A novel blood pressure estimation method with the combination of long short term memory neural network and principal component analysis based on PPG signals

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dc.authorid0000-0003-1840-9958
dc.authorid0000-0001-9610-9550
dc.authorid0000-0003-2975-7392
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorPolat, Kemal
dc.contributor.authorYücedağ, İbrahim
dc.date.accessioned2021-06-23T18:57:28Z
dc.date.available2021-06-23T18:57:28Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe worldwide high blood pressure-related mortality rate is increasing. Alternative measurement methods are required for blood pressure measurement. There are similarities between blood pressure and photoplethysmography (PPG) signals. In this study, a novel blood pressure estimation methods based on the feature extracted from the PPG signals have been proposed. First of all, 12-time domain features have extracted from the raw PPG signal. Secondly, raw PPG signals have been applied to Principal Component Analysis (PCA) to obtain 10 new features. The resulting features have been combined to form a hybrid feature set consisting of 22 features. After features extraction, blood pressure values have automatically been predicted by using the Long Short Term Memory Neural Network (LSTM-NN) model. The prediction performance measures including MAE, MAPE, RMSE, and IA values have been used. While the combination of 12-time domain features from PPG signals and LSTM has obtained the MAPE values of 0,0547 in the prediction of blood pressures, the combination of 10-PCA coefficients and LSTM has achieved the MAPE value of 0,0559. The combination model of all features (22) and LSTM has obtained the MAPE values of 0,0488 in the prediction of blood pressures. The achieved results have shown that the proposed hybrid model based on combining PCA and LSTM is very promising in the prediction of blood pressure from PPG signals. In the future, the proposed hybrid method can be used as a wearable device in the measurement of blood pressure without any calibration.en_US
dc.description.sponsorshipDüzce Üniversitesi: 2018.07.02.878en_US
dc.description.sponsorshipAcknowledgment. This paper has been supported by Duzce University Scientific Research and Projects Unit with the Project number 2018.07.02.878.en_US
dc.identifier.doi10.1007/978-3-030-36178-5_75
dc.identifier.endpage876en_US
dc.identifier.isbn9783030361778
dc.identifier.issn2367-4512
dc.identifier.scopus2-s2.0-85083464271en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage868en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-36178-5_75
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5197
dc.identifier.volume43en_US
dc.identifier.wosWOS:000678771000075en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofLecture Notes on Data Engineering and Communications Technologiesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLSTM-NNen_US
dc.subjectPCAen_US
dc.subjectPPG Measurementen_US
dc.subjectWearable Blood Pressureen_US
dc.titleA novel blood pressure estimation method with the combination of long short term memory neural network and principal component analysis based on PPG signalsen_US
dc.typeBook Chapteren_US

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Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
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