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On the Analysis of Fingertip Photoplethysmogram Signals
Open accessAbstract
Photoplethysmography (PPG) is used to estimate the skin blood flow using infrared light. Researchers from different domains of science have become increasingly interested in PPG because of its advantages as non-invasive, inexpensive, and convenient diagnostic tool. Traditionally, it measures the oxygen saturation, blood pressure, cardiac output, and for assessing autonomic functions. Moreover, PPG is a promising technique for early screening of various atherosclerotic pathologies and could be helpful for regular GP-assessment but a full understanding of the diagnostic value of the different features is still lacking. Recent studies emphasise the potential information embedded in the PPG waveform signal and it deserves further attention for its possible applications beyond pulse oximetry and heart-rate calculation. Therefore, this overview discusses different types of artifact added to PPG signal, characteristic features of PPG waveform, and existing indexes to evaluate for diagnoses. Keywords: Photoplethysmography, acceleration plethysmogram, second derivative plethysmogram, digital volume pulse, ageing, artery, autonomic function, blood pressure, cardiovascular, heart rate, pulse wave analysis, vascular disease
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![Fig. (13) Typical PPG waveforms show the parameters changes with age [62].](/paper-figures/8ee5a57901c347579a7a5160e4be1526/on-the-analysis-of-fingertip-photoplethysmogram-signals-figure-13.jpg)


![Fig. (16) APG waveforms and types of photoplethysmogram [73]. There are different types of APG waveforms. The first APG waveform A (far left) refers to good circulation, whereas the amplitude of b wave is lower than c wave. The last APG waveform G (far right) refers to distinctively bad circulation, whereas the amplitude of c wave is lower than b wave.](/paper-figures/54ec3ef6e18e4116ad4a30b26f4dd445/on-the-analysis-of-fingertip-photoplethysmogram-signals-figure-16.jpg)
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In the citing paper
“| c/a | Decrease | Takazawa et al., 1998 Elgendi et al., 2012 | [29,59] |”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“The shape of a clean PPG signal is closely related to the blood pulse wave of the human body25”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [25]
In the citing paper
“In this case, the pulsatile component of the PPG can be of the order of 1 mV in amplitude [55] although lower amplitudes can be caused by technical factors, such as inappropriate gain control, or physiological factors, such as vasoconstriction [9].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [9]
In the citing paper
“In particular, obesity can lead to changes in skin thickness, blood flow, and oxygen saturation. This affects the optical properties of skin in addition to the distance light has to travel to reach a target vessel or vessels [48,59,60].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“While, to the authors’ knowledge, publications that experimentally and explicitly demonstrate the effect of obesity or BMI on the PPG waveform are limited, we will explore works which suggest that there would be a substantial effect [56,59,60,61,62,63,64,65,66,67,68,69,70].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“The ratio b/a has been found to correlate positively to age, while c/a, d/a and e/a have all been correlated negatively to age [29,59].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“| e/a | Decrease | Takazawa et al., 1998 Elgendi et al., 2012 | [29,59] |”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“| d/a | Decrease | Takazawa et al., 1998 Elgendi et al., 2012 | [29,59] |”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [59]
In the citing paper
“Furthermore, PPG can be used to index heart rate variability (HRV), blood pressure, oxygen saturation, and cardiac output24, but as of yet have not be widely rolled out in consumer-use devices and therefore data collection other than HR will not be discussed further.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [24]
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Citations
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
