The use of photoplethysmography for assessing hypertension
Open accessAbstract
The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
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
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?
The wavelength of light affects the PPG measurement because green light penetrates less deeply than red and infrared light.
“The wavelength of light also affects the PPG measurement [18] since green light penetrates less deeply than red and infrared light [51].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [18]
Beyond fitness and heart rate monitoring, the primary medical use of PPG has been focused on obtaining information about the cardiovascular system towards cardiovascular disease (CVD) diagnosis and treatment.
“Beyond fitness and heart rate monitoring, the primary medical use of the PPG has been focused on obtaining information about the cardiovascular system towards cardiovascular disease (CVD) diagnosis and treatment [17,18].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [17]
Red light and NIR penetrate deeper into human body and provide a wider range of information about physiological signal.
“On the contrary, red light and NIR penetrate deeper into the human body and thus provide a wider range of information about the physiological signal [55,56].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [55]
Many algorithms are known where systolic and diastolic BP are estimated from the shape of the PPG curve.
“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 [55]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967 · see citing works
Bashkatov, A. N., Genina, E. A., Kochubey, V. I., & Tuchin, V. V. (2005) · Journal of Physics D: Applied Physics · doi:10.1088/0022-3727/38/15/004 · Cited by 1,720 · see citing works
Westerhof, N., Lankhaar, J., & Westerhof, B. (2009) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-008-0359-2 · Cited by 1,271 · see citing works
On the Analysis of Fingertip Photoplethysmogram Signals
Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312801215782 · Cited by 1,206 · see citing works
Self-citationSaeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., & Mark, R. G. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e31820a92c6 · Cited by 1,162 · see citing works
The association for the advancement of medical instrumentation
(1974) · Ultrasound in Medicine & Biology · doi:10.1016/0301-5629(74)90035-0 · Cited by 1,031 · see citing works
Determination of age-related increases in large artery stiffness by digital pulse contour analysis
Millasseau, S., Kelly, R. P., Ritter, J., & Chowienczyk, P. (2002) · Clinical Science · doi:10.1042/cs1030371 · Cited by 624 · see citing works
Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring
Kachuee, M., Kiani, M. M., Mohammadzade, H., & Shabany, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2580904 · Cited by 621 · see citing works
Smart Vest: Wearable multi-parameter remote physiological monitoring system
Pandian, P. S., Mohanavelu, K., Safeer, K. P., Kotresh, T. M., Shakunthala, D. T., Gopal, P., & Padaki, V. C. (2007) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2007.05.014 · Cited by 420 · see citing works
Smart health monitoring systems: An overview of design and modeling
Baig, M. M., & Gholamhosseini, H. (2013) · Journal of Medical Systems · doi:10.1007/s10916-012-9898-z · Cited by 396 · see citing works
Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time
Kachuee, M., Kiani, M. M., Mohammadzade, H., & Shabany, M. (2015) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2015.7168806 · Cited by 351 · see citing works
A Neural Network-based method for continuous blood pressure estimation from a PPG signal
Kurylyak, Y., Lamonaca, F., & Grimaldi, D. (2013) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2013.6555424 · Cited by 302 · see citing works
Lee, H., Kim, E., Lee, Y., Kim, H., Lee, J., Kim, M., Yoo, H., & Yoo, S. (2018) · Science Advances · doi:10.1126/sciadv.aas9530 · Cited by 277 · see citing works
New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring
van Gastel, M., Stuijk, S., & de Haan, G. (2016) · Scientific Reports · doi:10.1038/srep38609 · Cited by 253 · see citing works
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 · see citing works
Self-citationChan, P., Wong, C., Poh, Y. C., Pun, L., Leung, W. W., Wong, Y., Wong, M. M., Poh, M., Chu, D. W., & Siu, C. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.003428 · Cited by 240 · see citing works
The difference in pulse transit time to the toe and finger measured by photoplethysmography
