Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning
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TL;DR - Winder is a self-supervised architecture that explicitly encodes cardiac-cycle symmetry by separating phase-invariant features from phase-rotating harmonic representations. It preserves diagnostically useful ECG information with interpretable latent geometry and roughly 1 million parameters.
- Introduces a phase-equivariant objective tailored to cyclic physiological signals.
- Uses a fixed, closed-form transport operator derived from cardiac-cycle geometry, adding no learned parameters.
- Achieves PTB-XL linear-probe diagnostic accuracy within the reported range of state-of-the-art self-supervised methods.
- Produces phase-equivariant representations tied directly to a measurable physiological quantity.
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Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning
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TL;DR - Winder is a self-supervised architecture that explicitly encodes cardiac-cycle symmetry by separating phase-invariant features from phase-rotating harmonic representations. It preserves diagnostically useful ECG information with interpretable latent geometry and roughly 1 million parameters.
- Introduces a phase-equivariant objective tailored to cyclic physiological signals.
- Uses a fixed, closed-form transport operator derived from cardiac-cycle geometry, adding no learned parameters.
- Achieves PTB-XL linear-probe diagnostic accuracy within the reported range of state-of-the-art self-supervised methods.
- Produces phase-equivariant representations tied directly to a measurable physiological quantity.