Assessment of fat-free mass using bioelectrical impedance measurements of the human body
Lukaski, H., Johnson, P., Bolonchuk, W., & Lykken, G. (1985) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/41.4.810 · Cited by 1,896
Investigation of Chronic Venous Insufficiency: A Consensus Statement
Nicolaides, A. N. (2000) · Circulation · doi:10.1161/01.cir.102.20.e126 · Cited by 945
Fifteen years experience with finger arterial pressure monitoring: assessment of the technology
Imholz, B. P. M. (1998) · Cardiovascular Research · doi:10.1016/s0008-6363(98)00067-4 · Cited by 841
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
Takazawa, K., Tanaka, N., Fujita, M., Matsuoka, O., Saiki, T., Aikawa, M., Tamura, S., & Ibukiyama, C. (1998) · Hypertension · doi:10.1161/01.hyp.32.2.365 · Cited by 589
Photoelectric measurement of blood pressure, volume and flow in the finger
Peňáz, J. (1973) · Medical Entomology and Zoology · Cited by 528
Mobile monitoring with wearable photoplethysmographic biosensors
Asada, H. H., Shaltis, P., Reisner, A., Rhee, S., & Hutchinson, R. C. (2003) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2003.1213624 · Cited by 466
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
THE BLOOD SUPPLY OF VARIOUS SKIN AREAS AS ESTIMATED BY THE PHOTOELECTRIC PLETHYSMOGRAPH
Hertzman, A. B. (1938) · American Journal of Physiology-Legacy Content · doi:10.1152/ajplegacy.1938.124.2.328 · Cited by 460
Research works printed but not linked: 3
- [59]Takazawa KTN Fujita M Matsuoka O Saiki T Aikawa M Tamura S Ibukiyama C Assessment of vasocative agents and vascular aging by the second derivative of photoplethysmogram waveform Hypertension 1998 32 365 70 9719069
- [80]Elgendi M Jonkman M De Boer F Fred A Filipe J Gamboa h Heart Rate Variability and Acceleration Plethysmogram measured at rest Biomedical Engineering Systems and Technologies: 2011 Springer 266 77
- [81]Tokutaka H MY Gonda E Yamamoto M Construction of a General Physical Condition Judgment System Using Acceleration Plethysmogram Pulse-Wave Analysis 2009 Berlin/Heidelberg Springer 5629 307 15
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Items 401–500 of 1,001
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Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
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Masinelli, G., Dell’Agnola, F., Arza, A., & Atienza, D. (2021) · Sensors · doi:10.3390/s21082725 · Cited by 15
Rabow, S., & Olofsson, P. (2016) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2016.1186162 · Cited by 15
Contactless Monitoring of PPG Using Radar
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Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography
Rovas, G., Bikia, V., & Stergiopulos, N. (2023) · Bioengineering · doi:10.3390/bioengineering10040460 · Cited by 14
Rabow, S., Jonsson, H., Bro, E., & Olofsson, P. (2023) · The Journal of Maternal-Fetal & Neonatal Medicine · doi:10.1080/14767058.2023.2208252 · Cited by 14
Adıgüzel, G., Şentürk, Ü., & Polat, K. (2024) · Open Journal of Nano · doi:10.56171/ojn.1473276 · Cited by 14
Imaging Photoplethysmography: Signal Waveform Analysis
Djeldjli, D., Bousefsaf, F., Maaoui, C., & Bereksi-Reguig, F. (2019) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2019.8924239 · Cited by 14
Nidigattu, G. R., Mattela, G., & Jana, S. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027457 · Cited by 14
Photoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments
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Sports Science and Human Health - Different Approaches
Marinho, D. A., Neiva, H. P., Johnson, C. P., & Mohamudally, N. A. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.83154 · Cited by 14
Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint
Książczyk, M., Dębska-Kozłowska, A., Warchoł, I., & Lubiński, A. (2021) · JMIR mhealth and uhealth · doi:10.2196/23425 · Cited by 14
Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography
Ghosh, S. K., Chattopadhyay, B. P., Roy, R. M., Mukherjee, J., & Mahadevappa, M. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.11.001 · Cited by 14
Badji, A., Cohen‐Adad, J., & Girouard, H. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.856782 · Cited by 14
Prada, E. J. A., & Bolaños, S. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105338 · Cited by 14
