Classification based on the self-organization of child patients with developmental dysphasia

Abstract

Involvement of mathematical and engineering methods in medicine makes it possible to perform research into processes in the human body by non-invasive methods. Our team cooperates with neurologists in the domain of developmental dysphasia. We search for correlations between the results of EEG, magnetic resonance (MR) tractography, speech signal analysis, clinical speech therapy and psychology. Our aim is to verify a hypothesis of the possibility of classifying and visual representing changes in pathological speech by means of artificial neural networks. This contribution concentrates on one part of this research: disordered children's speech analysis and results from MR tractography. We try to divide the patients into three groups according to disorder relevance. For classification, we use PCA and SSOM. Evaluation of the results and preparation of a software pack with a user-friendly interface can facilitate the emergence of disease monitoring and improve the quality of therapy. © 2013 Springer-Verlag Berlin Heidelberg.