Repository at Hanyang UniversityCOLLEGE OF COMPUTING[E](소프트웨어융합대학)COMPUTER SCIENCE(소프트웨어학부)Articles
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dc.contributor.author강경태-
dc.date.accessioned2019-04-15T04:58:07Z-
dc.date.available2019-04-15T04:58:07Z-
dc.date.issued2015-11-
dc.identifier.citation2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Page. 1743-1744en_US
dc.identifier.issn978-1-4673-6799-8-
dc.identifier.urihttps://ieeexplore.ieee.org/document/7359947-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/101908-
dc.description.abstractWe propose a method of arrhythmia detection based on beat morphology, which offers a new set of features for heartbeat classification. This can be performed by nearest-neighbor search, which we applied to heartbeats from the MIT-BIH arrhythmia database. Our classifier achieved an overall accuracy of 98.18% on 103,923 heartbeats. © 2015 IEEE.en_US
dc.language.isoen_USen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.subjectECGen_US
dc.subjectheartbeat classificationen_US
dc.subjectbeat morphologyen_US
dc.titleHeartbeat Classification for Detecting Arrhythmia using Normalized Beat Mophology Featuresen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/BIBM.2015.7359947-
dc.relation.page1743-1744-
dc.contributor.googleauthorPark, J-
dc.contributor.googleauthorKang, M-
dc.contributor.googleauthorKim, Y-
dc.contributor.googleauthorKang, K-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF COMPUTER SCIENCE-
dc.identifier.pidktkang-
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