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dc.contributor.authorWaili, Tuerxunen_US
dc.contributor.authorNor, Rizal Mohden_US
dc.contributor.authorSidek, Khairul Azamien_US
dc.contributor.authorRahman, Abdul Wahab Bin Abdulen_US
dc.contributor.authorGüven, Gökhanen_US
dc.date.accessioned2019-03-17T22:00:01Z
dc.date.available2019-03-17T22:00:01Z
dc.date.issued2018
dc.identifier.citationWaili, T., Nor, R. M., Sidek, K. A., Rahman, A. W. B.A. & Güven, G. (2018). Real time electrocardiogram identification with multi-modal machine learning algorithms. 2nd International Conference of Reliable Information and Communication Technology (IRICT), 5, 459-466. doi:10.1007/978-3-319-59427-9_48en_US
dc.identifier.isbn9783319594262
dc.identifier.isbn9783319594279
dc.identifier.issn2367-4512
dc.identifier.urihttps://hdl.handle.net/11729/1468
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-59427-9_48
dc.description.abstractWeaknesses in conventional identification technologies such as identification cards, badges and RFID tags prompts attention to biometric form of identification. Biometrics like voice, brain signal and finger print are unique human traits that can be used for identification. In this paper we present an identification system based on Electrocardiogram (heart signal). There is a considerable number of research in the past with high accuracy for identification , however, most ignore the practical time required to identify an individual. In this study, we explored a more practical approach in identification by reducing the number of time required for identification. We explore ways to identity a person within 3-4 s using just 5 heart beats. We extracted few reliable features from each QRS complexes, combined effort of three algorithms to achieve 96% accuracy. This approach is more suitable and practical in real time applications where time for identification is important.en_US
dc.language.isoengen_US
dc.publisherSpringer International Publishing AGen_US
dc.relation.isversionof10.1007/978-3-319-59427-9_48
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSVMen_US
dc.subjectRandom foresten_US
dc.subjectLogistic regressionen_US
dc.subjectQRS complexen_US
dc.subjectECG biometricen_US
dc.subjectIdentificationen_US
dc.titleReal time electrocardiogram identification with multi-modal machine learning algorithmsen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisher's Versionen_US
dc.relation.journal2nd International Conference of Reliable Information and Communication Technology (IRICT)en_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.contributor.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.identifier.volume5
dc.identifier.startpage459
dc.identifier.endpage466
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorGüven, Gökhanen_US
dc.relation.indexWOSen_US
dc.relation.indexScopusen_US
dc.relation.indexConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.description.wosidWOS:000432202300048


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