Inverse-model-based cuffless blood pressure estimation using a single photoplethysmography sensor
Suzuki, A. (2015) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411915587957 · Cited by 14
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Euliano, T. Y., Michalopoulos, K., Singh, S., Gregg, A. R., Rio, M. D., Vasilopoulos, T., Johnson, A. M., Onkala, A., Darmanjian, S., Euliano, N. R., et al. (2017) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002532 · Cited by 17
Wei, H., Ta, N., Hu, W.-R., Xiao, M., Tang, X., Haryadi, B., Liou, J. J., & Wu, H. (2019) · Entropy · doi:10.3390/e21121229 · Cited by 14
Yao, L.-P., & Wei-zhang, L. (2020) · Physiological Measurement · doi:10.1088/1361-6579/aba537 · Cited by 14
A comparative study of photoplethysmogram and piezoelectric plethysmogram signals
Qananwah, Q. M., Dagamseh, A., Alquran, H., Ibrahim, K. S., Alodat, M. T., & Hayden, O. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00923-x · Cited by 14
Tarifi, B., Fainman, A., Pantanowitz, A., & Rubin, D. M. (2023) · Applied Sciences · doi:10.3390/app13063955 · Cited by 13
Hamid, H. A., & Nayan, N. A. (2020) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v16i09.13577 · Cited by 13
PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING
Chakraborty, A., Sadhukhan, D., Pal, S., & Mitra, M. (2020) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519420500372 · Cited by 13
Marchi, B. D., Frigerio, M., Nadai, S. D., Longinotti-Buitoni, G., & Aliverti, A. (2021) · Sensors · doi:10.3390/s21217334 · Cited by 13
Fuadah, Y. N., & Lim, K. M. (2022) · Diagnostics · doi:10.3390/diagnostics12112886 · Cited by 13
Zhang, L., Ji, Z., Yang, F., & Chen, G. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104757 · Cited by 13
Manninger, M., Hermans, A. N. L., Caracioni, A.-A., Rohrer, U., Eberl, A., Vernooy, K., Zirlik, A., Linz, D., & Scherr, D. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1199630 · Cited by 13
On the Filtering of Photoplethysmography Signals
Awodeyi, A. E., Alty, S. R., & Ghavami, M. (2014) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2014.76 · Cited by 13
A pulse-sensing robotic hand for tactile arterial palpation
Kong, K. W., Lau, W.-M., Wong, K. S., Chan, H., Lee, F. C. S., Shen, J., Wong, V. T., & Li, W. J. (2016) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2016.7574811 · Cited by 13
Yuan, H., Poeggel, S., Newe, T., Lewis, E., Viphavakit, C., & Leen, G. (2017) · Sensors · doi:10.3390/s17030556 · Cited by 13
Continous monitoring of blood glucose using photophlythesmograph signal
Philip, L. A., Rajasekaran, K., & Jothi, E. S. J. (2017) · International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT) · doi:10.1109/icieeimt.2017.8116832 · Cited by 13
von Wowern, E., Källén, K., & Olofsson, P. (2018) · Pregnancy Hypertension · doi:10.1016/j.preghy.2018.11.002 · Cited by 13
Continuous Blood Pressure Estimation Based on Two-Domain Fusion Model
Wang, Q., Xu, Y., Zeng, G., & Sun, M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/1981627 · Cited by 13
Current methods for the assessment of skin microcirculation: Part 2
Neubauer‐Geryk, J., Hoffmann, M., Wielicka, M., Piec, K., Kozera, G., & Bieniaszewski, L. (2019) · Advances in Dermatology and Allergology · doi:10.5114/ada.2019.83657 · Cited by 13
Ohmic‐Contact‐Gated Carbon Nanotube Transistors for High‐Performance Analog Amplifiers
Rojas, W. A. G., Beck, M. E., Sangwan, V. K., Guo, S., & Hersam, M. C. (2021) · Advanced Materials · doi:10.1002/adma.202100994 · Cited by 13
Haxha, S., Nwibor, C., Ali, M., Sakel, M., Saunders, K., Dyo, V., & Nabakooza, S. (2024) · Sensors · doi:10.3390/s24113301 · Cited by 12
Tran, K. T., Tran, T. T. T., Huynh, D. N., Le, N. K., Le, C. D., Mai, H. X., Linh, H. Q., & Nguyen, T.-L. (2025) · Sensors · doi:10.3390/s25216708 · Cited by 12
Lin, G., Haryadi, B., Yang, C.-M., Chu, S.-C., Yang, C., & Wu, H. (2017) · Entropy · doi:10.3390/e19030132 · Cited by 12
Kulkarni, T. R., & Dushyanth, N. D. (2021) · Results in Optics · doi:10.1016/j.rio.2021.100062 · Cited by 12
Mao, P., Li, H., Shan, X., Davis, M., Tang, T., Zhang, Y., Tong, X., Xin, Y., Cheng, J., Li, L., et al. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c04494 · Cited by 12
Liu, I., Liu, F., Zhong, Q., Ma, F., & Ni, S. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1912 · Cited by 13
