| Abstrakt: |
Microwave systems for prehospital stroke classification are currently being developed. In the future, these systems should enable rapid recognition of the type of stroke, shorten the time to start treatment, and thus significantly improve the prognosis of patients. In this study, we realized a realistic and reconfigurable 3D human head phantom for the development, testing, and validation of these newly developed diagnostic methods. The phantom enables automated and reproducible measurements for different positions of the stroke model. The stroke model itself is also interchangeable, so measurements can be made for different types, sizes, and shapes of strokes. Furthermore, an extensive series of measurements was performed at a frequency of 1 GHz, and an SVM classification algorithm was deployed, which successfully identified ischemic stroke in 80% of the corresponding measured data. If similar classification accuracy could be achieved in patients, it would lead to a dramatic reduction in the consequences of strokes. [ABSTRACT FROM AUTHOR] |
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