Photoplethysmography and its application in clinical physiological measurement

Allen, John

doi:10.1088/0967-3334/28/3/r01

Cited by 3,967Non-Invasive Vital Sign MonitoringHemodynamic Monitoring and TherapyCardiovascular Health and Disease Prevention

Photoplethysmography and its application in clinical physiological measurement

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Allen, John·2007·Physiological Measurement

DOI 10.1088/0967-3334/28/3/r01

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Abstract

Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile ('AC') physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying ('DC') baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.

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PPG sensors used to measure HR are liable to poor accuracy during activities with increased physical exertion or activities involving repetitive contractions of forearm skeletal muscles.
“Prior research has suggested that PPG sensors used to measure the HR are liable to poor accuracy during activities with increased physical exertion or activities involving repetitive contractions of forearm skeletal muscles [36-38].”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [37]
During activities involving sustained muscle contractions or higher intensity exercises, the contact between the device's PPG sensor and skin is decreased, leading to a disruption in the signal quality and causing poor quality data.
“It has been suggested that during activities involving sustained muscle contractions or higher intensity exercises, the contact between the device’s PPG sensor and skin is decreased, leading to a disruption in the signal quality and causing poor quality data [36,37].”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [37]
ECG monitoring via wearable sensors poses challenges for athletics: surface EMG, increased electrode impedance, respiration induced baseline drift, and electrode contact movement can cause noise and motion artifacts.
“Firstly, surface EMG, increased electrode impedance, respiration induced baseline drift, and electrode contact movement can cause noise and motion artifacts.88”
From Wearable sensors for monitoring the physiological and biochemical profile of the athlete (2019) · cites this paper as [88]
Intrinsic PPG disturbances originating from physiological processes such as respiration, vasoconstriction due to temperature changes or stress have not been considered.
“However, the intrinsic PPG disturbances originating from physiological processes, such as respiration, vasoconstriction due to temperature changes or stress [57], have not been considered.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [57]
The photoplethysmogram (PPG) signal is an optical measure of arterial blood volume that fluctuates with each heartbeat and is strongly influenced by the heart and blood vessels.
“The photoplethysmogram (PPG) signal is a measure of arterial blood volume, which fluctuates with each heartbeat and is used by many wearables to monitor heart rate (HR).”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [6]

The mechanisms underlying the PPG pulse wave shape are not yet fully understood.
“The mechanisms underlying the PPG pulse wave shape are not yet fully understood [6].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [6]
The shape of the PPG pulse wave is influenced by the incident wave transmitted through the arteries from the heart and by reflected waves from a range of arterial sites.
“The shape is influenced not only by the incident wave transmitted through the arteries from the heart but also by reflected waves from a range of arterial sites [6].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [6]
Cardiac output has been estimated from pulse wave shape and low-frequency PPG variations.
“Cardiac output, monitored in peri-operative and critical care settings, has been estimated from pulse wave shape and low-frequency PPG variations”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [6]
The non-pulsating component of PPG is a function of the average blood volume, respiration, the sympathetic nervous system, and thermoregulation.
“Thus, while the pulsating components of PPG are related to arterial blood volume, the non-pulsating component is a function of the average blood volume, respiration, the sympathetic nervous system, and thermoregulation [151].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [151]
More recent wearable physical activity devices that include a measure of heart rate utilize photoplethysmography (PPG) to measure heart rate.
“These more recent wearable physical activity devices, which include a measure of heart rate, utilize photoplethysmography (PPG) to measure heart rate.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [17]
PPG is a simple and low-cost optical technique that detects blood volume changes in the microvascular bed of tissue.
“PPG is a simple and low-cost optical technique that detects blood volume changes in the microvascular bed of tissue [17].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [17]

