arXiv AI
3d ago

On the role of the tokenizer in ECG transformer models

The study investigates how different tokenization methods affect ECG Transformer models by comparing eight strategies across four backbone architectures on the CPSC2018 classification task. Physiology-aware tokenizations such as Median-beat and HeartLang achieve higher mean macro-AUCs (0.893 and 0.889) than point-wise and patch-wise approaches (0.822 and 0.824), while also reducing sequence length and training memory usage. Combining the two physiology-aware representations further improves macro-AUC by 8.2%.

By Jiawei Li, Fabio Bonassi, Johan Sundstr\"om, Thomas B. Sch\"on, Ant\^onio H. Ribeiro
arXiv AI
Jul 7

ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment

arXiv:2607. 05009v1 Announce Type: cross Abstract: Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption.

By Xiaocheng Fang, Haoyu Wang, Jieyi Cai, Qinghao Zhao, Jun Li, Shanwei Zhang, Guangkun Nie, Yujie Xiao, Shun Huang, Jiarui Jin, Hongmin Liu, Guodong Wang, Shuohua Chen, Liming Lin, Shouling Wu, Hongyan Li, Shenda Hong