Model-based data generation for support vector machine stroke classification

This paper presents a new and efficient method to generate a dataset for brain stroke classification. Exploiting the Born approximation, it derives scattering parameters at antennas locations in a 3-D scenario through a linear integral operator. This technique allows to create a large amount of data in a short time, if compared with the full-wave simulations or measurements. Then, the support vector machine is used to create the classifier model, based on training set data with a supervised method and to classify the test set. The dataset is composed by 9 classes, differentiated for presence, typology and position of the stroke. The algorithm is able to classify the test set with a high accuracy.