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Self-citation
Regional Differences in the Basal and Maximal Rates of Blood Flow in the Skin
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Pulse oximetry in the oesophagus
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Cardiac output monitoring by pressure recording analytical method in cardiac surgery
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Spontaneous skin temperature oscillations in normal human subjects
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Respiration can be monitored by photoplethysmography with high sensitivity and specificity regardless of anaesthesia and ventilatory mode
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Finapres arterial pulse wave analysis with Modelflow is not a reliable non-invasive method for assessment of cardiac output
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Variability of photoplethysmographyperipheral pulse measurementsat the ears, thumbs and toes
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Optical noninvasive monitoring of skin blood pulsations
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Repeatability of peripheral pulse measurements on ears, fingers and toes using photoelectric plethysmography
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Association of Risk Factors With Increased Pulse Wave Velocity Detected by a Novel Method Using Dual-Channel Photoplethysmography
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A new approach for non-intrusive monitoring of blood pressure on a toilet seat
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Respiratory variations in the reflection mode photoplethysmographic signal. Relationships to peripheral venous pressure
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Spectral analysis of AC and DC components of the pulse photoplethysmograph at rest and during induction of anaesthesia
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Prevention of Pressure Sores
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Photoplethysmography Assessments in Cardiovascular Disease
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Simultaneous ipsilateral and contralateral measurements of vasomotion in conduit arteries of human upper limbs
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Toe Pulse Wave Analysis in Obliterating Atherosclerosis
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Digital pulse plethysmography (DPG) in studies of the hemodynamic response to nitrates — A survey of recording methods and principles of analysis
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Local cold exposure test with a new arterial photoplethysmographic sensor in healthy controls and patients with secondary Raynaud’s phenomenon
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Use of Pulse Transit Time To Distinguish Respiratory Events From Tidal Breathing in Sleeping Children
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The time-variable photoplethysmographic signal; dependence of the heart synchronous signal on wavelength and sample volume
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A New Probe for Ankle Systolic Pressure Measurement Using Photoplethysmography (PPG)
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DISTINCTION BETWEEN ARTERIAL, VENOUS AND FLOW COMPONENTS IN PHOTOELECTRIC PLETHYSMOGRAPHY IN MAN
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Prospective assessment of an artificial neural network for the detection of peripheral vascular disease from lower limb pulse waveforms
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Self-citation
Estimation of breathing interval from the photoplethysmographic signals in children
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Supraorbital photoplethysmography. Simple, accurate screening for carotid occlusive disease
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Comparison of three arterial pulse waveform classification techniques
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Self-citation
Photoplethysmography of the Distal Pulp in the Assessment of the Vasospastic Hand
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Lichtelektrische Plethysmogramme
Matthes, K. J., & Hauss, W. H. (1938) · Journal of Molecular Medicine · doi:10.1007/bf01778576 · Cited by 29 · see citing works
An assessment of blood vessel vasoactivity using photoplethysmography.
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Digital venous photoplethysmography in the seated position is a reproducible noninvasive measure of lower limb venous function in patients with isolated superficial venous reflux
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COMPARISON OF PLETHYSMOGRAMS TAKEN FROM FINGER AND PINNA DURING ANAESTHESIA
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Improvement in Deep Vein Haemodynamics Following Surgery for Varicose Veins
