Cuff-less continuous blood pressure estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) signals with artificial neural network

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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.authorYücedağ, İbrahim
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T18:51:32Z
dc.date.available2021-06-23T18:51:32Z
dc.date.issued2018
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionAselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netasen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780en_US
dc.description.abstractContinuous blood measurement important information about the health status of the individuals. Conventional methods use a cuff for blood pressure measurement and cannot be measured continuously. In this study, we proposed a system that estimates systolic blood pressure (SP) and diastolic blood pressure (DP) for each heart beat by extracting attributes from ECG and PPG signals. Simultaneous ECG and PPG signals from the PhysioNet Database are pre-processed (denoising, artifact cleaning and baseline wandering) to remove noise and artifacts and segmented into R-R peaks. For each heartbeat, 22-time domain features were extracted from ECG and PPG signals. SP and DP values were estimated by introducing these 22 attributes to the model of Lavenberg-Marquardt artificial neural networks (ANN). Arterial blood pressure (ABP) was also taken from the PhysioNet MIMIC II database along with ECG and PPG signals. ABP signals have been used as targets in the artificial neural network. The system performance has been evaluated by calculating the difference between the estimated ABP values and the actual by the ANN model. The performance value between the predicted SP and actual SP values is -0.14 ± 2.55 (mean ± standard deviation) and the performance value between estimated DP and actual DP values is -0.004 ± 1.6. The obtained results have shown that the proposed model has predicted blood pressure with high accuracy. In this study, SP and DP values can also be measured directly without any calibration in blood pressure estimation. © 2018 IEEE.en_US
dc.description.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404255&tag=1
dc.identifier.doi10.1109/SIU.2018.8404255
dc.identifier.endpage4en_US
dc.identifier.isbn9781538615010
dc.identifier.scopus2-s2.0-85050808204en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1109/SIU.2018.8404255
dc.identifier.urihttps://hdl.handle.net/20.500.12491/3843
dc.identifier.wosWOS:000511448500108en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof26th IEEE Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBlood pressure estimation with Artificial Neural Networken_US
dc.subjectCuffless blood pressure estimationen_US
dc.subjectECGen_US
dc.subjectPPGen_US
dc.subjectEKG
dc.subjectPPG
dc.subjectMaşonsuz Kan Basınç Tahmini
dc.subjectYapay Sinir Ağları İle Kan Basınç Tahmini
dc.titleCuff-less continuous blood pressure estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) signals with artificial neural networken_US
dc.typeConference Objecten_US

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