Energy-driven Microwave Medical Sensing for Post-acute Brain Stroke Monitoring

This paper presents a simple microwave-sensing framework for monitoring post-acute stroke evolution, featuring three key innovations: a low-complexity helmet-like device employing only sixteen antennas in a cross-sectional multi-view architecture, an ad-hoc anthropomorphic multi-tissue head phantom, and an energy-based algorithm designed to detect and track temporal pathological changes. The method interprets the Frobenius norm of the differential scattering matrices as a global energy indicator of dielectric variations, enabling the detection of volumetric stroke evolution. Experimental validation on the multi-tissue phantom demonstrates consistent performance across different progression scenarios, showing that the proposed indicator correlates with clinically relevant centimetric volume changes. These results support the method’s suitability for bedside use and highlight the potential of low-complexity microwave sensing as a practical tool for non-invasive, real-time follow-up of stroke evolution in clinical workflows.