PULSE: Physiology-Anchored Set Encoding for Variable-Lead ECG Representation Learning

Most ECG representation models assume fixed lead configurations, limiting robustness to variations in lead availability, ordering, and signal degradation. In real-world deployment, ECG lead configurations vary widely across acquisition systems, from standard 12-lead recordings to single-lead wearables. We introduce PULSE, a physiology-anchored set encoding framework for variable-lead ECG representation learning, designed to learn from arbitrary subsets and orderings of the 12 standard ECG leads. The framework encodes each lead independently without explicit lead-identity embeddings and then combines the leads through two parallel branches: one models interactions between leads with a single pairwise cross-lead attention block and pools them by permutation-invariant set aggregation, while the other pools per-lead representations that stay unchanged by which other leads are present. It is pretrained with a self-supervised objective: given randomly selected lead subsets with temporal masking, it predicts fixed morphology and rhythm descriptors computed from the corresponding clean signals. A single fine-tuned model then supports varying lead configurations without configuration-specific retraining. Comprehensive experiments across multiple ECG datasets and lead configurations demonstrate that PULSE achieves competitive or superior performance to existing self-supervised ECG representation-learning methods, while remaining robust across variable-lead settings. With 0.43M parameters, PULSE runs on a Raspberry Pi 4B, processing a 10-second ECG in about 90ms more than $100\times$ faster than real time, with the same accuracy as on a workstation. These results show that variable-lead representation learning, compact modeling, and edge deployment can be achieved within a single ECG encoder.