The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring
Open accessAbstract
One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010–2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures






Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“it is less suitable for use in wearable devices due to the need for a cuff”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [19]
In the citing paper
“Many algorithms are known where systolic and diastolic BP are estimated from the shape of the PPG curve [55,102].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [102]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global burden of hypertension: analysis of worldwide data
Kearney, P. M., Whelton, M., Reynolds, K., Muntner, P., Whelton, P. K., & He, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17741-1 · Cited by 8,698
Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy
Hutcheon, J. A., Lisonkova, S., & Joseph, K. S. (2011) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2011.01.006 · Cited by 1,056
The use of photoplethysmography for assessing hypertension
Elgendi, M., Fletcher, R., Liang, Y., Howard, N., Lovell, N. H., Abbott, D., Lim, K., & Ward, R. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0136-7 · Cited by 625
Self-citationWhite, W. B., Berson, A., Robbins, C., Jamieson, M. J., Prisant, L. M., Roccella, E. J., & Sheps, S. G. (1993) · Hypertension · doi:10.1161/01.hyp.21.4.504 · Cited by 382
Monte-Moreno, E. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.05.001 · Cited by 311
An optimal filter for short photoplethysmogram signals
Liang, Y., Elgendi, M., Chen, Z., & Ward, R. (2018) · Scientific Data · doi:10.1038/sdata.2018.76 · Cited by 242
Self-citationSmartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology
Hong, L., Yang, D., Barszczyk, A., Vempala, N. N., Wei, J., Wu, S. J., Zheng, P. P., Fu, G., Lee, K., & Feng, Z. (2019) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.119.008857 · Cited by 242
Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method
Chandrasekhar, A., Kim, C., Naji, M. B., Natarajan, K., Hahn, J., & Mukkamala, R. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aap8674 · Cited by 208
Ding, X., Zhao, N., Yang, G., Pettigrew, R. I., Lo, B. P. L., Miao, F., Li, Y., Liu, J., & Zhang, Y. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2620995 · Cited by 184
Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification
Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Biosensors · doi:10.3390/bios8040101 · Cited by 176
Self-citation
Research works printed but not linked: 1
- Albert, R., Vogt, W., and Helbig, W. (1973). Photoelectric measurement of blood pressure and flow in the finger. The Digest of 10th Inernational Conference of Medical Biological Engineering, Dresden, GDR, Germany, 13–17 August, 1973, The Conference Committee.
Cited by
Sorted by citations
Items 101–136 of 136
Early detection of high blood pressure from natural speech sounds with graph diffusion network
Ankışhan, H., Çelik, H., Ulucanlar, H., & Yenigün, B. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109591 · Cited by 2
Hinatsu, S., Matsuda, N., Ishizuka, H., Ikeda, S., & Oshiro, O. (2022) · IEEE Access · doi:10.1109/access.2022.3221456 · Cited by 2
Investigation on the Feasibility of Blood Pressure Estimation Through Millimeter-wave FMCW Radar
Kim, Y., Kim, J., You, S., Jeong, B. J., & Kim, Y. (2023) · European Conference on Antennas and Propagation · doi:10.23919/eucap57121.2023.10133218 · Cited by 2
In-Vitro Investigation of Flow Profiles in Arteries Using the Photoplethysmograph
Pilt, K., May, J. M., & Kyriacou, P. A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629713 · Cited by 1
Self-citationA CNN Approach To Predicting Non-Invasive Blood Pressure of Surgical Patients
Ko, P.-Y., Lai, L., Ku, T.-H., & Lin, Y. (2022) · IEEE International Conference on Knowledge Innovation and Invention (ICKII) · doi:10.1109/ickii55100.2022.9983601 · Cited by 1
