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Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/92406
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dc.contributor.authorDolganov, A.en
dc.contributor.authorKublanov, V.en
dc.contributor.authorBelo, D.en
dc.contributor.authorGamboa, H.en
dc.date.accessioned2020-10-20T16:35:38Z-
dc.date.available2020-10-20T16:35:38Z-
dc.date.issued2018-
dc.identifier.citationDolganov 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.en
dc.identifier.isbn9789897582790-
dc.identifier.otherhttps://doi.org/10.5220/0006728903250331pdf
dc.identifier.other2-3good_DOI
dc.identifier.otherd8287ffc-efc6-40b1-b898-907a8db4f1abpure_uuid
dc.identifier.otherhttp://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85051731137m
dc.identifier.urihttp://hdl.handle.net/10995/92406-
dc.description.abstractThe 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.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherSciTePressen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceBIOSIGNALS 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 2018en
dc.subjectARTERIAL HYPERTENSIONen
dc.subjectARTIFICIAL NEURAL NETWORKSen
dc.subjectCLASSIFICATIONen
dc.subjectHEART RATE VARIABILITYen
dc.subjectARTIFICIAL HEARTen
dc.subjectBIOMEDICAL ENGINEERINGen
dc.subjectBIOMIMETICSen
dc.subjectHEARTen
dc.subjectLEARNING SYSTEMSen
dc.subjectMACHINERYen
dc.subjectMULTILAYER NEURAL NETWORKSen
dc.subjectSIGNAL PROCESSINGen
dc.subjectARTERIAL HYPERTENSIONen
dc.subjectCLASSIFICATION ACCURACYen
dc.subjectHEART RATE VARIABILITYen
dc.subjectHEART RATE VARIABILITY SIGNALSen
dc.subjectHIDDEN LAYERSen
dc.subjectSHORT TERMen
dc.subjectTEST STUDYen
dc.subjectDECISION SUPPORT SYSTEMSen
dc.titleDevelopment of the decision support system in treatment of arterial hypertension application of artificial neural networks for evaluation of heart rate variability signalsen
dc.typeConference Paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.5220/0006728903250331-
dc.identifier.scopus85051731137-
local.affiliationUral Federal University, Mira 19, Yekaterinburg, 620002, Russian Federation
local.affiliationLaboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, Caparica, 2892-516, Portugal
local.contributor.employeeDolganov, A., Ural Federal University, Mira 19, Yekaterinburg, 620002, Russian Federation
local.contributor.employeeKublanov, V., Ural Federal University, Mira 19, Yekaterinburg, 620002, Russian Federation
local.contributor.employeeBelo, D., Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, Caparica, 2892-516, Portugal
local.contributor.employeeGamboa, H., Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, Caparica, 2892-516, Portugal
local.description.firstpage325-
local.description.lastpage331-
local.issue4-
local.identifier.pure7761212-
local.identifier.eid2-s2.0-85051731137-
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