Nitzan, M., Khanokh, B., & Slovik, Y. (2001) · Physiological Measurement · doi:10.1088/0967-3334/23/1/308 · Cited by 218 · see citing works
A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China
Liang, Y., Chen, Z., Liu, G., & Elgendi, M. (2018) · Scientific Data · doi:10.1038/sdata.2018.20 · Cited by 215 · see citing works
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 · see citing works
Mack, D. C., Patrie, J. T., Suratt, P. M., Felder, R. A., & Alwan, M. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2007194 · Cited by 196 · see citing works
Continuous noninvasive blood pressure measurement by pulse transit time
Fung, P., Dumont, G. A., Ries, C. R., Mott, C., & Ansermino, J. M. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2004.1403264 · Cited by 181 · see citing works
Cattivelli, F. S., & Garudadri, H. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.35 · Cited by 172 · see citing works
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 170 · see citing works
Self-citationValidation of the Instant Blood Pressure Smartphone App
Plante, T. B., Urrea, B., MacFarlane, Z. T., Blumenthal, R. S., Miller, E. R., Appel, L. J., & Martin, S. S. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0157 · Cited by 159 · see citing works
Wireless Body Area Networks for Healthcare: A Survey
Crosby, G. V. (2012) · International Journal of Ad hoc Sensor & Ubiquitous Computing · doi:10.5121/ijasuc.2012.3301 · Cited by 155 · see citing works
In vivo reflectance of blood and tissue as a function of light wavelength
Cui, W., Ostrander, L. E., & Lee, B. Y. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.55667 · Cited by 149 · see citing works
Cuffless Differential Blood Pressure Estimation Using Smart Phones
Chandrasekaran, V., Dantu, R., Jonnada, S., Thiyagaraja, S. R., & Subbu, K. P. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2211078 · Cited by 144 · see citing works
Kwok, A. C., Funk, L. M., Baltaga, R., Lipsitz, S. R., Merry, A., Dziekan, G., Ciobanu, G., Berry, W. R., & Gawande, A. A. (2012) · Annals of Surgery · doi:10.1097/sla.0b013e3182777fa4 · Cited by 139 · see citing works
Blood pressure variability: Its measurement and significance in hypertension
Parati, G., Faini, A., & Valentini, M. (2006) · Current Hypertension Reports · doi:10.1007/s11906-006-0051-6 · Cited by 136 · see citing works
Zhang, Q., Zhou, D., & Zeng, X. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0317-z · Cited by 132 · see citing works
Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database
Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Diagnostics · doi:10.3390/diagnostics8030065 · Cited by 132 · see citing works
Self-citationA novel method to detect Heart Beat Rate using a mobile phone
Pelegris, P., Banitsas, K. A., Orbach, T., & Kostas Marias, A. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626580 · Cited by 126 · see citing works
Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transform
Choi, Y., Zhang, Q., & Ko, S. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2012.09.005 · Cited by 123 · see citing works
A history of cardiac auscultation and some of its contributors
Hanna, I. R., & Silverman, M. E. (2002) · The American Journal of Cardiology · doi:10.1016/s0002-9149(02)02465-7 · Cited by 121 · see citing works
Ruiz‐Rodríguez, J. C., Ruiz‐Sanmartín, A., Ribas, V. J., Caballero, J., García‐Roche, A., Riera, J., Nuvials, X., NADAL, M. D., Solà-Morales, O., Serra, J., et al. (2013) · Intensive Care Medicine · doi:10.1007/s00134-013-2964-2 · Cited by 117 · see citing works
Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms
Elgendi, M., Liang, Y., & Ward, R. K. (2018) · Diseases · doi:10.3390/diseases6010020 · Cited by 107 · see citing works
Self-citationHolz, C., & Wang, E. J. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3132024 · Cited by 106 · see citing works
Otsuka, T., Kawada, T., Katsumata, M., & Ibuki, C. (2006) · Circulation Journal · doi:10.1253/circj.70.304 · Cited by 103 · see citing works
Wang, E. J., Zhu, J., Jain, M., Lee, T.-J., Saba, E., Nachman, L., & Patel, S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173999 · Cited by 103 · see citing works
Detection of a and b waves in the acceleration photoplethysmogram
Elgendi, M., Norton, I., Brearley, M. B., Abbott, D., & Schuurmans, D. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-139 · Cited by 100 · see citing works
Self-citationHypertension Assessment Using Photoplethysmography: A Risk Stratification Approach
Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm8010012 · Cited by 99 · see citing works
