Blood Pressure Estimation from PPG Signals Using Deep Residual Network with Transfer Learning

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dc.authoridArslan, Ozkan/0000-0003-1949-3688
dc.contributor.authorKoparir, Huseyin Murat
dc.contributor.authorArslan, Ozkan
dc.date.accessioned2024-10-29T17:58:15Z
dc.date.available2024-10-29T17:58:15Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesien_US
dc.description31st IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUL 05-08, 2023 -- Istanbul Tech Univ, Ayazaga Campus, Istanbul, TURKEYen_US
dc.description.abstractIn this study, we present an approach that enables transfer learning-based estimation of systolic and diastolic blood pressure (SBP and DBP) using photoplethysmography (PPG) signal. In the development of BP estimation models, we use the MIMIC II database containing PPG and arterial BP signals. The proposed approach utilizes algorithms based on recurrent neural network architecture with ResNet50, VGG16 and MobileNetV2 deep features. The results show that a high estimation performance is achieved with the selected ResNet50 deep features and bi-directional gated recurrent unit algorithm. Our proposed approach has a mean absolute error and standard deviation (MAE +/- SD) of 5.66 +/- 8.82 and 2.82 +/- 5.60 mmHg for estimating SBP and DBP, respectively. The proposed estimation model satisfies both the BHS and AAMI standards for SBP and DBP. The results demonstrate the effectiveness of the proposed approach to estimate BP, especially in patients at risk for cardiovascular disease and hypertension.en_US
dc.description.sponsorshipIEEE,TUBITAK BILGEM,Turkcellen_US
dc.identifier.doi10.1109/SIU59756.2023.10224052
dc.identifier.isbn979-8-3503-4355-7
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85173551175en_US
dc.identifier.urihttps://doi.org/10.1109/SIU59756.2023.10224052
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14153
dc.identifier.wosWOS:001062571000256en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2023 31st Signal Processing and Communications Applications Conference, Siuen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectphotoplethysmographyen_US
dc.subjectblood pressureen_US
dc.subjectcuff-less and non-invasive measurementen_US
dc.subjecttransfer learningen_US
dc.subjectdeep learningen_US
dc.titleBlood Pressure Estimation from PPG Signals Using Deep Residual Network with Transfer Learningen_US
dc.typeConference Objecten_US

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