Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/92406
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://hdl.handle.net/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, проиндексированные в SCOPUS и WoS CC

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