A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks

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dc.authoridYucedag, Ibrahim/0000-0003-2975-7392
dc.authorwosidYucedag, Ibrahim/ABI-4140-2020
dc.contributor.authorSenturk, Umit
dc.contributor.authorPolat, Kemal
dc.contributor.authorYucedag, Ibrahim
dc.date.accessioned2021-12-01T18:47:44Z
dc.date.available2021-12-01T18:47:44Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractCardiovascular diseases (CVD) have become the most important health problem of our time. High blood pressure, which is cardiovascular disease, is a risk factor for death, stroke, and heart attack. Blood pressure measurement is commonly used to limit blood flow in the arm or wrist, with the cuff. Since blood pressure cannot be measured continuously in this method, the dynamics underlying blood pressure cannot be determined and are inefficient in capturing symptoms. This paper aims to perform blood pressure estimation using Photoplethysmography (PPG) and Electrocardiography (ECG) signals that do not obstruct the vascular access. These signals were filtered and segmented synchronously from the R interval of the ECG signal, and chaotic, time, and frequency domain features were subtracted, and estimation methods were applied. Different methods of machine learning in blood pressure estimation are compared. Dynamic learning methods such as Recurrent Neural Network (RNN), Nonlinear Autoregressive Network with Exogenous Inputs Neural Networks NARX-NN and Long-Short Term Memory Neural Network (LSTM-NN) used. Estimation results have been evaluated with performance criteria. Systolic Blood Pressure (SBP) error mean +/- standard deviation = 0.0224 +/- (2.211), Diastolic Blood Pressure (DBP) error mean +/- standard deviation = 0.0417 +/- (1.2193) values have been detected in NARX artificial neural network. The blood pressure estimation results are evaluated by the British Hypertension Society (BHS) and American National Standard for Medical Instrumentation ANSI/AAMI SP10: 2002. Finding the most accurate and easy method in blood pressure measurement will contribute to minimizing the errors. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipDuzce University, Scientific Research and Projects UnitDuzce University [2018.07.02.878]en_US
dc.description.sponsorshipThis paper has been supported by Duzce University, Scientific Research and Projects Unit with the Project number (2018.07.02.878).en_US
dc.identifier.doi10.1016/j.apacoust.2020.107534
dc.identifier.issn0003-682X
dc.identifier.issn1872-910X
dc.identifier.scopus2-s2.0-85088659757en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.apacoust.2020.107534
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10362
dc.identifier.volume170en_US
dc.identifier.wosWOS:000565374000044en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofApplied Acousticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNARX-NNen_US
dc.subjectRNNen_US
dc.subjectCuffless blood pressureen_US
dc.subjectLSTM-NNen_US
dc.subjectDynamic Neural Networksen_US
dc.subjectPulse Transit-Timeen_US
dc.subjectWave-Formen_US
dc.subjectPhotoplethysmographyen_US
dc.subjectHypertensionen_US
dc.subjectRegressionen_US
dc.subjectDiseaseen_US
dc.titleA non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networksen_US
dc.typeArticleen_US

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