Sensors, Vol. 23, Pages 2031: On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification

Sensors, Vol. 23, Pages 2031: On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification Sensors doi: 10.3390/s23042031 Authors: Tomas Pokorny Jan Vrba Ondrej Fiser David Vrba Tomas Drizdal Marek Novak Luca Tosi Alessandro Polo Marco Salucci 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 a...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research