Differentiation of brain stroke type by using microwave-based machine learning classification

Brain stroke is an emergency condition that is caused either by a blocked or a burst vessel (ischemic or hemorrhagic stroke, respectively), resulting into abnormal blood supply into the affected area, with severe and sometimes deadly consequences. Early diagnosis of the stroke is vital, as the time that passes from the offset of the symptoms is strictly correlated with the survival of the patient and the treatment success. Concurrently, it is also essential to successfully identify the type of the stroke as treating a hemorrhagic stroke (h-stroke) as an ischemic (i-stroke) stroke could be lethal for the patient [1] . Therefore, there is an increased need for a portable and low-cost diagnostic method that will detect and differentiate the type of the brain stroke as early as possible.