Advances in Electrocardiogram‐Based Non‐Invasive Blood Glucose Monitoring Technology

Blood glucose monitoring is fundamental to diabetes management, yet traditional invasive methods are limited by patient discomfort and infection risks. In recent years, electrocardiogram (ECG), a conventional tool for cardiovascular assessment, has gained attention as a prospective method for non-invasive blood glucose monitoring. The underlying principle is that glycemic fluctuations can modulate autonomic nervous system activity, thereby influencing cardiac electrophysiology and leading to alterations in ECG waveforms. Researchers have investigated the link using machine learning, deep learning or multimodal fusion models to estimate blood glucose levels. This review outlines the physiological basis and recent advances in ECG-based non-invasive glucose monitoring while evaluating clinical challenges and future directions.