On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2031
• Main Authors: Pokorny, Tomas, Vrba, Jan, Fiser, Ondrej, Vrba, David, Drizdal, Tomas, Novak, Marek, Tosi, Luca, Polo, Alessandro, Salucci, Marco
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2023; MDPI
• Subjects: Accuracy; Algorithms; Antennas; Antennas (Electronics); Brain; brain stroke; Classification; Datasets; Dielectric properties; Discriminant analysis; Geometry; Humans; Hypotheses; Laboratories; Machine learning; microwave devices; Microwaves; numerical model; Patients; Simulation; Stroke; Stroke - diagnosis; Support Vector Machine; Support vector machines; SVM; Tissues; microwave devices; SVM; numerical model; brain stroke
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23042031

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.