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

Learning representations from physiological signals is challenging because heterogeneous sensing modalities provide indirect and modality dependent observations of underlying cardiovascular dynamics. In this work, we propose the Decomposed Physics-Informed Kolmogorov–Arnold Network (DPIKAN), a compact framework that incorporates physiological knowledge through two mechanisms: multi-scale waveform modeling and cardiovascular descriptor guidance. For multi-scale waveform modeling, DPIKAN decomposes physiological signals into fast- and slow-varying components and assigns them component-specific functional capacities through a dual-order KAN. For cardiovascular descriptor guidance, it integrates cardiovascular descriptors through a physics-informed KAN and regularizes how predictions respond to variations in these descriptors. During experiments, we evaluate DPIKAN for blood pressure estimation on four datasets spanning photoplethysmography (PPG), bioimpedance (BioZ), and millimeter-wave (mmWave) sensing. We further introduce Ring-HGCPT, a smart ring PPG dataset collected during controlled cardiovascular perturbations, and evaluate DPIKAN using leave-one-subject-out cross-validation to assess its generalization to unseen participants. Across these heterogeneous sensing settings, DPIKAN achieves competitive performance for BP estimation. On Ring-HGCPT, it achieves mean errors of 1.08 and 0.09 mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively, with corresponding error standard deviations of 5.41 and 4.81 mmHg. Overall, DPIKAN offers a compact, physiology-informed approach to representation learning from heterogeneous physiological signals. We will release our source code and data.