用心跳同步分词让心电图模型更精准，压缩序列长度同时提升性能。
Beat-Synchronous Tokenization for ECG Transformers
- 按心跳周期分词，避免切分心脏波形结构
- 在PTB-XL上达到0.8945的平均宏AUROC，序列长度从100减至11.2
- 适合心电图诊断与节奏分析任务，尤其关注波形完整性
基于Transformer的心电图（ECG）模型通常将波形划分为固定时间片段。尽管方便，但固定分段可能将单个心跳结构切分到不同标记边界。本文研究了以心跳同步为基础的分词方法，作为生理学上更合理的替代方案，对比了固定片段与三种心跳对齐策略：重采样心跳、自适应池化心跳，以及加入RR间期信息的重采样心跳。实验涵盖两种场景：在经过MIMIC-IV-ECG掩码预训练后，于PTB-XL上进行10秒12导联诊断分类；在经过患者级对比学习预训练后，于Icentia11k上进行60秒单导联节律分类。在PTB-XL上，重采样心跳标记达到最高平均宏AUROC（0.8945），几乎匹配最佳固定分段的宏AUPRC（0.7414），平均序列长度从100降至11.2。在Icentia11k上，心跳同步分词器获得与固定分段相当的AUPRC，且跨运行更稳定。结果表明，保持形态特征的心跳分词是一种紧凑且具有竞争力的替代方案。
原文摘要 · Abstract (English)
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.
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