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AuthorIbtehaz, Nabil
AuthorMahmud, Sakib
AuthorChowdhury, Muhammad E. H.
AuthorKhandakar, Amith
AuthorSalman Khan, Muhammad
AuthorAyari, Mohamed A.
AuthorTahir, Anas M.
AuthorRahman, M. S.
Available date2023-04-17T06:57:41Z
Publication Date2022
Publication NameBioengineering
ResourceScopus
URIhttp://dx.doi.org/10.3390/bioengineering9110692
URIhttp://hdl.handle.net/10576/41937
AbstractCardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.
SponsorThis work was partially supported by QUST-2-CENG-2022-711 from Qatar University and NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, and a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
Subjectblood pressure
cuff-less blood pressure
mobile health
Photoplethysmogram (PPG)
regression
TitlePPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms
TypeArticle
Issue Number11
Volume Number9
dc.accessType Open Access


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