A novel cuffless blood pressure prediction: Uncovering new features and new hybrid ml models

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dc.authorid0000-0001-8461-1404en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0001-9610-9550en_US
dc.contributor.authorNour, Majid
dc.contributor.authorPolat, Kemal
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorArıcan, Murat
dc.date.accessioned2023-09-04T10:43:19Z
dc.date.available2023-09-04T10:43:19Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThis paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R-2, mean square error (MSE), and the mean absolute error (MAE). The obtained RMSE was 4.277 for systolic blood pressure (SBP) values using the Matern 5/2 Gaussian process regression model. The obtained RMSE was 2.303 for diastolic blood pressure (DBP) values using the rational quadratic Gaussian process regression model. The results of this study have shown that the proposed feature extraction and regression models can predict cuffless blood pressure with reasonable accuracy. This study provides a novel approach for predicting cuffless blood pressure and can be used to develop more accurate models in the future.en_US
dc.identifier.citationNour, M., Polat, K., Şentürk, Ü., & Arıcan, M. (2023). A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models. Diagnostics, 13(7), 1278.en_US
dc.identifier.doi10.3390/diagnostics13071278
dc.identifier.endpage18en_US
dc.identifier.issn2075-4418
dc.identifier.issue7en_US
dc.identifier.pmid37046499en_US
dc.identifier.scopus2-s2.0-85152529155en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.3390/diagnostics13071278
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11650
dc.identifier.volume13en_US
dc.identifier.wosWOS:000969576500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorŞentürk, Ümit
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHypertensionen_US
dc.subjectPPGen_US
dc.subjectBlood Pressure Predictionen_US
dc.subjectCuffless Blood Pressureen_US
dc.subjectRegressionen_US
dc.subjectMore Accurate Modelsen_US
dc.titleA novel cuffless blood pressure prediction: Uncovering new features and new hybrid ml modelsen_US
dc.typeArticleen_US

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