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Filist SA, Al-Kasasbeh RT, Shatalova OV, Aikeyeva AA, Al-Habahbeh OM, Alshamasin MS, Alekseevich KN, Khrisat M, Myasnyankin MB, Ilyash M. Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology. Comput Methods Biomech Biomed Engin 2023; 26:1400-1418. [PMID: 36305552 DOI: 10.1080/10255842.2022.2117551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/20/2022] [Accepted: 08/22/2022] [Indexed: 11/03/2022]
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.
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Affiliation(s)
- 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 Ilyash
- National Research University of Information Technologies, Mechanics and Optics (ITMO University), Saint-Petersburg, Russian Federation
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