КОРЕЛЯЦІЙНА ЕФЕКТИВНІСТЬ РЕАЛІЗАЦІЙ ФУНКЦІОНАЛЬНИХ ШУМІВХАРАКТЕРИНИХ ДЛЯ ПОРУШЕНЬ ДИХАЛЬНОЇ СИСТЕМИ

The study investigates the correlation properties of acoustic signals generated by the functioning of the humanrespiratory system. It is shown that the analysis of noise realizations, particularly through the expansion of informativefeatures, can improve the effectiveness of non-invasive diagnosis of respiratory disorders.The problems of respiratory sound segmentation are outlined, considering the variability of breathing cycles, thepresence of external noise and technical artifacts, as well as individual anatomical differences. It is determined thattraditional amplitude-based signal processing methods are insufficiently effective in such cases, which necessitates theuse of more informative, particularly probabilistic, characteristics. Consequently, the application of sliding statisticalestimations—local mean, variance, and information entropy—is considered, allowing for the localization of respiratorymovements within time windows. Based on these estimations, a correlation analysis was implemented to detect signalperiodicity, which improved the accuracy of identifying individual respiratory phases (breathing movements). During the research, an autocorrelation analysis of respiratory noise amplitudes represented by various statisticalestimations was conducted. It was found that variance and information entropy estimations provide the most effectivesegmentation of respiratory movements; however, the entropic representation is characterized by lower fluctuation ofcorrelation function apertures and does not require additional scaling, which simplifies hardware implementation.Experimental studies have shown that the use of probabilistic estimations—specifically variance and informationentropy—enables more accurate identification of specific types of functional respiratory disorders. For each type ofdisorder, reference signals were formed, exhibiting a high degree of cross-correlation with experimental realizations.Moreover, a comparison of cross-correlation results showed that the representation of signals using slidinginformation entropy estimations provides approximately twice the resolution between homogeneous and heterogeneousdisorders compared to the variance-based approach.The presented results may have practical significance in the development of digital diagnostic systems based onmicrocontrollers and computer-based signal processing tools.