SOURCE-LINKED INTELLIGENCE
Beat-Synchronous Tokenization for ECG Transformers
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 cla
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T07:25:12.000Z
First collected: 2026-09-21T07:01:58.596Z. This is not the publication date.