Self-citationLiang, Y., Abbott, D., Howard, N., Lim, K. I., Ward, R. K., & Elgendi, M. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030337 · Cited by 94 · see citing works
Self-citationProença, J., Muehlsteff, J., Aubert, X. L., & Carvalho, P. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626627 · Cited by 92 · see citing works
Sandberg, M., Zhang, Q., STYF, J. R., Gerdle, B. U. C., & Lindberg, L. (2005) · Acta Physiologica Scandinavica · doi:10.1111/j.1365-201x.2005.01412.x · Cited by 91 · see citing works
Estimating blood pressure trends and the nocturnal dip from photoplethysmography
Radha, M., de Groot, K., Rajani, N. B., Wong, C. C. P., Kobold, N., Vos, V., Fonseca, P. M., Mastellos, N., Wark, P. A., Velthoven, N., et al. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab030e · Cited by 85 · see citing works
Zheng, Y., Poon, C. C. Y., Yan, B. P., & Lau, J. Y. W. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0558-6 · Cited by 82 · see citing works
Estimating blood pressure using Windkessel model on photoplethysmogram
Choudhury, A. D., Banerjee, R., Sinha, A., & Kundu, S. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944640 · Cited by 78 · see citing works
Михайлович, У. А. (2018) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/aabd09 · Cited by 78 · see citing works
Detection of c, d, and e waves in the acceleration photoplethysmogram
Elgendi, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.08.001 · Cited by 73 · see citing works
Self-citationEvaluation of blood pressure changes using vascular transit time
Foo, J. Y. A., Lim, C. S., & Wang, P. (2006) · Physiological Measurement · doi:10.1088/0967-3334/27/8/003 · Cited by 62 · see citing works
Lee, H., Chung, H., Ko, H., & Lee, J. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2801385 · Cited by 61 · see citing works
Zahedi, E., Chellappan, K., Ali, M. A. M., & Singh, H. (2007) · Cardiovascular Engineering An International Journal · doi:10.1007/s10558-007-9037-5 · Cited by 60 · see citing works
Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors
Baek, H. J., Kim, K. K., Kim, J. S., Lee, B., & Park, K. S. (2009) · Physiological Measurement · doi:10.1088/0967-3334/31/2/002 · Cited by 59 · see citing works
Slope Transit Time (STT): A Pulse Transit Time Proxy requiring Only a Single Signal Fiducial Point
Addison, P. S. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2528507 · Cited by 57 · see citing works
Thomas, S. S., Nathan, V., Zong, C., Akinbola, E., Aroul, A. L. P., Philipose, L., Soundarapandian, K., Shi, X., & Jafari, R. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944076 · Cited by 52 · see citing works
A Blood Pressure Monitoring Method for Stroke Management
Ma, H. T. (2014) · BioMed Research International · doi:10.1155/2014/571623 · Cited by 46 · see citing works
Photoplethysmography: Part 1 Comparison with laser Doppler flowmetry
Lindberg, L. G., Tamura, T., & Öberg, P. Å. (1991) · Medical & Biological Engineering & Computing · doi:10.1007/bf02446294 · Cited by 46 · see citing works
PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
Fujita, D., Suzuki, A., & Ryu, K. (2019) · Applied Sciences · doi:10.3390/app9020304 · Cited by 46 · see citing works
A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal
Lim, H.-J., Kim, B., Noh, G., & Yoo, S. K. (2019) · Sensors · doi:10.3390/s19020384 · Cited by 46 · see citing works
McCombie, D. B., Shaltis, P., Reisner, A. T., & Asada, H. H. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352301 · Cited by 43 · see citing works
Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography
Waugh, W., Allen, J., Wightman, J., Sims, A. J., & Beale, T. A. W. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/6812404 · Cited by 38 · see citing works
Lai, P., & Kim, I.-S. (2015) · Healthcare Technology Letters · doi:10.1049/htl.2014.0097 · Cited by 37 · see citing works
Li, P., Liu, M., Zhang, X., et al. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5400-0 · Cited by 37 · see citing works
Design and development of continuous cuff-less blood pressure monitoring devices
Griggs, D. J., Sharma, M., Naghibi, A., Wallin, C., Ho, V., Barbosa, K., Ghirmai, T., Cao, H., & Krishnan, S. K. (2016) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2016.7808908 · Cited by 37 · see citing works
Adaptive algorithm for continuous monitoring of blood pressure using a pulse transit time
Jadooei, A., Zaderykhin, O., & Shulgin, V. (2013) · IEEE International Conference on Electronics and Nanotechnology (ELNANO) · doi:10.1109/elnano.2013.6552042 · Cited by 35 · see citing works
Chen, Z., Yang, X., Teo, J. T., & Ng, S. H. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610029 · Cited by 35 · see citing works
