Factorizing Predictable Structure and Conditional Variation for Multimodal Cardiovascular Signal Generation

Multimodal cardiovascular signals, such as electrocardiography (ECG), photoplethysmography (PPG), and arterial blood pressure (ABP), provide complementary views of the same physiological process. Generating a missing modality from partial observations is nevertheless underdetermined: deterministic predictors tend to average plausible targets, whereas direct generative models must learn both observation-constrained structure and remaining target variation within a single distribution. We introduce a factorized framework that separates these roles in a shared latent space. Observed modalities are encoded into a global context that captures cross-modal cardiovascular structure and a local condition that retains high-resolution temporal information. A target-specific deterministic anchor first estimates the target structure predictable from the available observations. Conditional flow matching then models the residual relative to this anchor, and the final signal is obtained by decoding the sum of the anchor and a sampled residual. This formulation yields a testable hypothesis: as additional informative modalities are observed, the deterministic anchor should explain more target structure, leaving a smaller and more concentrated residual distribution. A single model supports nine generation tasks formed by single- and dual-modality observations across ECG, PPG, and ABP. We evaluate conditional accuracy, temporal alignment, distributional fidelity, and downstream physiological utility, and analyze how observation completeness affects anchor error, residual energy, and sample dispersion. The results show that separating deterministic prediction from residual generation improves distribution-level fidelity while retaining competitive conditional reconstruction, providing a practical approach to generative modeling under partial multimodal observations.