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Age and gender do not influence the ability to detect respiration by photoplethysmography
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Quantitative photoplethysmography: Lambert-Beer law or inverse function incorporating light scatter
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Quantification of inspiratory‐induced vasoconstrictive episodes: a comparison of laser Doppler fluxmetry and photoplethysmography
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Blood flow rate in arteriovenous anastomoses and capillaries in thumb, first toe, ear lobe, and nose
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Vibration plethysmography: A method for studying the visco-elastic properties of finger arteries
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Initial Experience with Personal Digital Assistant-Based Reflectance Photoplethysmograph for Free Tissue Transfer Monitoring
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Remote plethysmographic imaging using ambient light
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Highly Stretchable Resistive Pressure Sensors Using a Conductive Elastomeric Composite on a Micropyramid Array
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On the Analysis of Fingertip Photoplethysmogram Signals
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Advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment
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Organic Photodetectors for Next‐Generation Wearable Electronics
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TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise
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How accurate is pulse rate variability as an estimate of heart rate variability?
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Human Emotion Recognition: Review of Sensors and Methods
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Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging
Sun, Y., & Thakor, N. V. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2476337 · Cited by 567 · see citing works
Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables
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Wearable sensors for monitoring the physiological and biochemical profile of the athlete
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Improved motion robustness of remote-PPG by using the blood volume pulse signature
de Haan, G., & van Leest, A. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/9/1913 · Cited by 491 · see citing works
A High‐Performance Solution‐Processed Organic Photodetector for Near‐Infrared Sensing
Huang, J., Lee, J., Vollbrecht, J., Брус, В. В., Dixon, A. L., Cao, D. X., Zhu, Z., Du, Z., Wang, H., Cho, K., et al. (2019) · Advanced Materials · doi:10.1002/adma.201906027 · Cited by 492 · see citing works
Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions.
Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/9/015 · Cited by 461 · see citing works
Large-area low-noise flexible organic photodiodes for detecting faint visible light
Fuentes‐Hernandez, C., Chou, W.-F., Khan, T. M., Diniz, L., Lukens, J., Larrain, F. A., Rodriguez-Toro, V. A., & Kippelen, B. J. (2020) · Science · doi:10.1126/science.aba2624 · Cited by 446 · see citing works
Photoplethysmogram Analysis and Applications: An Integrative Review
Park, J., Seok, H. S., Kim, S.-S., & Shin, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.808451 · Cited by 450 · see citing works
Contact-Based Methods for Measuring Respiratory Rate
Massaroni, C., Nicolò, A., Presti, D. L., Sacchetti, M., Silvestri, S., & Schena, E. (2019) · Sensors · doi:10.3390/s19040908 · Cited by 442 · see citing works
DistancePPG: Robust non-contact vital signs monitoring using a camera
Kumar, M., Veeraraghavan, A., & Sabharwal, A. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.001565 · Cited by 417 · see citing works
Measuring pulse rate with a webcam — A non-contact method for evaluating cardiac activity
Lewandowska, M., Rumiński, J., Kocejko, T., & Nowak, J. (2011) · Cited by 420 · see citing works
Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction
Zhang, Z. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2406332 · Cited by 388 · see citing works
Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
Slapničar, G., Mlakar, N., & Luštrek, M. (2019) · Sensors · doi:10.3390/s19153420 · Cited by 387 · see citing works
The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379 · see citing works
Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review
Charlton, P. H., Birrenkott, D. A., Bonnici, T., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Tarassenko, L., Watkinson, P. J., Beale, R., & Clifton, D. A. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2763681 · Cited by 379 · see citing works
Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health
