Measured Structure as Self-Supervision: Phase-Equivariant Latent Dynamics for Cyclic Biosignals

The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a particularly well-defined setting in which to exploit it. We derive a phase-equivariant self-supervised objective and introduce Winder, a joint-embedding architecture that organises representations into phase-invariant coordinates and phase-rotating harmonic subspaces. Its transport operator is fixed and closed-form, derived from the cycle's geometry rather than learned, and adds no parameters. Evaluated on PTB-XL under a frozen linear-probe protocol, Winder attains diagnostic accuracy within the range reported by state-of-the-art self-supervised methods at a ~1 M parameter footprint, while exhibiting phase-equivariant latent geometry. Winder also improves single-lead dropout robustness at identical inference cost. These results show that measured physiological coordinates can provide a testable dynamics prior for reliable health world models, without increasing deployed model capacity–––unlocking fully edge, high-throughput deployment for O(ms) condition detection capabilities.