DPIKAN: Decomposed Physics-Informed KANs for Physiological Signal Representation Learning

Anonymous (ICLR 2027 double-blind)

Summary

Compact physiology-informed framework: (1) multi-scale waveform modeling — decomposes signals into fast- and slow-varying components handled by a dual-order KAN; (2) cardiovascular-descriptor guidance — a physics-informed KAN integrates descriptors and regularizes how predictions respond to them. Evaluated for BP on four datasets spanning PPG, bioimpedance (BioZ) and mmWave. Introduces Ring-HGCPT, a smart-ring PPG dataset recorded during controlled cardiovascular perturbations; leave-one-subject-out there: mean error 1.08 / 0.09 mmHg (SBP/DBP), error SD 5.41 / 4.81 mmHg.

Reported benchmarks