Cuff-Less Continuous Blood Pressure Estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) Signals with Artificial Neural Network

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dc.contributor.authorŞentürk, Ümit
dc.contributor.authorYücedağ, İbrahim
dc.contributor.authorPolat, Kemal
dc.date.accessioned2020-05-01T09:11:15Z
dc.date.available2020-05-01T09:11:15Z
dc.date.issued2018
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYen_US
dc.descriptionWOS: 000511448500108en_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.en_US
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univen_US
dc.identifier.isbn978-1-5386-1501-0
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5426
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 26Th Signal Processing And Communications Applications Conference (Siu)en_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectECGen_US
dc.subjectPPGen_US
dc.subjectCuffless blood pressure estimationen_US
dc.subjectblood pressure estimation with Artificial Neural Networken_US
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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