Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/8873
DC FieldValueLanguage
dc.contributor.authorSimjanoska, Monikaen_US
dc.contributor.authorGjoreski, Martinen_US
dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.contributor.authorKoteska, Bojanaen_US
dc.contributor.authorGams, Matjažen_US
dc.contributor.authorTasič, Jurijen_US
dc.date.accessioned2020-09-04T13:56:38Z-
dc.date.available2020-09-04T13:56:38Z-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/8873-
dc.publisherSCITEPRESS - Science and Technology Publicationsen_US
dc.titleECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learningen_US
dc.relation.conferenceProceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologiesen_US
dc.identifier.doi10.5220/0006538202820292-
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
Show simple item record

Page view(s)

95
checked on Jul 24, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.