ECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks

dc.contributor.authorHanılcı, Ayca
dc.contributor.authorGürkan, Hakan
dc.date.accessioned2026-02-08T15:03:28Z
dc.date.available2026-02-08T15:03:28Z
dc.date.issued2019
dc.departmentBursa Teknik Üniversitesi
dc.description181N14
dc.description.abstractIn this paper, an ECG biometric identification method, based on a two-dimensional convolutional neural network, is introduced for biometric applications. The proposed model includes two-dimensional convolutional neural networks that work parallel and receive two different sets of 2-dimensional features as input. First, ACDCT features and cepstral properties are extracted from overlapping ECG signals. Then, these features are transformed from one-dimensional representation to two-dimensional representation by matrix manipulations. For feature learning purposes, these two-dimensional features are given to the inputs of the proposed model, separately. Finally, score level fusion is applied to identify the user. Our experimental results show that the proposed biometric identification method achieves an accuracy of %88.57 and an identification rate of 90.48% for 42 persons.
dc.description.sponsorshipBursa Technical University
dc.identifier.doi10.38088/jise.559236
dc.identifier.endpage22
dc.identifier.issn2602-4217
dc.identifier.issue1
dc.identifier.startpage11
dc.identifier.urihttps://doi.org/10.38088/jise.559236
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4133
dc.identifier.volume3
dc.language.isoen
dc.publisherBursa Teknik Üniversitesi
dc.relation.ispartofJournal of Innovative Science and Engineering
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectEngineering
dc.subjectMühendislik
dc.titleECG Biometric Identification Method based on Parallel 2-D Convolutional Neural Networks
dc.typeArticle

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