Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology.

Journal: Computer methods in biomechanics and biomedical engineering
PMID: 36305552

Abstract

Currently, intelligent systems built on a multimodal basis are used to study the functional state of living objects. Its essence lies in the fact that a decision is made through several independent information channels with the subsequent aggregation of these decisions. The method of forming descriptors for classifiers of the functional state of the respiratory system includes the study of the spectral range of the respiratory rhythm and the construction of the wavelet plane of the monitoring electrocardiosignal overlapping this range. Then, variations in the breathing rhythm are determined along the corresponding lines of the wavelet plane. Its analysis makes it possible to select slow waves corresponding to the breathing rhythm and systemic waves of the second order. Analysis of the spectral characteristics of these waves makes it possible to form a space of informative features for classifiers of the functional state of the respiratory system. To construct classifiers of the functional state of the respiratory system, hierarchical classifiers were used. As an example, we took a group of patients with pneumonia with a well-defined diagnosis (radiography, X-ray tomography, laboratory data) and a group of volunteers without pulmonary pathology. The diagnostic sensitivity of the obtained classifier was 76% specificity with a diagnostic specificity of 82%, which is comparable to the results of X-ray studies. It is shown that the corresponding lines of the wavelet planes are correlated with the respiratory system and, using their Fourier analysis, descriptors can be obtained for training neural network classifiers of the functional state of the respiratory system.

Authors

  • Sergey Alekseevich Filist
    Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia.
  • Riad Taha Al-Kasasbeh
    Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan.
  • Olga Vladimirovna Shatalova
    Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia.
  • Altyn Amanzholovna Aikeyeva
    Department of Radio Engineering, Electronics and Telecommunications, Faculty of Physics and Technology, L.N. Gumilyov Eurasian National University, Nur-Sultan, Kazakhstan.
  • Osama M Al-Habahbeh
    Mechatronics Engineering Department, The University of Jordan, Amman, Jordan.
  • Mahdi Salman Alshamasin
    Department of Mechatronics Engineering, Al-Balqa Applied University, Faculty of Engineering Faculty, Amman, Jordan.
  • Korenevskiy Nikolay Alekseevich
    Department of Biomedical Engineering, Faculty of Fundamental and Applied Informatics, Southwestern State University, Kursk, Russia.
  • Mohammad Khrisat
    Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan.
  • Maksim Borisovich Myasnyankin
    Department of Mechatronics Engineering, School of Engineering, University of Jordan, Amman, Jordan.
  • Maksim Ilyash
    Mechanics and Optics, Saint-Petersburg National Research University of Information Technologies, Russian Federation.

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