Kompakt mikrovågssystem för detektering av intrakraniella blödningar

Abstract

Stroke is one of the leading causes of death in Sweden and worldwide. Despite the fact that the time between symptom onset, diagnosis and treatment has a major impact on a patient’s chances of survival and recovery, there are currently no effective and reliable methods for prehospital stroke diagnosis. Microwave imaging has been proposed as a potential solution for detecting intracranial hemorrhages, such as stroke, already in the ambulance. However, these measurement systems are limited by factors such as high cost and bulky design. The aim of this project was to further develop and evaluate a compact and cost-effective measurement system designed to achieve performance comparable to that of a Vector Network Analyzer (VNA). To enable the measurement of weaker signals, the system was modified to reduce noise levels and crosstalk between system components. Different configurations using different components were tested and evaluated with respect to signal-to-noise ratio, isolation, and the ability to detect weaker signals. To evaluate the performance of the system and to develop a simple machine learning algorithm, a phantom model of a human head both with and without hemorrhage was constructed. The measurement results showed that the isolation of the measurement system had increased from 30 dB to 100 dB, and that its ability to detect weaker signals had improved. The measurement system and the VNA produced comparable measurement results for phantoms with and without hemorrhage. However, the measurement system still exhibited higher noise levels and lower precision compared to the VNA, indicating that further development is necessary. Although it was difficult to visually distinguish between measurement results from phantoms with and without hemorrhage, the classification algorithm achieved a high accuracy (81%) and an AUC value of 0.963.