Methods for the assessment of microcirculation in patients with hypertension
Lewandowska, K., Marzyńska, D., Rzesoś, P., Partyka, A., Dydowicz, F., Lewandowski, M., Pawlak-Chomicka, R., Tykarski, A., & Uruski, P. (2023) · Arterial Hypertension · doi:10.5603/ah.a2023.0004 · Cited by 1
A NEW LOW-COMPLEXITY ALGORITHM FOR THE PULSE TRANSIT TIME EVALUATION
Lilia, R., & Omari, T. (2023) · Bioscience & Engineering An International Journal · doi:10.5121/bioej.2023.10202 · Cited by 1
Autoencoder-Based PPG Compression for Resource-Constrained Device
Santiwongkarn, S., Kornworakarn, N., Suksompong, P., Choksatchawathi, T., Kaewlee, T., & Wilaiprasitporn, T. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10207971 · Cited by 1
Simulating a Specialist's Treatment Experience for Hypertension Using Deep Neural Networks
Ri, J., Jo, K., & Mun, T. (2025) · Journal of Clinical Hypertension · doi:10.1111/jch.70173 · Cited by 1
Medical Devices and Instrumentation
Badnjević, A., Spahić, L., Bego, T., & Deumić, S. (2026) · Series in bioengineering · doi:10.1007/978-3-032-10978-1_2 · Cited by 1
Zhang, J., Guan, C., Wu, D., Chen, Z., Liu, G., Nie, Y., Shokouhimehr, M., He, F., & Liu, Z. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae3cb4 · Cited by 1
Seoane-Casqueiro, Ó., Vázquez-Agra, N., Cruces-Sande, A., López-Prado, E., López-Paz, J., Camafort, M., Casariego-Vales, E., Hermida-Ameijeiras, Á., & Pose-Reino, A. (2026) · Revista clínica española (Ed. impresa) · doi:10.1016/j.rce.2026.502523 · Cited by 1
Ishwary, S., Shankar, M., Kiran, V. R., Nabeel, P. M., & Joseph, J. (2023) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea57477.2023.10171895 · Cited by 1
Contribution of Timescale Patterns to Photoplethysmography-based Blood Pressure EstimationPreprint
Xing, X., Huang, R., Jiang, C., & Dong, W. (2023) · Research Square · doi:10.21203/rs.3.rs-2430236/v2 · Cited by 1
Garrett, A., Kim, B., Gurel, N. Z., Sie, E. J., Wilson, B. K., Marsili, F., Forman, J. P., Hamburg, N. M., Boas, D. A., & Roblyer, D. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.16.608163 · Cited by 1
Zheng, Z. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/155391
Pansini, P. F., Gonçalves, A. K., Rebuli, G. D. S., Vidoto, L. V., de Mello, J. P. V., Soares, A. R., Filho, V. Z., Guizardi, P., Tozetti, L. F., & Rodrigues, R. M. (2022) · Technical Papers ... Rio Oil & Gas · doi:10.48072/2525-7579.rog.2022.321
Contribution of Timescale Patterns to Photoplethysmography-based Blood Pressure EstimationPreprint
Xing, X., Huang, R., Jiang, C., & Dong, W. (2023) · Research Square · doi:10.21203/rs.3.rs-2430236/v1
Chen, C., Zhao, H. Y., Zheng, S. H., Ramachandra, R. A., He, X., Zhang, Y. H., & Sudarshan, V. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4182263
A NEW ALGORITHM FOR MEASURING PULSE TRANSIT TIME FROM ECG AND PPG SIGNALS
Lilia, R., & Taha, O. (2023) · International journal of Biomedical Engineering and Science · doi:10.5121/ijbes.2023.10101
Design of PPG and ECG synchronous acquisition system based on NRF52832
Chen, H.-D., Wang, L.-H., Fan, M.-H., Sun, K.-K., Chen, C., Liu, C., & Huang, P.-C. (2023) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan58799.2023.10226900
Wearable Device for Daily Continuous Blood Pressure Estimation Based on Pulse Rate Measurement
Hayase, T. (2024) · Springer eBooks · doi:10.1007/978-981-99-8122-9_9
Ishwarya, S., Kiran, R. V., Manoj, R., Nabeel, P. M., & Joseph, J. (2024) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea60663.2024.10596728
App-Timizing Cardiac Rehabilitation: Enhancing Patient Engagement with Mobile Health Applications
Tayon, K. G., Carlisle, A. E., Taylor, B. J., Cornwell, W. K., Shapiro, B. P., Thomas, R. J., & Dineen, E. H. (2024) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-024-00751-8
