# A Physics-Aware Diffusion Framework for Robust ECG Synthesis Using Mesoscopic Lattice Boltzmann Constraints

## TL;DR

A new AI system uses physics rules to convert wearable pulse data into accurate ECGs, avoiding incorrect or impossible results.

## Contribution

Introduces PhysDiff-LBM, a physics-aware diffusion model with Lattice Boltzmann constraints for robust ECG synthesis.

## Key findings

- The physics-guided AI system generates accurate and medically valid ECGs from pulse data.
- Incorporating fluid dynamics constraints improves signal fidelity and clinical applicability.
- The model outperforms data-driven approaches by adhering to hemodynamic conservation laws.

## Abstract

Smartwatches and fitness bands track our pulse using simple optical sensors, but diagnosing true heart disease requires an electrical electrocardiogram (ECG) typically recorded in hospitals with sticky patches and wires, while artificial intelligence (AI) has been used to translate simple wrist pulse data into clinical ECGs, standard AI often “guesses” the waveforms, creating medically incorrect or physically impossible results. To overcome this, we developed a new AI system that directly embeds the natural physical rules of blood circulation into its learning process. Instead of just learning from data patterns, we taught the AI the actual physical laws of how blood pumps from the heart and flows through blood vessels. Constrained by these natural laws of fluid dynamics, our AI is prevented from making impossible physiological guesses. Our tests show this physics-guided approach successfully turns ordinary pulse data into highly accurate, doctor-ready ECGs, bringing us one step closer to hospital-level heart monitoring directly from everyday wearables.

Cardiovascular disease has become the leading cause of death worldwide, underscoring the urgent need for widespread cardiac monitoring, while the Electrocardiogram (ECG) remains the diagnostic gold standard, the complexity of its acquisition limits its long-term feasibility. In contrast, Photoplethysmography (PPG), ubiquitous in wearable devices, is increasingly adopted due to its accessibility. However, synthesizing ECG from PPG poses an intrinsically ill-posed inverse problem. Existing purely data-driven paradigms often neglect underlying biophysical mechanisms, resulting in a lack of physical constraints and interpretability, which renders them prone to generating non-physiological hallucinations. To address this, we propose PhysDiff-LBM, a novel physics-aware framework that incorporates Lattice Boltzmann hemodynamic constraints into a conditional diffusion model. Employing a dual-stream architecture, our framework captures high-frequency morphological details via a cross-attention-guided diffusion model with region-wise adaptability. Synergistically, we physically regularize the ECG synthesis by leveraging the mesoscopic streaming and collision operators of LBM. By forcing the synthesized waveform gradients to evolve consistently with hemodynamic momentum, this mechanism constrains the model to strictly adhere to the fluid dynamic conservation laws governing pulse wave propagation. Experimental results demonstrate that our method achieves superior signal fidelity and exhibits significant advantages in downstream clinical applications.