Wearable Microwave Medical Sensing for Stroke Classification and Localization: A Space-Division-Based Decision-Tree Learning Method

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this article, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (RUE) (i.e., the head). The space division is enabled by the generalized scattering matrix (GSM) theory and incorporates the brain anatomy. Then a novel decision-tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision-tree also results in rapid stroke localization. To verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial hemorrhage (ICH) stroke and an ischemic stroke (IS) with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.