A respiratory sounds interpreter adaptated to signal recording devices
The article considers the problem of constructing respiratory sounds interpreter programs that are customized for signal recording device parameters. When using neural network technologies for respiratory sounds classification, technical characteristics of electronic stethoscopes are different. Thus, the paper shows that it is necessary to readjust scales for assessing learning sample objects' features. The paper describes the architecture and features of the software to analyze respiratory sounds records; it highlightes the subsystem for adjusting to a noises recorder, which allows generating an individual set of classification rules for each stethoscope model. These rules are generated by neural-like hierarchical structures; each of them synthesizes concepts of several respiratory sounds classes recorded by the stethoscopes of one model. The class descriptions are created using fuzzy features. The generation of scales for their evaluation is automated. The paper considers the results of system operation with 3 different types of respiratory sounds registration devices (3M Littmann 4100, an original device Pat. 66174 and KoRA-03M1 device) that have different characteristics. 
 The analysis of the results revealed significant differences in the parameters of the classifiers of neuron-like hierarchical structures formed for different respiratory sounds recording devices. However, the generated rules showed similar results 
 (88–93 % accuracy) of respiratory sounds interpreters for each electronic stethoscope model.