icentia11k数据集上开展60秒单导联节律分类。在PTB-XL上，重采样心跳token取得最高平均宏ROC曲线下面积（AUROC；0.8945），且接近最优固定时间块的宏精确召回曲线下面积（AUPRC；0.7414），将平均序列长度从100个token降至11.2个。在Icentia11k上，心跳同步分词器取得与固定时间块相当的AUPRC，且运行稳定性更优。这些结果表明，保留形态的心跳分词是紧凑且具竞争力的固定时间分词替代方案。
英文摘要
Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split heartbeat structures across token boundaries. We study beat-synchronous tokenization as a physiologically grounded alternative, comparing fixed patches with three beat-aligned strategies: resampled beats, adaptive pooled beats, and resampled beats augmented with R--R interval information. Experiments span two settings: 10-second 12-lead diagnostic classification on PTB-XL after MIMIC-IV-ECG masked pretraining, and 60-second single-lead rhythm classification on Icentia11k after patient-level contrastive pretraining. On PTB-XL, resampled beat tokens achieve the highest mean macro Area Under the ROC Curve (AUROC; 0.8945) and nearly match the best fixed-patch macro Area Under the Precision-Recall Curve (AUPRC; 0.7414), reducing average sequence length from 100 to 11.2 tokens. On Icentia11k, beat-synchronous tokenizers obtain comparable AUPRC to fixed patching with better stability across runs. These results suggest morphology-preserving beat tokenization is a compact, competitive alternative to fixed temporal patching.
Comments6 pages, 1 figure, 3 tables. Accepted at the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026)
