Evaluating Synthetic ECG Pretraining: When Can Patient-Free Simulators Substitute for Real ECG Data?

ECG, with mature physiological simulators and public benchmarks, is a useful testbed for evaluating what synthetic pretraining supports. We introduce a reusable framework for ECG representation learning that compares patient-free physiological simulators, patient-derived real ECGs, and learned synthetic ECGs from real-ECG-trained generators. Holding the encoder and MAE objective fixed, we study three matched settings. Transfer-protocol analysis compares abnormal-inclusive real ECGs, normal-only real ECGs, and simulator pretraining under frozen probing and full fine-tuning on 26 abnormal-ECG tasks from PTB-XL, G12EC, and CPSC2018 using a single-lead-II pipeline. Compute-normalized PTB-XL scaling tests whether simulator pretraining matches real-data pretraining under equal processed-sample budgets. A fixed 100k-sample generator-class comparison evaluates VAE, DCGAN, SSSD-ECG diffusion variants with and without labels, and two simulators, SimECG-M and SimECG-N. The transfer analysis shows that protocol determines the supported claim: abnormal-inclusive real ECG pretraining yields more linearly separable pathology features under frozen probing, whereas full fine-tuning narrows the gap and can reverse it when the simulator corpus is sufficiently scaled. Under the fixed-100k protocol, task-wise F1 averaged over five runs ranks SimECG-N first on 22/26 tasks; no evaluated learned generator ranks first under this fixed-100k protocol; and SimECG-N obtains higher mean F1 than both diffusion variants on all 26 tasks. These results delineate where abnormal-inclusive real exposure remains beneficial and where patient-free physiological simulation can serve as an effective, protocol-bound substitute under the evaluated single-lead-II MAE pretraining and full-fine-tuning setting. We release pretraining corpora, splits, and code to support reproducible evaluation of synthetic ECG pretraining sources.