A real-time FPGA-based hardware module for adaptive noise correction in cardiac bioelectronic systems: Toward robust signal conditioning for implantable biosensors

Cardiovascular diseases are a leading cause of mortality worldwide. Recent efforts focus on hardware-based emulation of cardiac pacemaker cells using differential equation models for the electrophysiological activity of sinoatrial (SA) and Purkinje Fiber (PF) cells. While these models provide high signal fidelity, they assume ideal, noise-free conditions, ignoring the inevitable presence of noise in biological and digital biosensor systems, including implantable bioelectronic platforms. This study presents a hardware module for real-time noise correction in cardiac biosignals, designed for processing data from various sources by identifying disturbances and applying corrective algorithms. It examines noise correction using two filtering algorithms: Least Mean Squares (LMS) and Unscented Kalman Filter (UKF), highlighting the specific advantages and applications of each. Additionally, the impact of several optimization techniques on the results of the hardware implementation, including accuracy, power consumption, resource usage, and frequency, is analyzed. This work can serve as an initial step toward integrating adaptive noise correction methods into future implantable or diagnostic cardiac biosensing systems, enhancing the robustness and reliability of next-generation bioelectronic interfaces.