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dc.contributor.authorAnkishan, Haydar
dc.date.accessioned2021-02-28T11:45:55Z
dc.date.available2021-02-28T11:45:55Z
dc.date.issued2019
dc.identifier.issn1746-8094en_US
dc.identifier.urihttp://hdl.handle.net/11727/5428
dc.description.abstractThis paper introduces a new hybrid feature vector for revealing the relationship between human voice and heartbeat rate (HBR). Various samples of the sustained vowel /a/ for different HBR have been extracted from a database which is created for this study. A convolutional neural network (CNN)-Regression (R), support vector machines (SVMs)-Regression (R), and multiple linear regression (MLR) are used as regression models. The experimental results show that the percentage of predictions within an acceptable error margin has been obtained as 98.92% for CNN-R, 98.70% for SVMs-R and 96.88% for MLR when Forward Sequential is used as a feature selection algorithm. The results also reveal that the CNN-R (root mean square error (RMSE) =0.3909) has produced better prediction values in estimating HBR than those produced by SVMs-R (RMSE=0.4277) and MLR (RMSE =0.4449). As a result, it is seen that the extracted hybrid feature vector provides a novel relationship between human voice and HBR. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1016/j.bspc.2019.01.015en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature extractionen_US
dc.subjectHeartbeat rateen_US
dc.subjectHuman voice heartbeat rate interactionen_US
dc.titleEstimation of heartbeat rate from speech recording with hybrid feature vector (HFV)en_US
dc.typearticleen_US
dc.relation.journalBIOMEDICAL SIGNAL PROCESSING AND CONTROLen_US
dc.identifier.volume49en_US
dc.identifier.startpage483en_US
dc.identifier.endpage492en_US
dc.identifier.wos000458942500044en_US
dc.identifier.scopus2-s2.0-85059517350en_US
dc.contributor.orcID0000-0002-6240-2545en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergien_US
dc.contributor.researcherIDAAH-4421-2019en_US


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