aiqbench analysis
This research proposes beat-synchronous tokenization as a physiologically grounded alternative to fixed temporal patching in transformer-based ECG models, effectively preserving heartbeat morphology while significantly reducing sequence length. By aligning tokens with cardiac cycles rather than arbitrary time intervals, the method achieves competitive diagnostic accuracy on standard benchmarks like PTB-XL and Icentia11k with improved stability. The approach represents a meaningful optimization in medical machine learning architectures that prioritizes signal integrity over computational convenience.
Beat-Synchronous Tokenization for ECG Transformers
arXiv:2608.30367v1 Announce Type: new Abstract: 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.
Abstract: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.
6 pages, 1 figure, 3 tables. Accepted at the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026)
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2608.30367 [cs.LG]
(or arXiv:2608.30367v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.30367 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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