Relations between ac-dc components and optical path length in photoplethysmography
Lee, C., Shin, H., & Lee, M. (2011) · Journal of Biomedical Optics · doi:10.1117/1.3600769 · Cited by 34 · see citing works
Seeberg, T. M., Orr, J. G., Opsahl, H., Austad, H. O., Røed, M. H., Dalgard, S. H., Houghton, D., Jones, D. E., & Strisland, F. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2606538 · Cited by 26 · see citing works
TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach
Elgendi, M. (2016) · Biosensors · doi:10.3390/bios6040055 · Cited by 26 · see citing works
Self-citationMobiHealthcare System: Body Sensor Network Based M-Health System for Healthcare Application
Miao, F., Miao, X., Shangguan, W., et al. (2012) · E-Health Telecommunication Systems and Networks · doi:10.4236/etsn.2012.11003 · Cited by 22 · see citing works
User guide and documentation for the MIMIC II database (version 2, release 1)
Clifford, G. D., Scott, D. J., & Villarroel, M. (2010) · Cited by 20 · see citing works
Simulation Recording of an ECG, PCG, and PPG for Feature Extractions
Al-Qazzaz, N. K., Abdulazez, I. F., & Ridha, S. A. (2014) · Al-Khwarizmi Engineering Journal · Cited by 19 · see citing works
Elgendi, M., Howard, N., Lovell, N. H., Cichocki, A. S., Brearley, M. B., Abbott, D., & Adatia, I. (2016) · JMIR Biomedical Engineering · doi:10.2196/biomedeng.6401 · Cited by 18 · see citing works
Self-citationSolà, J., Proença, M., Braun, F., Pierrel, N., Degiorgis, Y., Verjus, C., Lemay, M., Bertschi, M., & Schoettker, P. (2016) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2016-0060 · Cited by 18 · see citing works
Plante, T. B., O’Kelly, A. C., Urrea, B., MacFarlane, Z. T., Blumenthal, R. S., Charleston, J. B., Miller, E. R., Appel, L. J., & Martin, S. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0039-z · Cited by 18 · see citing works
Eventogram: A Visual Representation of Main Events in Biomedical Signals
Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040022 · Cited by 17 · see citing works
Self-citationExtracting blood flow parameters from Photoplethysmograph signals: A review
Utami, N., Setiawan, A. W., Zakaria, H., Mengko, T. R., & Mengko, R. K. W. (2013) · International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering · doi:10.1109/icici-bme.2013.6698535 · Cited by 16 · see citing works
A fixed-parameter algorithm for the maximum agreement forest problem on multifurcating trees
Shi, F., Wang, J., Yang, Y., et al. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5355-1 · Cited by 14 · see citing works
Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals
Calamanti, C., Moccia, S., Migliorelli, L., Paolanti, M., & Frontoni, E. (2019) · Electronics · doi:10.3390/electronics8030271 · Cited by 10 · see citing works
Banerjee, R., Choudhury, A. D., Sinha, A., et al. (2014) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2668332.2668378 · Cited by 9 · see citing works
Guo, D. G., Tay, F. E. H., Xu, L. W., et al. (2009) · International Workshop on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2009.12 · Cited by 9 · see citing works
A personal medical device for multi-sensor, remote vital signs collection in the elderly
Munnoch, R., & Jiang, P. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237285 · Cited by 8 · see citing works
Demonstration abstract: BioWatch — A wrist watch based physiological signal acquisition system
Thomas, S. S., Nathan, V., Zong, C., Aroul, A. L. P., Philipose, L., Soundarapandian, K., Shi, X., & Jafari, R. (2014) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2014.6846797 · Cited by 8 · see citing works
Iokibe, T., KURIHARA, M., Maniwa, Y., Ohta, S., UCHIDA, I., Amata, M., & YAMAMOTO, M. (2003) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.15.565 · Cited by 5 · see citing works
A wearable blood pressure sensor using oscillometric photoplethysmography and micro accelerometers
Shaltis, P. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5 · see citing works
Virtual Cuff: Multisensory Non-Intrusive Blood Pressure Monitoring
Jung, K.-H., Tran, V. P., Gabrielian, V., et al. (2014) · International Conference on Body Area Networks · doi:10.4108/icst.bodynets.2014.257046 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- [41]Radha, M. et al. Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks. arXiv preprint arXiv:1805.09121 (2018). · arXiv
- [52]Wang, E. J. et al. Seismo: Blood pressure monitoring using built-in smartphone accelerometer and camera. In 2018 CHI Conference on Human Factors in Computing Systems, 425 (ACM Digital Library, Montreal QC, Canada, 2018).
