Real-time brain stroke detection through a learning-by-examples technique – An experimental assessment

The real-time detection of brain strokes is addressed within the Learning-by-Examples (LBE) framework. Starting from scattering measurements at microwave regime, a support vector machine (SVM) is exploited to build a robust decision function able to infer in real-time whether a stroke is present or not in the patient head. The proposed approach is validated in a laboratory-controlled environment by considering experimental measurements for both training and testing SVM phases. The obtained results prove that a very high detection accuracy can be yielded even though using a limited amount of training data.