FIELD: medicine.
SUBSTANCE: invention relates to medicine and can be used in practical medicine for non-invasive diagnosis of diseases of the bronchopulmonary system. Three types of audio recordings from the patient are recorded: cough, breathing, speech, discrete integral transformation of audio recordings is performed, the result of which is a set of spectrograms of these audio recordings, additional segmentation of spectrograms into separate fragments with intersections in time is carried out, methods for preprocessing a signal with using ultra-precise linear layers and obtaining a set of feature vectors that are fed to the input of a convolutional neural network for classification with obtaining the generated feature vector at the output, the obtained vectors of signs are combined from the three original audio recordings, the combinations of the obtained vectors are transformed using a linear layer, and a conclusion about the patient's health is formed based on the results obtained.
EFFECT: invention provides automation and simplification of the technological process for the diagnosis of COVID using deep machine learning methods.
5 cl, 10 dwg
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