Identification of pathobiomechanical markers of statokinesiograms on the example of neural network identification of a post-stroke state

The purpose of this study is neural network modeling and determination of the parameters of statokinesiograms, which are carriers of useful information about the features of postural regulation, which determined the obtained trajectory of movements of the human center of mass. A technique for obtaining informative markers by identifying clustering centroids based on self-organizing Kohonen neural networks with the Euclidean metric has been developed. Kohonen networks trained without a teacher (that is, without the use of a priori diagnostic information about the state of the subjects) are a powerful and informative method that allows you to obtain not only graphic, but also mathematical markers of health disorders in subjects, to explore the biomechanics of micromovements of the center of pressure based on a flexible mathematical apparatus – neural network cluster analysis. On the example of the identification of the post-stroke state, a neural network analysis of stabilometric data was carried out and a method for identifying pathobiomechanical markers of statokinesiograms was shown, which made it possible to standardize the type affiliation of the groups of subjects. With the help of neural networks, it was possible to identify clusters that are amenable to biomedical interpretation with a reliability of up to 95.9 %, which determines the theoretical significance of the results obtained. Decision trees and a supervised multilayer neural network were also considered. A multilayer neural net-work made it possible to identify markers of health disorders with a probability of 71.9 %.