Vital signs monitoring using fuzzy logic rules

Oleg A. Khorozov; Iurii V. Krak; Veda S. Kasianiuk; Małgorzata Szatkowska; Kalamkas Begaliyeva

doi:10.1117/12.2501585

1 October 2018 Vital signs monitoring using fuzzy logic rules

Oleg A. Khorozov, Iurii V. Krak, Veda S. Kasianiuk, Małgorzata Szatkowska, Kalamkas Begaliyeva

Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 108083I (2018) https://doi.org/10.1117/12.2501585
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland

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.

Citation Download Citation

Oleg A. Khorozov, Iurii V. Krak, Veda S. Kasianiuk, Małgorzata Szatkowska, and Kalamkas Begaliyeva "Vital signs monitoring using fuzzy logic rules", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108083I (1 October 2018); https://doi.org/10.1117/12.2501585

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