BIOMATERIAL IMPEDANCE MODELS FOR THE FORMATION OF DESCRIPTORS IN INTELLIGENT SYSTEMS FOR THE DIAGNOSIS OF INFECTIOUS DISEASES
As a result of the study, fundamentally new results have been obtained, which make it possible to create intelligent decision support systems for the diagnosis of infectious diseases. A bioimpedance analysis model has been created, based on multifrequency bioimpedance measurement, which allows decomposition of biomaterial impedance into structural elements. On the basis of the proposed model, descriptors were formed, intended for classifiers, performed on trained neural networks. To obtain descriptors, multifrequency sounding of the biomaterial was carried out, on the basis of which Cole's graphs were constructed. Using iterative algorithms and these graphs, Voigt models of the biomaterial impedance were obtained. The parameters of these models are used as descriptors for the trained classifiers. On the basis of multifrequency sensing, algorithms for differential control of tissue impedance and fluid impedance have been obtained, which will make it possible to obtain new decisive rules for diagnosing pathological conditions of the body (cardiovascular, infectious and oncological diseases). In modern Russian healthcare, the task of long-term monitoring of a person's condition is almost always associated with either his hospitalization, which is unacceptable both for the working-age population and in some cases for sick people, or with the rent of expensive monitoring systems for a period not exceeding, as a rule, 24 hours, which is not always enough for diagnostic tasks.