Brewer, L. C., Fortuna, K. L., Jones, C., Walker, R., Hayes, S. N., Patten, C. A., & Cooper, L. A. (2020) · JMIR mHealth and uHealth · doi:10.2196/14512 · Cited by 367 · see citing works
Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions
Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J. F., & Sebe, N. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.263 · Cited by 351 · see citing works
Self-powered cardiovascular electronic devices and systems
Zheng, Q., Tang, Q., Wang, Z. L., & Li, Z. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0426-4 · Cited by 348 · see citing works
Diode fibres for fabric-based optical communications
Rein, M., Favrod, V. D., Hou, C., Khudiyev, T., Stolyarov, A. M., Cox, J. R., Chung, C., Chhav, C., Ellis, M., Joannopoulos, J. D., et al. (2018) · Nature · doi:10.1038/s41586-018-0390-x · Cited by 344 · see citing works
Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography
Selvaraj, N., Jaryal, A. K., Santhosh, J., Deepak, K. K., & Anand, S. (2008) · Journal of Medical Engineering & Technology · doi:10.1080/03091900701781317 · Cited by 342 · 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
Intelligence in the Internet of Medical Things era: A systematic review of current and future trends
Al-Turjman, F. M., Nawaz, M. H., & Uluşar, Ü. D. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.12.030 · Cited by 338 · see citing works
The psychophysiology of flow during piano playing.
de Manzano, Ö., Theorell, T., Harmat, L., & Ullén, F. (2010) · Emotion · doi:10.1037/a0018432 · Cited by 330 · see citing works
Non-contact heart rate and heart rate variability measurements: A review
Kranjec, J., Beguš, S., Geršak, G., & Drnovšek, J. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.03.004 · Cited by 317 · see citing works
Reliability of Ultra-Short-Term Analysis as a Surrogate of Standard 5-Min Analysis of Heart Rate Variability
Baek, H. J., Cho, C.-H., Cho, J., & Woo, J. (2015) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2014.0104 · Cited by 317 · see citing works
Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques
Monte-Moreno, E. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.05.001 · Cited by 311 · see citing works
Remote measurement of cognitive stress via heart rate variability
McDuff, D., Gontarek, S., & Picard, R. W. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944243 · Cited by 315 · see citing works
Arterial blood pressure measurement and pulse wave analysis-–their role in enhancing cardiovascular assessment
Avolio, A. P., Butlin, M., & Walsh, A. (2009) · Physiological Measurement · doi:10.1088/0967-3334/31/1/r01 · Cited by 309 · see citing works
Photoplethysmography based atrial fibrillation detection: a review
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Flexible graphene photodetectors for wearable fitness monitoring
Polat, E. O., Mercier, G., Nikitskiy, I., Puma, E., Galán, T., Gupta, S., Marques, M. M., Piqueras, J. J., Bouwens, M., Durduran, T., et al. (2019) · Science Advances · doi:10.1126/sciadv.aaw7846 · Cited by 299 · see citing works
A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure
El-Hajj, C., & Kyriacou, P. A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101870 · Cited by 300 · see citing works
Accurate Heart Rate Monitoring During Physical Exercises Using PPG
Temko, A. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2676243 · Cited by 295 · see citing works
CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment
Biswas, D., Everson, L. R., Liu, M., Panwar, M., Verhoef, B.-E., Patki, S., Kim, C. H., Acharyya, A., Hoof, C. A. V., Konijnenburg, M., et al. (2019) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2019.2892297 · Cited by 293 · see citing works
Advances in Photopletysmography Signal Analysis for Biomedical Applications
Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., De Albuquerque, V. H. C., & Alexandria, A. R. (2018) · Sensors · doi:10.3390/s18061894 · Cited by 292 · see citing works
Wearable Sensing Technology Applications in Construction Safety and Health
Ahn, C. R., Lee, S. H., Sun, C., Jebelli, H., Yang, K., & Choi, B. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001708 · Cited by 290 · see citing works
Recent Progress on Broadband Organic Photodetectors and their Applications
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Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera
McDuff, D., Gontarek, S., & Picard, R. W. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2323695 · Cited by 287 · see citing works
Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch
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Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
Li, Q., & Clifford, G. D. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1491 · Cited by 274 · see citing works
Heart Rate Estimation From Wrist-Worn Photoplethysmography: A Review