A brief survey of blood pressure measurement using video processing techniques
Yawle, P., & Ghongade, R. (2024) · AIP conference proceedings · doi:10.1063/5.0241791
Hawthorne, G., Richardson, M., Greening, N. J., Esliger, D., Briggs-Price, S., Chaplin, E. J., Clinch, L., Steiner, M. C., Singh, S. J., & Orme, M. W. (2022) · Figshare · doi:10.6084/m9.figshare.19663806
Hawthorne, G., Richardson, M., Greening, N. J., Esliger, D., Briggs-Price, S., Chaplin, E. J., Clinch, L., Steiner, M. C., Singh, S. J., & Orme, M. W. (2022) · Figshare · doi:10.6084/m9.figshare.19663800
Hawthorne, G., Richardson, M., Greening, N. J., Esliger, D., Briggs-Price, S., Chaplin, E. J., Clinch, L., Steiner, M. C., Singh, S. J., & Orme, M. W. (2022) · Figshare · doi:10.6084/m9.figshare.19663803
Xu, T., Ren, M., Yang, K., Xu, J. T., & Zeng, X. L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356095
Seoane-Casqueiro, O., Vázquez-Agra, N., Cruces‐Sande, A., López-Prado, E., López-Paz, J. E., Camafort, M., Casariego-Vales, E., Ameijeiras, Á. H., & Pose-Reino, A. (2026) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2026.502523
Non-invasive Evaluation of Cardiovascular Dynamics Using Statistical Learning Tools
Aguirre, N. A. (2023) · theses.fr (ABES) · doi:10.70675/0d0519bfzb38bz4b9fzab46z8a255d75d280
Liu, Z. H., Mai, C., & Huang, J. (2026) · International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) · doi:10.1109/icaide69370.2026.11604547
Wang, B., Liu, Y., Pang, L., Wang, K., Hou, Z., Wang, M., & Yan, F. (2026) · Optical Fiber Technology · doi:10.1016/j.yofte.2026.104751
Gangadharan, N., Baskaran, B., Manavalan, A., Praveenkumar, M., Anandhakumar, M., Jebaraj, B., Devasahayam, S. R., & Subramani, S. (2026) · medRxiv · doi:10.64898/2026.10.01.26364471
Radev, H., & Petrova, G. I. (2026) · Applied Sciences · doi:10.3390/app162010201
A narrative review of methods for estimating blood pressure waveforms from photoplethysmography
Olszewski, B., Mataczyński, C., Ostrowski, Z., & Kasprowicz, M. (2026) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-026-01521-0
Metadata
- Authors
- Hosanee, M., Chan, G., Welykholowa, K., Cooper, R., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., Howard, N., Chan, W.-S., Lim, K., Fletcher, R., Ward, R., & Elgendi, M.
- Affiliations
- University of British Columbia (CA)
- City, University of London (GB)
- Coventry University (GB)
- Freeman Hospital (GB)
- The University of Adelaide (AU)
- Simon Fraser University (CA)
- UNSW Sydney (AU)
- University of Oxford (GB)
- University of Massachusetts Chan Medical School (US)
- Corresponding author
- Elgendi, Mohamed
- ORCID
- Venue
- Journal of Clinical Medicine
- Publisher
- MDPI AG
- Published
- 2020-03-07
- Volume
- 9
- Issue
- 3
- Pages
- 723
- ISSN
- 2077-0383 (ISSN-L)
- PMC
- PMC7141397
- OpenAlex
- W3009202705
- Cited by
- 136
- References
- 36
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 17 · 2023: 36 · 2022: 35 · 2021: 22 · 2020: 8
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Journal of Clinical Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure (Coventry University) (acceptedVersion, cc-by)
- City Research Online (City University London) (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by)
- eScholarship@UMassChan (submittedVersion, cc-by)
- Summit (Simon Fraser University) (submittedVersion)
- cIRcle (University of British Columbia) (submittedVersion, cc-by)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Summit (Simon Fraser University) (submittedVersion, cc-by)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by)
- Open Collections
- Funders
- Canada Research Chair (CRC)
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-04462)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Keywords
- cuffless blood pressure measurement, non-invasive blood pressure estimation, hypertension detection, wearable devices
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review