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Proceedings Paper

Vital signs monitoring using fuzzy logic rules
Author(s): Oleg A. Khorozov; Iurii V. Krak; Veda S. Kasianiuk; Małgorzata Szatkowska; Kalamkas Begaliyeva
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Paper Abstract

The methods of machine learning for real-time detection of abnormal values of the patient's vital signs are considered. The aim is to assess the risk of the disease with worsening of the patient's condition. The system is designed to monitor patients using expert assessments that are included in fuzzy logic rules to compare patient vitals signs with disease risk assessment. Deviation of values from the norm is identified as an "abnormal" class in order to determine the reasons for the worsening of the patient's condition. The integrated platform "m-Health" system for decision making with feedback control allows the patient to be mobile and their vital signs are mapping in the current mode.

Paper Details

Date Published: 1 October 2018
PDF: 6 pages
Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108083I (1 October 2018); doi: 10.1117/12.2501585
Show Author Affiliations
Oleg A. Khorozov, Glushkov Institute of Cybernetics (Ukraine)
Iurii V. Krak, Glushkov Institute of Cybernetics (Ukraine)
Taras Shevchenko National Univ. of Kyiv (Ukraine)
Veda S. Kasianiuk, Taras Shevchenko National Univ. of Kyiv (Ukraine)
Małgorzata Szatkowska, Lublin Univ. of Technology (Poland)
Kalamkas Begaliyeva, Al-Farabi Kazakh National Univ. (Kazakhstan)


Published in SPIE Proceedings Vol. 10808:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
Ryszard S. Romaniuk; Maciej Linczuk, Editor(s)

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