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Title: | Development of the decision support system in treatment of arterial hypertension application of artificial neural networks for evaluation of heart rate variability signals |
Authors: | Dolganov, A. Kublanov, V. Belo, D. Gamboa, H. |
Issue Date: | 2018 |
Publisher: | SciTePress |
Citation: | Dolganov A. Development of the decision support system in treatment of arterial hypertension application of artificial neural networks for evaluation of heart rate variability signals / A. Dolganov, V. Kublanov, D. Belo, H. Gamboa. — DOI 10.5220/0006728903250331 // BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. — 2018. — Iss. 4. — P. 325-331. |
Abstract: | The article presents first step of the decision support system development in treatment of arterial hypertension. Results of arterial hypertension diagnostic task by short-term signals of heart rate variability are presented. The tilt test study was used as the functional load. The peculiarity of this work is application of neural networks for this task. The different number of hidden layers in the neural networks and different number of neurons in hidden layers were tested in this study. The classification accuracy of the neural networks was compared with those of simple machine learning classifiers. The following steps of the decision support system development are declared. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. |
Keywords: | ARTERIAL HYPERTENSION ARTIFICIAL NEURAL NETWORKS CLASSIFICATION HEART RATE VARIABILITY ARTIFICIAL HEART BIOMEDICAL ENGINEERING BIOMIMETICS HEART LEARNING SYSTEMS MACHINERY MULTILAYER NEURAL NETWORKS SIGNAL PROCESSING ARTERIAL HYPERTENSION CLASSIFICATION ACCURACY HEART RATE VARIABILITY HEART RATE VARIABILITY SIGNALS HIDDEN LAYERS SHORT TERM TEST STUDY DECISION SUPPORT SYSTEMS |
URI: | http://elar.urfu.ru/handle/10995/92406 |
Access: | info:eu-repo/semantics/openAccess |
SCOPUS ID: | 85051731137 |
PURE ID: | 7761212 |
ISBN: | 9789897582790 |
DOI: | 10.5220/0006728903250331 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
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File | Description | Size | Format | |
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10.5220-0006728903250331.pdf | 407,18 kB | Adobe PDF | View/Open |
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