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dc.contributor.authorLi, Peihao
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2020-09-14T13:49:59Z
dc.date.available2020-09-14T13:49:59Z
dc.date.issued2020-08-28
dc.identifier.citationLi, P., & Laleg-Kirati, T.-M. (2020). Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc44109.2020.9176849
dc.identifier.isbn978-1-7281-1991-5
dc.identifier.issn1557-170X
dc.identifier.doi10.1109/EMBC44109.2020.9176849
dc.identifier.urihttp://hdl.handle.net/10754/665144
dc.description.abstractIn this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9176849/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9176849/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9176849
dc.rightsArchived with thanks to IEEE
dc.titleSchrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure
dc.typeConference Paper
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentEstimation, Modeling and ANalysis Group
dc.conference.date20-24 July 2020
dc.conference.name2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
dc.conference.locationMontreal, QC, Canada
dc.eprint.versionPost-print
kaust.personLi, Peihao
kaust.personLaleg-Kirati, Taous-Meriem
dc.date.published-online2020-08-28
dc.date.published-print2020-07


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