Biswas, D., Simões‐Capela, N., Hoof, C. A. V., & Helleputte, N. V. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2914166 · Cited by 263 · see citing works
Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes
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Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques
Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E. H., Mahbub, Z. B., Uddin, M. M., Khandakar, A., & Reaz, M. B. I. (2020) · Sensors · doi:10.3390/s20113127 · Cited by 264 · 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
Monitoring blood pressure and cardiac function without positioning via a deep learning–assisted strain sensor array
Li, S., Wang, H., Ma, W., Qiu, L., Xia, K., Zhang, Y., Lü, H., Zhu, M., Liang, X., Wu, X., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0615 · Cited by 257 · 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
Current progress of photoplethysmography and SPO2 for health monitoring
TAMURA, T. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-019-00097-w · Cited by 260 · see citing works
A new look at the essence of the imaging photoplethysmography
Kamshilin, A. A., Nippolainen, E., Sidorov, I. S., Васильев, П. В., Ерофеев, Н. П., Podolyan, N. P., & Владимирович, Р. Р. (2015) · Scientific Reports · doi:10.1038/srep10494 · Cited by 250 · see citing works
Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting
Chan, 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
Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review
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Epidermal devices for noninvasive, precise, and continuous mapping of macrovascular and microvascular blood flow
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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
Physiological and Behavioral Responses Reveal 9-Month-Old Infants’ Sensitivity to Pleasant Touch
Fairhurst, M. T., Löken, L. S., & Großmann, T. (2014) · Psychological Science · doi:10.1177/0956797614527114 · Cited by 242 · see citing works
Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices
Dooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7043 · Cited by 242 · see citing works
Wearable Devices for Physical Monitoring of Heart: A Review
Prieto-Avalos, G., Cruz-Ramos, N. A., Alor‐Hernández, G., Sánchez-Cervantes, J. L., Rodríguez-Mazahua, L., & Guarneros-Nolasco, L. R. (2022) · Biosensors · doi:10.3390/bios12050292 · Cited by 259 · see citing works
Publication guidelines for human heart rate and heart rate variability studies in psychophysiology—Part 1: Physiological underpinnings and foundations of measurement
Quigley, K. S., Gianaros, P. J., Norman, G. J., Jennings, J. R., Berntson, G. G., & de Geus, E. J. C. (2024) · Psychophysiology · doi:10.1111/psyp.14604 · Cited by 253 · see citing works
Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method
Mousavi, S. S., Firouzmand, M., Charmi, M., Hemmati, M. R., Moghadam, M., & Ghorbani, Y. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.022 · Cited by 234 · see citing works
Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study
Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., & Natarajan, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30246-6 · Cited by 229 · see citing works
Flexible Organic Transistors for Biosensing: Devices and Applications
Song, J., Liu, H., Zhao, Z., Lin, P., & Yan, F. (2023) · Advanced Materials · doi:10.1002/adma.202300034 · Cited by 230 · see citing works
Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review
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Authors: Allen, J.
Affiliations: Freeman Hospital (GB)
Corresponding author: Allen, John
ORCID: Allen, John
Venue: Physiological Measurement
Publisher: IOP Publishing
Published: 2007-02-20
Volume: 28
Issue: 3
Pages: R1-R39
ISSN: 0967-3334 (ISSN-L)
1361-6579
DOI: 10.1088/0967-3334/28/3/r01
OpenAlex: W2083872334
Cited by: 3,967
References: 275
Citations by year: 2026: 308 · 2025: 292 · 2024: 310 · 2023: 317 · 2022: 323 · 2021: 314 · 2020: 291 · 2019: 304 · 2018: 299 · 2017: 227 · 2016: 185 · 2015: 216 · 2014: 159 · 2013: 132 · 2012: 86
FWCI: 3.44
Type: journal article
Language: en
Open access: green
doi.org
Locations: Physiological Measurement (publishedVersion)
PubMed (publishedVersion)
Newcastle University ePrints (Newcastle Univesity)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
Keywords: photoplethysmography, blood volume changes, pulse wave analysis, non-invasive physiological monitoring, oxygen saturation, blood pressure measurement, cardiac output, autonomic function, peripheral vascular disease, vascular assessment, light-tissue interaction
MeSH terms: Autonomic Nervous System, Blood Vessels, Humans, Monitoring, Physiologic, Vascular Diseases, Photoplethysmography

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Photoplethysmography and its application in clinical physiological measurement

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