Title: Simulator‑Based Synthetic ECGs for Self-Supervised Pretraining
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: simulator synthesized ECG, self‑supervised pretraining, knowledge‑driven simulators

Abstract: Medical data remain scarce and sensitive despite rich domain knowledge. 
We test whether knowledge‑driven parametric ECG generators can supply scalable pretraining signals without using patient records during pretraining.
Using two established simulator-based ECG generator, we synthesize 10‑s, 500‑Hz signals to pretrain a Transformer with masked autoencoding and compare against pretraining on real PTB‑XL and VAE/GAN‑generated ECG. 
We fine‑tune on 26 abnormal‑ECG classification tasks across PTB‑XL, G12EC, and CPSC2018 and benchmark against five supervised baselines. 
The Transformer pretrained on simulator‑based synthetic ECG performs comparably to real‑data pretraining and outperforms supervised baselines on 24 tasks, yielding a mean +5.49% improvement over the strongest baseline. 
With reduced labeled data and across patient demographics, it preserves the advantages of real‑data pretraining and shows robust performance across all 12 single leads, while avoiding patient‑data exposure during pretraining. 
These results indicate that knowledge‑driven synthetic ECG corpora can deliver practical, privacy‑enhancing initialization for downstream ECG models at scale.

TL;DR: Pretraining a Transformer with MAE on simulator-synthesized ECG matches real-data pretraining, beats supervised baselines (+5.49% mean F1, 26 tasks), and stays robust with few labels and demographic shifts—without using patient data.