A novel feature ranking algorithm for biometric recognition with PPG signals

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-4380-9075en_US
dc.contributor.authorKavsaoglu, Ahmet Reşit
dc.contributor.authorPolat, Kemal
dc.contributor.authorBozkurt, Mehmet Recep
dc.date.accessioned2021-06-23T19:35:54Z
dc.date.available2021-06-23T19:35:54Z
dc.date.issued2014
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThis study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compbiomed.2014.03.005
dc.identifier.endpage14en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid24705467en_US
dc.identifier.scopus2-s2.0-84897520456en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2014.03.005
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7889
dc.identifier.volume49en_US
dc.identifier.wosWOS:000337214500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiometricsen_US
dc.subjectPhotoplethysmography (PPG)en_US
dc.subjectIdentificationen_US
dc.subjectClassificationen_US
dc.subjectDerivativesen_US
dc.subjectFeature Extractionen_US
dc.titleA novel feature ranking algorithm for biometric recognition with PPG signalsen_US
dc.typeArticleen_US

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