Continuous and Wireless Monitoring of Myocardial Dynamics Using Biocompatible Magneto-Viscoelastic Patches

We report a machine learning (ML)-augmented magnetic sensing system for real-time, wireless monitoring of cardiac kinematics during recovery following surgery. While conventional monitoring focuses on electrical activity, it overlooks mechanical performance and pumping efficiency. To bridge this diagnostic gap, we developed a biocompatible magnetic particle matrix (MPM) patch incorporating biocompatible ferromagnetic α″-Fe₁₆N₂ microparticles. By integrating this patch onto the epicardium during surgery, our platform enables passive magnetic readout and analysis of epicardial motion without implanted electronics. The system utilizes an external magnetometer to sense 3D magnetic field vectors generated during cardiac contraction and relaxation. By employing a ML-based regression model, we translate dynamic magnetic field variations into precise 3D spatial coordinates to reconstruct the heart's motion. Beyond positional tracking, this framework enables extraction of clinical-grade myocardial velocity profiles, delivering real-time assessment of cardiac health. In vivo studies in rodent models demonstrate the patch resolves cardiac mechanical motion and quantifies acute diastolic dysfunction, revealing pathological shifts undetected by surface ECG. Furthermore, the absence of adverse tissue responses and negligible systemic toxicity after 12 weeks of implantation underscores the biocompatibility of the magnetic particles. These findings establish a foundation for untethered monitoring of cardiac kinematics and suggest an approach for tracking internal organs.