Biometric Authentication Using the Unique Characteristics of the ECG Signal

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dc.contributor.authorRepčík, Tomášcs
dc.contributor.authorPoláková, Veronikacs
dc.contributor.authorWaloszek, Vojtěchcs
dc.contributor.authorNohel, Michalcs
dc.contributor.authorSmital, Lukášcs
dc.contributor.authorVítek, Martincs
dc.contributor.authorKolář, Radimcs
dc.coverage.issue1cs
dc.coverage.volume47cs
dc.date.issued2020-12-28cs
dc.description.abstractECG is a biological signal specific for each person that is hard to create artificially. Therefore, its usage in biometry is highly investigated. It may be assumed that in the future, ECG for biometric purposes will be measured by wearable devices. Therefore, the quality of the acquired data will be worse compared to ambulatory ECG. In this study, we proposed and tested three different ECG-based authentication methods on data measured by Maxim Integrated wristband. Specifically, 29 participants were involved. The first method extracted 22 time-domain features – intervals and amplitudes from each heartbeat and Hjorth descriptors of an average heartbeat. The second method used 320 features extracted from the wavelet domain. For both methods a random forest was used as a classifier. The deep learning method was selected as the third method. Specifically, the 1D convolutional neural network with embedded feed-forward neural network was used to classify the raw signal of every heartbeat. The first method reached an average false acceptance rate (FAR) 7.11% and false rejection rate (FRR) 6.49%. The second method reached FAR 6.96% and FRR 21.61%. The third method reached FAR 0.57% and FRR 0.00%.en
dc.formattextcs
dc.format.extent1-4cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputing in Cardiology. 2020, vol. 47, issue 1, p. 1-4.en
dc.identifier.doi10.22489/CinC.2020.321cs
dc.identifier.issn2325-887Xcs
dc.identifier.orcid0000-0002-2679-2160cs
dc.identifier.orcid0000-0003-1526-4626cs
dc.identifier.orcid0000-0002-8059-1087cs
dc.identifier.orcid0000-0002-0469-6397cs
dc.identifier.other166055cs
dc.identifier.researcheridGQJ-8442-2022cs
dc.identifier.researcheridH-8505-2014cs
dc.identifier.researcheridD-3351-2014cs
dc.identifier.researcheridC-8547-2014cs
dc.identifier.scopus57222009745cs
dc.identifier.scopus54960986600cs
dc.identifier.scopus35767287500cs
dc.identifier.urihttp://hdl.handle.net/11012/196702
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofComputing in Cardiologycs
dc.relation.urihttp://www.cinc.org/archives/2020/pdf/CinC2020-321.pdfcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2325-887X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectECGen
dc.subjectbiometric authenticationen
dc.subjectHjorth descriptorsen
dc.subjectwavelet domain featuresen
dc.subject1D convolutional neural networken
dc.titleBiometric Authentication Using the Unique Characteristics of the ECG Signalen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-166055en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:46en
sync.item.modts2025.01.17 18:42:55en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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