- [53]Holz, C. & Wang, E. J. Glabella: Continuously sensing blood pressure behavior using an unobtrusive wearable device. Proc. ACM Inter. Mobile Wearable Ubiquit. Technol.1, 58 (2017).
- [60]Banerjee, R., Choudhury, A. D., Sinha, A. & Visvanathan, A. HeartSense: smart phones to estimate blood pressure from photoplethysmography. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems 322–323 (The ACM Digital Library, Memphis, Tennessee, 2014).
- [73]Choi Y Zhang Q Ko S Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert-Huang transform Comput. Electr. Eng. 2013 39 103 111 10.1016/j.compeleceng.2012.09.005 · DOI
- [84]Kachuee, M., Kirani, M. M., Mohammadzade, H. & Shabany, M. Cuff-less highaccuracy calibration-free blood pressure estimation using pulse transit time. In Circuits and Systems (ISCAS), IEEE International Symposium (IEEE, 2015).
Cited by
Sorted by citations
Flexible Wearable Sensors for Cardiovascular Health Monitoring
Chen, S., Qi, J., Fan, S., Qiao, Z., Yeo, J. C., & Lim, C. T. (2021) · Advanced Healthcare Materials · doi:10.1002/adhm.202100116 · Cited by 453 · see citing works
Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348 · see citing works
Photoplethysmography based atrial fibrillation detection: a review
Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., Colorado, R., Meisel, K., & Hu, X. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0207-9 · Cited by 300 · see citing works
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250 · see citing works
Cuffless Blood Pressure Measurement
Mukkamala, R., Stergiou, G. S., & Avolio, A. P. (2022) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-014644 · Cited by 180 · see citing works
Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., & Perin, E. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20247146 · Cited by 160 · see citing works
A Review of Wearable Multi-Wavelength Photoplethysmography
Ray, D., Collins, T., Woolley, S., & Ponnapalli, P. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3121476 · Cited by 155 · see citing works
Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring
Hosanee, M., Chan, G., Welykholowa, K., Cooper, R., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9030723 · Cited by 136 · see citing works
Self-citationWearables in cardiology: Here to stay
Dagher, L., Shi, H., Zhao, Y., & Marrouche, N. F. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.02.023 · Cited by 107 · see citing works
The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
Ajmal, Boonya-Ananta, T., Rodriguez, A. J., Du Le, V. N., & Ramella-Roman, J. C. (2021) · Biomedical Optics Express · doi:10.1364/boe.439893 · Cited by 79 · see citing works
Qawqzeh, Y. K., Bajahzar, A. S., Jemmali, M., Otoom, M. M., & Thaljaoui, A. (2020) · BioMed Research International · doi:10.1155/2020/3764653 · Cited by 71 · see citing works
RetractedNowara, E. M., McDuff, D., & Veeraraghavan, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00150 · Cited by 66 · see citing works
Duarah, R., Torné‐Morató, H., Zhang, G., Amin, Y., Pabast, M., Sharma, N., He, S., Das, M. R., & Pompa, P. P. (2026) · Advanced Healthcare Materials · doi:10.1002/adhm.202504419 · Cited by 24 · see citing works
2D Hybrid Perovskite Sensors for Environmental and Healthcare Monitoring
Riisnaes, K. J., Alshehri, M., Leontis, I., Mastria, R., Lam, H. T., De Marco, L., Coriolano, A., Craciun, M. F., & Russo, S. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c02966 · Cited by 21 · see citing works
- Authors
- Elgendi, M., Fletcher, R., Liang, Y., Howard, N., Lovell, N. H., Abbott, D., Lim, K., & Ward, R.
- Affiliations
- University of British Columbia (CA)
- University of Massachusetts Chan Medical School (US)
- University of Oxford (GB)
- UNSW Sydney (AU)
- The University of Adelaide (AU)
- Corresponding author
- Elgendi, Mohamed
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2019-06-26
- Volume
- 2
- Issue
- 1
- Pages
- 60-60
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6594942
- OpenAlex
- W2955961229
- Cited by
- 625
- References
- 86
- Citations by year
- 2026: 70 · 2025: 89 · 2024: 87 · 2023: 112 · 2022: 111 · 2021: 94 · 2020: 55 · 2019: 6
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by)
- Funders
- Children's Hospital Foundation
- BC Children's Hospital
- 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
- photoplethysmography, hypertension, blood pressure estimation, pulse oximetry, wearable devices, smartphone health monitoring, non-invasive blood pressure measurement, physiological signal fusion, continuous blood pressure monitoring
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
