Bioimpedance mapping models based on equivalent multipole networks in intelligent lung disease support systems

Purpose of research . Domestic and foreign studies have proven that the electrical impedance of biotissue can be used as a predictor of chest diseases. However, this method needs to be improved, since it has limitations in resolution, as well as in the imperfection of bioimpedance models required to form input vectors for machine learning systems. Methods . The presented study proposes a hybrid model of an intelligent bioimpedance research system that uses both a machine learning model and the intelligence of a specialist who analyzes the image of an anatomical object obtained from the results of electrical impedance mapping. To obtain an image, a multi-pole model of biomaterial impedance is used. To construct such a model, direct and inverse problems were solved. As a result of solving the inverse problem, equations were obtained that allow one to determine the potentials at the nodes of a multi-pole with a known impedance in its links. As a result of solving the inverse problem, the impedances of the multipole links were determined with known potentials at its poles. Results . During the study, a program was developed for constructing heat maps of the chest impedance distribution. The program is a powerful tool for visualizing the impedance distribution over the chest. It combines a convenient graphical interface, modules for mathematical data processing and clear visualization of results. The program allows medical professionals to quickly get an idea of the impedance distribution, which can be useful for diagnostics and assessing the current condition of the patient. The flexibility of choosing the interpolation method and the ability to save the results make the program a valuable tool in medical practice. Conclusion . A comprehensive solution is proposed that combines advanced mathematical data processing methods and modern approaches to creating user interfaces, which provides medical specialists with a powerful tool for analyzing chest impedance data.