Yan, J., Wang, Z., Guo, R. J., Yan, H., Wang, Y., & Qiu, W. (2024) · Measurement · doi:10.1016/j.measurement.2024.115446 · Cited by 12
Yang, Z., Kim, B. G., Lim, J., & Wang, D. H. (2024) · Applied Surface Science · doi:10.1016/j.apsusc.2024.161488 · Cited by 12
Horst, S. T., van Wijk, R. J., Schoonhoven, A. D., de Lange, A., ter Maaten, J. C., & Bouma, H. R. (2025) · Journal of Critical Care · doi:10.1016/j.jcrc.2025.155115 · Cited by 12
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Iqbal, S., Agarwal, S. C., Purcell, I. F., Murray, A., Bacardit, J., & Allen, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104993 · Cited by 12
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Lin, C., Tseng, C.-K., Wang, C., Chao, S.-H., Hwang, Y., & Kau, L. (2023) · Measurement · doi:10.1016/j.measurement.2023.113441 · Cited by 12
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Moscato, S., Orlandi, S., Gregorio, F. D., Lullini, G., Pozzi, S., Sabattini, L., Chiari, L., & Porta, F. L. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-073534 · Cited by 12
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Bao, H. (2014) · Medical Science Monitor · doi:10.12659/msm.889924 · Cited by 12
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Grote, L., & Zou, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-323-24288-2.00167-7 · Cited by 12
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Kim, S.-C., & Cho, S.-H. (2020) · Applied Sciences · doi:10.3390/app10124068 · Cited by 12
Athaya, T., & Choi, S. (2021) · Journal of Sensors · doi:10.1155/2021/9925033 · Cited by 12
Park, S. H., Choi, S.-J., & Park, K. (2022) · Microsystem Technologies · doi:10.1007/s00542-022-05249-0 · Cited by 12
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Lee, J. J., Heo, J. H., Han, J. H., Kim, B. R., Gwon, H. Y., & Yoon, Y. R. (2020) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00507-w · Cited by 11
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McLean, M. K., Weaver, R. G., Lane‐Cordova, A. D., Smith, M. T., Parker, H., Stone, B., McAninch, J., Matolak, D. W., Burkart, S. A., Chandrashekhar, M. V. S., et al. (2023) · Sensors · doi:10.3390/s23073429 · Cited by 11
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Yang, S., Zhang, Y., Morgan, S. P., Cho, D. S.-Y., Correia, R. G., & Wen, L. (2018) · doi:10.1117/12.2502315 · Cited by 11
Lapitan, D. G., Tarasov, A. P., & Рогаткин, Д. А. (2022) · Journal of Biomedical Photonics & Engineering · doi:10.18287/jbpe22.08.030306 · Cited by 11
Bancelin, D., Bachrátá, B., Bollmann, S., Cardoso, P. L., Szomolányi, P., Trattnig, S., & Robinson, S. (2022) · Human Brain Mapping · doi:10.1002/hbm.26152 · Cited by 11
van Velzen, M. H. N., Loeve, A. J., Kortekaas, M. C., Niehof, S. P., Mik, E. G., & Stolker, R. J. (2015) · Physiological Measurement · doi:10.1088/0967-3334/37/1/52 · Cited by 11
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Kulin, D., Antali, F., Kulin, S., Wafa, D., Lucz, K. I., Veres, D. S., & Miklós, Z. (2020) · Applied Sciences · doi:10.3390/app10227977 · Cited by 12
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Metadata
- Authors
- Elgendi, M.
- Affiliations
- Charles Darwin University (AU)
- Corresponding author
- Elgendi, Mohamed
- ORCID
- Elgendi, Mohamed
- Venue
- Current Cardiology Reviews
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2012-06-01
- Volume
- 8
- Issue
- 1
- Pages
- 14-25
- ISSN
- 1573-403X (ISSN-L)
- PMC
- PMC3394104
- OpenAlex
- W1981918735
- Cited by
- 1,206
- References
- 88
- Citations by year
- 2026: 120 · 2025: 106 · 2024: 116 · 2023: 109 · 2022: 130 · 2021: 114 · 2020: 118 · 2019: 118 · 2018: 86 · 2017: 65 · 2016: 46 · 2015: 42 · 2014: 24 · 2013: 11 · 2012: 1
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Current Cardiology Reviews (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- Univerzita Karlova v Praze
- Australian Government
- Charles Darwin University
- 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 › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, digital volume pulse, aging, autonomic function, blood pressure, heart rate, pulse wave analysis, cardiovascular disease, vascular disease, atherosclerosis screening
- MeSH terms
- Fingers, Hemodynamics, Humans, Skin, Artifacts, Photoplethysmography, Pulse Wave Analysis
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